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  "frozen_on": "2026-10-05",
  "run_id": "AAS-2026-10-04-dynamic-cheatsheet-01",
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  "source_revision": "5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9",
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      "text": "---\nresult-disposition: complete\ntarget-class: \"memory/knowledge/context-engineering system\"\nboundary-kind: whole-system\nreviewed-boundary: \"5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\"\nanalysis-cutoff: \"2026-10-04\"\nevidence-tier: code-grounded\nsource:\n  kind: git\n  identity: https://github.com/suzgunmirac/dynamic-cheatsheet\n  revision: \"5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\"\n  path: /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/related-systems/suzgunmirac--dynamic-cheatsheet\n  sha256: null\n---\n\n## Boundary and evidence\n\nDynamic Cheatsheet is an inference-time memory and context-engineering system. This run covers the shipped prompt-driven generation and curation approaches, retrieval and cumulative-state flow, the benchmark driver that persists and reloads state, and the supplied evaluation and result artifacts. The memory is represented as text passed between calls; this repository does not provide a separate durable memory service. The boundary is the complete shipped system in this repository, rather than only the `LanguageModel` library API.\n\n`LanguageModel.advanced_generate` assembles a generator prompt from the current cheatsheet, calls the model, calls a curator with the answer and prior cheatsheet, and returns the extracted updated cheatsheet. Other supported branches implement retrieval, retrieval-synthesis, hybrid retrieval plus cumulative memory, and full-history appending. `run_benchmark.py` loads prompt files and optional initial memory, supplies the current state to each call, takes the returned final cheatsheet forward, and can reload the last saved cheatsheet when resuming. Thus the driver, prompts, retrieval data, persistence path and task evaluators are material parts of the shipped loop.\n\n> After each answer, a curator call updates the cheatsheet.\n> --- `dynamic_cheatsheet/language_model.py`\n\n> last_cheatsheet = outputs[-1].get(\"final_cheatsheet\")\n> --- `run_benchmark.py`\n\n> cheatsheet = output_dict[\"final_cheatsheet\"]\n> --- `run_benchmark.py`\n\n> - Carefully analyze both the question and cheatsheet before starting\n> --- `prompts/generator_prompt.txt`\n\nExternal dependencies include model-provider APIs and their inference/code-execution services; credentials and those services are outside this repository. The benchmark driver also obtains benchmark datasets through the `datasets` package where applicable. No hosted run or service behavior is included as inspected execution evidence. The tracked datasets, embeddings and result files are repository artifacts; their provenance and experimental conditions are not independently established by this boundary inspection.\n\nCoverage of top-level tracked paths at the frozen commit:\n\n| Path | Coverage | Role and boundary decision |\n|---|---|---|\n| `.gitignore` | Included | Repository setup conventions; no operating responsibility identified. |\n| `README.md` | Included | Declares purpose, usage, approaches, model dependencies and benchmark interface. Design claims are not treated as observed operation. |\n| `LICENSE` | Included | Distribution context; no runtime responsibility. |\n| `dynamic_cheatsheet/` | Included | Core model wrapper, generation/curation/retrieval flow, answer and cheatsheet extraction, local execution helper and task evaluation functions. |\n| `prompts/` | Included | Generator and curator instructions define consumption and maintenance of retained text memory. |\n| `run_benchmark.py` | Included | Shipped driver wires prompts, data, model calls, cumulative state, checkpoint/resume, output persistence and evaluation. |\n| `text_generation/` | Included | Shipped provider client used by the core wrapper to issue model calls; provider services remain external. |\n| `data/` | Included | Tracked benchmark datasets and dataset metadata supplied to the driver. |\n| `embeddings/` | Included | Tracked vectors used by retrieval approaches. |\n| `results/` | Included | Tracked result artifacts consumed by result analysis; artifacts alone do not establish reproducible execution or causal effects. |\n| `EvaluatingResults.ipynb` | Included | Shipped downstream analysis of saved results and use of task evaluators. |\n| `ExampleUsage.ipynb` | Included | Shipped interactive caller demonstrating prompt loading, generation and reuse of returned memory. |\n| `figures/` | Included | Published illustration and performance visuals; no runtime responsibility and claims require separate source evidence. |\n\nNo material shipped state-production, maintenance, persistence/reload, later-consumption or evaluation path was found outside the inspected repository. This whole-system boundary supports source-grounded claims about the shipped design and implementation. It does not by itself establish provider behavior, benchmark reproducibility, or that the reported results were caused by a particular component.\n\n## Source register\n\n| source ID | kind | identity/location | revision or capture | evidence layer | inspected scope | citation anchors | access gaps and conclusion prevented |\n|---|---|---|---|---|---|---|---|\n| SRC-1 | git | `https://github.com/suzgunmirac/dynamic-cheatsheet` | `5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9` | implementation: `dynamic_cheatsheet/`, `run_benchmark.py`, `text_generation/`; doctrine/design: `README.md`, `prompts/`; tracked artifacts: `data/`, `embeddings/`, `results/`, `EvaluatingResults.ipynb`, `ExampleUsage.ipynb`, `figures/`; repository metadata: `.gitignore`, `LICENSE` | Inspected tracked repository tree and frozen revision; core orchestration, benchmark state initialization/resume/carry-forward, provider-client boundary, evaluator functions, generator and cumulative curator prompts, README usage and approach descriptions, notebook callers/result-analysis imports. These are initial coverage paths, not a path allowlist. | `dynamic_cheatsheet/language_model.py`; `run_benchmark.py`; `prompts/generator_prompt.txt`; `README.md`; `EvaluatingResults.ipynb`; `ExampleUsage.ipynb` | Provider APIs, credentials, and any live execution are outside the repository inspection. Dataset/result artifact provenance and experimental protocol are not established here, preventing independent reproducibility or causal attribution claims. No source checkout access gap identified. |\n"
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    "records/runtime.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md",
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      "text": "---\ntype: agentic-system-analyses/types/agentic-system-runtime-report.md\ndescription: \"Runtime baseline of Dynamic Cheatsheet across its shipped inference, memory and benchmark routes\"\nrun-id: AAS-2026-10-04-dynamic-cheatsheet-01\nreviewed-boundary: \"5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\"\n---\n\n# Dynamic Cheatsheet runtime report\n\n## Runtime account\n\n### Evidence basis and runtime shape\n\nThis is an implementation-grounded account of the repository at the registered commit. No live provider run or independent experiment was supplied. The tracked notebook contains saved outputs, but its cells do not establish a run performed under the frozen analysis conditions. Operation, whether retrieved or curated text changes a later answer, and the claimed performance benefits therefore remain unobserved here. The Source register in `boundary.md` supplies the evidence identity; this member inspected the listed orchestration, provider, prompt, execution, extraction and evaluation paths.\n\nThe benchmark's ordinary cumulative path is selected by `--approach_name DynamicCheatsheet_Cumulative`. The principal is the benchmark caller (or a notebook/user of `LanguageModel.advanced_generate`). It provides a task input, prompt templates, current cheatsheet and configured model. `run_benchmark.py` builds the current question from the dataset input, omitting the target from the prompt, and calls `advanced_generate`. `LanguageModel` substitutes the input and cheatsheet into the generator template, then passes a one-message history to `generate`. The provider client sends this to the selected external model API. The output is parsed for a final answer; the same configured model is called again with the answer, question and prior cheatsheet in the curator prompt. `extract_cheatsheet` accepts text between `<cheatsheet>` tags and otherwise retains the previous text. The returned answer, raw output, updated text, question and benchmark target are assembled into a result object. The driver scores the final answer against the task target after curation; this score does not gate the curation decision.\n\n> generator_prompt = generator_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[CHEATSHEET]]\", generator_cheatsheet_content)\n> --- `dynamic_cheatsheet/language_model.py`\n\nThe generator prompt directs the model to inspect and use the supplied cheatsheet. The cumulative curator prompt asks it to assess the current solution before retaining or revising text, but the software does not verify those judgments or require a separate approval. The curator produces a full replacement; the prompt instructs it to preserve useful prior entries. The parsing fallback is the previous cheatsheet, not a rejected candidate selected by a reviewer.\n\n> Before updating the cheatsheet, however, you should first assess the correctness of the provided solution and strategically incorporate code blocks, insights, and solutions into the new cheatsheet.\n> --- `prompts/curator_prompt_for_dc_cumulative.txt`\n\n> if \"<cheatsheet>\" in response:\n>     try:\n>         txt = response.split(\"<cheatsheet>\")[1].strip()\n>         txt = txt.split(\"</cheatsheet>\")[0].strip()\n>         return txt\n>     except:\n>         return old_cheatsheet\n> else:\n>     return old_cheatsheet\n> --- `dynamic_cheatsheet/utils/extractor.py`\n\nThe benchmark owns per-example progression. It starts with `(empty)` unless an initial cheatsheet file is supplied. At each question the returned `final_cheatsheet` becomes the next call's input, while the final output is appended to the preceding-output list. The benchmark writes its accumulated JSONL after each processed example and once again at completion. For resume, it reloads all previous output rows, takes the last row's `final_cheatsheet` when present, restores the prior final outputs, and skips the corresponding number of dataset rows. The JSONL checkpoint is the only shipped durable memory path found; the library wrapper itself returns state to its caller and does not implement a separate memory service. In the direct notebook/API route, later read-back depends on the caller passing the returned text into the next call.\n\n> last_cheatsheet = outputs[-1].get(\"final_cheatsheet\")\n> --- `run_benchmark.py`\n\n> # Save the outputs to a file after each example (for crash recovery)\n>         write_jsonl(args.save_path_name, outputs)\n> --- `run_benchmark.py`\n\nResumption verifies only a selected group of saved arguments (generator prompt path, temperature, code-execution flag, task, model name, approach name and number of rounds). Other settings, including the curator prompt path and retrieval top-k, are not compared in that loop. The default dataset shuffle is seeded with 10. These checks and the saved row count are implementation evidence; no resumed execution was supplied to show recovery under interruption or changed files.\n\n### Alternate approach routes\n\nThe approach name determines the material supplied to each generator call:\n\n- `default` sends one generator request with an empty cheatsheet and returns no retained text.\n- `DynamicCheatsheet_Cumulative` sends the prior text, then asks a curator to replace it after each answer.\n- `DynamicCheatsheet_RetrievalSynthesis` takes the current precomputed vector, compares it with vectors for prior inputs using cosine similarity, selects the top-k prior input/output pairs, and asks the curator to synthesize a per-question cheatsheet before the generator call. The runner records the returned `final_cheatsheet` as state, so it can become the `PREVIOUS_CHEATSHEET` supplied to the next synthesis prompt or be reloaded on resume.\n- `Dynamic_Retrieval` selects the same kind of top-k prior pairs and gives them directly to the generator. It does not call the curator in that branch. The runner still serializes the returned text field and carries it forward, but this branch does not consume the carried `cheatsheet` when constructing its retrieved examples.\n- `DynamicCheatsheet_CumulativeRetrieval` combines the current cumulative text with top-k examples, calls the generator, then calls the curator to replace the cumulative text.\n- `FullHistoryAppending` includes all prior input/output pairs in the generator prompt without curation.\n\nFor retrieval approaches, vectors are loaded from the repository's `embeddings/<task>.csv`; rows are reordered to match the task dataset. No embedding service or embedding model is invoked by the inspected runtime path, and the vectors' model provenance is not established by source inspection. The similarity scores choose which examples to include; neither correctness labels nor an expected answer are part of this selection. Retrieval is therefore an implemented context-selection rule, not evidence that its selected examples improve an answer.\n\n> similarities = cosine_similarity([current_original_input_embedding], prev_original_input_embeddings)\n> --- `dynamic_cheatsheet/language_model.py`\n\nThe full-history path grows the prompt with every previous pair, whereas retrieval limits its examples to the configured top-k. Both use the current task ordering and accumulated outputs maintained by the benchmark. In a retrieval resume, the driver restores prior outputs and starts at the saved row count; the exact vector alignment and behavior after changed unvalidated parameters have not been observed.\n\n### Executor, controls and effect boundary\n\nThe ordinary answer and curator calls use the same configured `LanguageModel` instance and its same provider/model choice; role differences come from templates and inputs, not distinct configured model objects. The wrapper supports OpenAI, Anthropic/Claude, Gemini, xAI, and OpenAI-compatible endpoints including Together AI, DeepSeek and Ollama. Provider APIs, credentials, endpoint behavior, hosted policy enforcement and actual resolved model versions are outside this source boundary. The client retries a failed provider function three times with a ten-second delay by default; it does not establish successful delivery or response quality.\n\n> client_kwargs = {\"provider\": provider, \"model\": model_id, \"max_retries\": 3, \"retry_delay\": 10}\n> --- `dynamic_cheatsheet/language_model.py`\n\nA second generator execution path is available when the caller sets `use_code_interpreter`. That option disables local execution in `advanced_generate`; OpenAI creates a server-side container and uses Responses API code-interpreter tooling, while Claude enables its native code-execution tool. Other providers do not implement `create_container` in the wrapper. The generator gets the tool on each applicable model call; curator calls explicitly disable code execution. Provider sandbox isolation and persistence are not inspectable here. The returned textual answer remains the interface to the benchmark.\n\nBy default, the generator can also trigger a local subprocess: its output must contain the configured `EXECUTE CODE!` string and the text before that flag must end in a code fence. The extractor selects the first Python block, may wrap the last line in `print`, writes it to a temporary file, and starts `python3`. The subprocess has a three-second timeout but no source-level sandbox, approval gate, resource policy or rollback. A generated program can use whatever filesystem, network and process permissions the deployed Python process has; the actual host privilege boundary was not supplied. The prompt encourages code generation and execution, and asks for result validation. That is guidance to the model, not a technical restriction on executable effects.\n\n> # TODO (msuzgun): Improve the security of this function by using a sandboxed environment\n> --- `dynamic_cheatsheet/utils/execute_code.py`\n\nThe code-trigger route admits changes to the local environment to the extent that process permissions allow. Its trigger is model-authored output, not a human approval event. A response without the exact trigger/fence pattern is not executed by this branch; a malformed or failed program can return an error string, and timeout kills the direct process. No undo mechanism is implemented. In code-interpreter mode, code is instead admitted by the caller's option and executed by provider infrastructure. The generator prompt's execution instructions shape proposed code in both modes, but the provider's actual retention, isolation, and rollback contracts remain unknown.\n\nThe benchmark evaluates the final answer with task-specific code after generation and curation. Game of 24 is checked by parsing/evaluating expressions; AIME is exact text matching after punctuation removal; multiple-choice tasks compare parsed option text with the target; equation balancing uses its own evaluator. These local evaluators supply an answer oracle in the benchmark mode, but their result updates only printed counters and summary: it is not fed back to the curator or used to admit/rollback the just-written cheatsheet. When a direct caller uses the library without the benchmark, no such oracle is wired in this loop.\n\n> result = eval_for_GameOf24(original_input, final_answer)\n> --- `run_benchmark.py`\n\nNo human review, approval, veto or manual graph control is wired into the benchmark route. A user may choose the approach, model, prompts, initial cheatsheet, code-execution settings and input files before a call. The selected provider's capabilities and policies may differ, but are external to the inspected code. The saved outputs offer a recovery checkpoint, not a guarantee that the current provider, prompt files, dataset or execution environment match the earlier run.\n\n## Shared records\n\n### Components\n\n#### RT-CMP-model — Configured inference model\n\n- Identity: the model selected by `model_name`, used for both generator and curator roles through one `LanguageModel` and `UnifiedLLMClient`.\n- Representational form: distributed-parametric.\n- Storage substrate: provider-managed model weights, or a model served at the configured OpenAI-compatible/local endpoint; model storage is outside the inspected repository.\n- Parameters change during operation: no, not in the inspected inference code. The repository describes inference-time memory as separate from parameter modification; this is a source-level design claim, not provider telemetry.\n- Exact version pinned: not guaranteed by the interface. `model_name` is supplied as a string and examples include both dated identifiers and provider aliases.\n- Mutable endpoint can resolve to another model: yes, possible for aliases or mutable external endpoints; endpoint resolution is not inspected.\n- Evidence: `dynamic_cheatsheet/language_model.py`; `README.md`.\n\n> Rather than repeatedly re-discovering solutions or making the same mistakes, DC enables models to accumulate and leverage strategies, code snippets, and problem-solving techniques without modifying the underlying model parameters.\n> --- `README.md`\n\n### Operative objects\n\n#### RT-OBJ-cheatsheet — Curated text\n\n- Identity: the `cheatsheet` / `final_cheatsheet` value passed between calls.\n- Representational form: natural-language text. Its effect depends on a model interpreting the supplied prompt.\n- Storage substrate: caller memory for direct API use; in benchmark runs it is a string held during the loop and copied into the JSONL result row for disk persistence.\n- Evidence: `dynamic_cheatsheet/language_model.py`; `dynamic_cheatsheet/utils/extractor.py`; `run_benchmark.py`; `prompts/curator_prompt_for_dc_cumulative.txt`.\n\n#### RT-OBJ-retrieval-context — Prior examples and vectors\n\n- Identity: task input corpus, generator outputs and per-task precomputed `embedding` rows used by retrieval approaches.\n- Representational form: mixed natural-language inputs/outputs and dense numerical vectors; similarity scores are scalar numeric values.\n- Storage substrate: tracked CSV files for vectors, dataset objects in the benchmark process for examples and outputs.\n- Provenance limit: the vectors' generating model and conditions are not declared in the inspected route; no runtime embedding inference is shown.\n- Evidence: `run_benchmark.py`; `dynamic_cheatsheet/language_model.py`.\n\n#### RT-OBJ-run-checkpoint — Benchmark output file\n\n- Identity: JSONL rows holding inputs, targets, raw inputs, generated outputs and returned cheatsheet, plus a companion JSON parameters file.\n- Representational form: mixed symbolic JSON structure and natural-language text values.\n- Storage substrate: filesystem path under the chosen save directory.\n- Evidence: `run_benchmark.py`.\n\n### Routes\n\n#### RT-RTE-cumulative-loop — Cumulative generation and curation\n\n- Endpoints and progression: benchmark input → generator prompt/model → answer extraction → curator prompt/model → cheatsheet extraction → return object → next benchmark input; benchmark evaluator scores the answer after the curator call.\n- Owner: benchmark driver owns sequence and carry-forward; configured model proposes an answer and cheatsheet; local extraction code applies tag parsing.\n- Context, state and effects: current question and current text reach the generator; question, raw generator output and old text reach the curator. Extracted text is the next state; output row and target are persisted by benchmark mode.\n- Runtime-client controls: configured provider/model, temperature, token limits, extra API parameters, retries, approach selection, code execution options.\n- Persistence, coordination and return: library returns state to its caller; benchmark writes JSONL after each row and resume can reload its last returned cheatsheet.\n- Recovery and terminal output: parsing without `<cheatsheet>` retains old text; benchmark resumes from output count and last `final_cheatsheet`; answer and output are returned and scored, with no rollback on a wrong score.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: extracted answer, raw generator output, and newly extracted cheatsheet in the result dictionary.\n- Later read-back: benchmark passes returned text into the next question and can reload it on resume; direct callers must pass it themselves.\n- Delegated visibility: generator sees current text; curator sees previous text and current generator output; benchmark evaluator sees extracted answer and task target, but does not feed its score to the curator.\n- Selection predicate: CLI/API caller selects `DynamicCheatsheet_Cumulative`; the curator prompt and model select retained prose without a deterministic acceptance test.\n- Invalidation or expiry: no age, task boundary, size or expiry policy is implemented in this branch; a curator replacement can omit prior content.\n- Activation or effect: prompt substitution wires the text into the next generator invocation; behavioral change from that text is unobserved.\n- Evidence limits: implementation only; no trace establishes provider interpretation, correctness of curator assessments or benefit.\n- Decision roles: model proposes answer and replacement; the curator model performs the only in-loop content selection; program accepts tagged output or retains old text on extraction failure; no human approval or veto is wired.\n- Answer oracle: benchmark evaluator compares answer with task-specific target after curation; its score is not used for content admission. Direct API calls have no in-loop target oracle.\n- Operating mode and improvement trigger: sequential task loop; each processed question triggers generation and curation, even when the final answer fails local scoring.\n- Admission mechanism: tagged curator output replaces the text. The cumulative curator prompt says to judge solution correctness and preserve useful content. Missing tags retain the old text. No separate rejection mechanism or rollback exists; benchmark checkpoints permit resuming an earlier file only by caller choice, which is not a transaction rollback feature.\n\n> # Extract the new cheatsheet from the output (if present); otherwise, return the old cheatsheet\n> new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=current_cheatsheet)\n> cheatsheet = new_cheatsheet\n> --- `dynamic_cheatsheet/language_model.py`\n\n#### RT-RTE-retrieval-synthesis — Retrieve and synthesize\n\n- Endpoints and progression: current vector and prior vectors → cosine ranking/top-k input-output pairs → curator synthesis prompt → generator prompt/model → extracted answer and text return.\n- Owner: benchmark loads and aligns vectors; local code ranks examples; configured model synthesizes context and generates answer.\n- Context, state and effects: each request sees top-k prior pairs; the curator also receives the prior returned text and next input; generator receives synthesized text. Benchmark carries the returned text into the next iteration and persists it in JSONL.\n- Runtime-client controls: caller chooses approach, `retrieve_top_k`, model/provider, prompts, temperature, code options and token limits.\n- Persistence, coordination and return: prior model outputs are in-memory during a run and in checkpoint rows; vectors are read from tracked CSV; returned synthesized text can be reloaded on resume.\n- Recovery and terminal output: extraction failure falls back to the retrieved-context string; benchmark checkpoint/resume applies as above; no answer-based rollback.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: extracted answer, generator output, selected prior pairs and the synthesized context as `final_cheatsheet`.\n- Later read-back: next benchmark iteration supplies returned text as the prior cheatsheet to the next curator; resume reloads it; the direct-retrieval branch differs and does not consume it.\n- Delegated visibility: curator sees retrieved prior pairs, next input and prior cheatsheet; generator sees synthesized text and current input; no evaluator score reaches the curator.\n- Selection predicate: local cosine similarity over supplied vectors, descending top-k; no expected answer or correctness filter.\n- Invalidation or expiry: retrieved pairs are request-local; stored vectors have no runtime refresh or expiry path; returned synthesis may be replaced on the next request.\n- Activation or effect: synthesized text is inserted into the current generator prompt; effect on answer quality is unobserved.\n- Evidence limits: vector provenance and provider/model outputs are not established; no retrieval or synthesis trace was supplied.\n- Decision roles: local cosine ranking proposes selected pairs; curator model synthesizes and selects text; program accepts tagged output or retains its fallback; no human approval or veto.\n- Answer oracle: none for selecting pairs or admitting synthesized text. The benchmark's target-based evaluator scores the final answer after these decisions only.\n- Operating mode and improvement trigger: per-question bounded top-k synthesis; every question with this approach triggers ranking, synthesis and generation.\n- Admission mechanism: curator-tagged output becomes the returned context, guided by retrieval-synthesis prompt instructions to preserve/use useful prior content; missing tags retain the pre-synthesis retrieved string. No explicit correctness gate, human rejection or rollback.\n\n> # Get the current original input embedding\n> current_original_input_embedding = original_input_embeddings[-1]\n> prev_original_input_embeddings = original_input_embeddings[:-1]\n> \n> # Retrieve the most similar k input-output pairs from the previous inputs and outputs\n> if len(prev_original_input_embeddings) > 0:\n>     similarities = cosine_similarity([current_original_input_embedding], prev_original_input_embeddings)\n>     top_k_indices = np.argsort(similarities[0])[::-1][:retrieve_top_k]\n> --- `dynamic_cheatsheet/language_model.py`\n\n#### RT-RTE-cumulative-retrieval — Cumulative text with retrieval\n\n- Endpoints and progression: current cumulative text plus top-k prior pairs → generator → answer extraction → curator using question, raw answer and prior cumulative text → text extraction and return.\n- Owner: local code ranks; model answers and proposes replacement text; benchmark coordinates and carries state.\n- Context, state and effects: generator sees general text and retrieved examples; curator sees the current problem, generator response and cumulative text. Returned text and outputs are saved by benchmark mode.\n- Runtime-client controls: caller sets approach, top-k, provider/model, prompts, sampling/token settings and execution mode.\n- Persistence, coordination and return: in-memory output list and current text; checkpoint JSONL and resume provide the benchmark persistence path.\n- Recovery and terminal output: missing tags retain prior cumulative text; checkpoint resume restores last text; no correctness-based rollback.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: answer, raw output, retrieved pairs and updated cumulative text.\n- Later read-back: benchmark supplies updated text and prior outputs to the next call; resume reads the last saved text.\n- Delegated visibility: generator receives cumulative text and selected examples; curator sees prior cumulative text and current raw answer; local evaluator does not inform the curator.\n- Selection predicate: cosine top-k selects examples; curator prompt/model selects text to carry forward.\n- Invalidation or expiry: no expiry or corpus pruning is implemented; curator can rewrite or omit text; selected pairs are current-call context only.\n- Activation or effect: both cumulative text and examples are in the generator prompt; resulting behavioral effect is unobserved.\n- Evidence limits: source shows wiring, not correct similarity provenance, provider behavior or benefit.\n- Decision roles: program ranks by cosine; curator model proposes replacement; extraction accepts tagged output or falls back; no human review/veto.\n- Answer oracle: benchmark checks the final answer against target after curation but does not condition curation or state carry-forward on the score.\n- Operating mode and improvement trigger: one bounded retrieval-plus-cumulative-memory iteration per input; a question triggers both example selection and a curator update.\n- Admission mechanism: curator-tagged replacement is carried forward; prompt guidance requests assessment and preservation of useful prior text; missing tags retain old text; no rollback beyond choosing an earlier saved checkpoint.\n\n> combined_cheatsheet = f\"{cheatsheet}\\n{retrieved_section}\"\n> --- `dynamic_cheatsheet/language_model.py`\n\n#### RT-RTE-direct-retrieval — Top-k example context\n\n- Endpoints and progression: aligned precomputed vectors → local cosine ranking → retrieved prior input/output pairs → generator prompt/model → answer extraction and return.\n- Owner: driver supplies aligned vectors and outputs; local code ranks; model interprets examples and answers.\n- Context, state and effects: current input plus selected pairs; no curator call in this branch. The driver stores outputs and the returned text field, but this branch does not use the carried cheatsheet when constructing retrieval context.\n- Runtime-client controls: approach, top-k, provider/model, generator prompt, temperature/token limits and code mode.\n- Persistence, coordination and return: vectors are tracked CSV; outputs are in memory and benchmark checkpoint rows; direct library caller owns subsequent use.\n- Recovery and terminal output: benchmark can reload rows on resume; no separate correction or rollback of retrieved examples.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: answer, raw output, and selected prior pairs; returned `final_cheatsheet` is the selected example context.\n- Later read-back: prior output pairs participate in later top-k selection in a benchmark run; the returned text field is not consumed by this branch's next generation step.\n- Delegated visibility: only the generator sees selected examples; evaluator later sees answer and target in benchmark mode.\n- Selection predicate: descending cosine similarity over supplied fixed vectors, clipped to `retrieve_top_k`; no correctness or answer oracle.\n- Invalidation or expiry: no vector refresh or expiry; top-k membership changes with the current vector and prior corpus.\n- Activation or effect: examples are prompt content; behavioral effect is unobserved.\n- Evidence limits: embedding provenance and retrieval quality are unknown; source code does not establish that similarity implies task relevance.\n\n#### RT-RTE-full-history — Append prior solutions\n\n- Endpoints and progression: prior input/output list → local string construction of all prior pairs → generator prompt/model → answer extraction and return.\n- Owner: benchmark maintains pair history; local code appends; model interprets and answers.\n- Context, state and effects: full preceding pair history is included; no curation model is called. Benchmark retains outputs for subsequent calls and checkpointing.\n- Runtime-client controls: selected approach, provider/model, prompt and generation parameters, code options.\n- Persistence, coordination and return: prior outputs are in process memory and saved in JSONL by benchmark mode.\n- Recovery and terminal output: resume reloads outputs; no mechanism removes stale or incorrect pairs except caller changing/restarting input state.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: generated answer, raw output and the assembled history text in the result.\n- Later read-back: all prior outputs can appear in later prompts; saved rows are reloaded on resume.\n- Delegated visibility: generator sees the whole preceding history; evaluator sees answer/target after generation in benchmark mode.\n- Selection predicate: all outputs before the current input are included in sequence; no relevance or correctness filtering.\n- Invalidation or expiry: none within a run; the prompt grows with history until external context limits or provider behavior intervene.\n- Activation or effect: prior pairs are included in the prompt; effect is unobserved.\n- Evidence limits: no run trace or context-window failure evidence was supplied.\n\n> curated_cheatsheet += f\"#### Previous Input #{i+1}:\\n\\n{previous_input_txt}\\n\\n#### Model Solution to Previous Input #{i+1}:\\n\\n{previous_output_txt}\\n---\\n---\\n\\n\"\n> --- `dynamic_cheatsheet/language_model.py`\n\n#### RT-RTE-default — Single-call baseline\n\n- Endpoints and progression: current input and `(empty)` cheatsheet placeholder → generator prompt/model → answer extraction and return.\n- Owner: caller selects approach and model; local code builds prompt and extracts answer; model responds.\n- Context, state and effects: one question; no carried cheatsheet is consumed or returned.\n- Runtime-client controls: configured model/provider, generation options and code execution settings.\n- Persistence, coordination and return: no memory persistence in this branch; caller receives answer/output.\n- Recovery and terminal output: no checkpoint/recovery in library; benchmark wrapper still saves the row if this mode is used.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: extracted answer and raw generator output; `final_cheatsheet` is `None`.\n- Later read-back: inapplicable — this branch returns no accumulated memory for a later consumer.\n- Delegated visibility: generator sees current input and empty placeholder; evaluator sees final answer and target only in benchmark mode.\n- Selection predicate: explicit caller choice of `default`; no context-item selection.\n- Invalidation or expiry: inapplicable — this branch creates no retained item.\n- Activation or effect: inapplicable — no prior material is delivered for activation.\n- Evidence limits: source-level single-call wiring only; provider output and operation are unobserved.\n\n#### RT-RTE-checkpoint-resume — Save and resume benchmark state\n\n- Endpoints and progression: each completed benchmark row and current returned text → JSONL overwrite → later invocation loads JSONL and parameter file → restores outputs/text and skips saved row count.\n- Owner: benchmark process owns file writes and reload; caller chooses save and continue paths.\n- Context, state and effects: JSONL includes answer, target and returned memory text; companion JSON stores invocation arguments.\n- Runtime-client controls: save path, continue path, task/model/approach and other CLI arguments; resume compares only a subset before proceeding.\n- Persistence, coordination and return: filesystem JSONL persists rows; last row supplies text; restored outputs supply future retrieval and full-history context.\n- Recovery and terminal output: reload/restart is implemented; no atomic write, version migration, integrity check or rollback transaction is evidenced in this path.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: none from the checkpoint mechanism itself; the driver prints final summary when it processes new rows.\n- Later read-back: saved outputs and last text are read by a continuation run.\n- Delegated visibility: driver sees all loaded rows; subsequent approach branches see the prior text and/or prior outputs according to their mode.\n- Selection predicate: caller selects an existing result path; skip position is derived from number of rows, not a separately validated cursor.\n- Invalidation or expiry: no expiry; a different prompt or runtime setting can be used if not among the compared parameters.\n- Activation or effect: restored state enters later prompts only where the selected approach consumes it; actual effect is unobserved.\n- Evidence limits: no interruption/restart execution trace was supplied; file corruption, concurrent writers and provider/model drift are not tested.\n\n#### RT-RTE-local-python — Model-triggered local execution\n\n- Endpoints and progression: generator response with fenced Python and execution flag → extraction/print adjustment → temporary `.py` file → local `python3` subprocess → captured stdout/stderr appended to messages → another model call until depth condition ends.\n- Owner: model authors code and emits trigger; wrapper detects trigger; local process executes; no human or policy service approves the code.\n- Context, state and effects: prompt/question and model output; process inherits deployed host permissions; output is returned to model on continuation and in final output.\n- Runtime-client controls: `allow_code_execution`, trigger string, recursion-depth limit and 3-second subprocess communication timeout.\n- Persistence, coordination and return: temporary file is removed after direct process returns or times out; arbitrary side effects made by code are not reverted; output joins the returned answer transcript.\n- Recovery and terminal output: errors are rendered as text; timeout kills the process; no rollback or recovery for side effects.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: captured output/error string is appended to the conversation and final output.\n- Later read-back: only the same generator conversation receives execution output; retained benchmark history may store the combined final output, but no structured execution record is written.\n- Delegated visibility: generator receives local execution output in a subsequent turn; benchmark evaluator sees the final extracted answer, not a control decision about execution.\n- Selection predicate: response must contain the configured flag and preceding text must end with a code fence; first Python block is extracted.\n- Invalidation or expiry: temporary script file is removed; side effects outside it have no expiry or rollback.\n- Activation or effect: source code executes in the local Python process; the deployed OS permission and isolation envelope is unknown.\n- Evidence limits: subprocess implementation inspected; no execution trace or host configuration was supplied, and timeout is not evidence of sandboxing.\n- Trigger and proposal: the model proposes Python and requests execution with the flag; the generator prompt explicitly instructs this protocol when coding is required.\n- Admission and rejection: the wrapper executes on syntactic trigger without human review; absence of trigger or required fence prevents this branch from executing.\n- Rollback and recovery: no rollback is implemented; timeout kills the direct process and removes the temporary script, but cannot restore other side effects.\n- Guidance and persistence: generator instructions request self-contained Python, imports, comments and validation; execution output persists only in the conversation/result text and benchmark row, while process side effects may outlive the call.\n\n> process = Popen([\"python3\", temp_file.name], stdout=PIPE, stderr=PIPE)\n> --- `dynamic_cheatsheet/utils/execute_code.py`\n\n#### RT-RTE-provider-code — Provider code interpreter\n\n- Endpoints and progression: caller enables option → wrapper creates OpenAI container or sets Claude native sentinel → generator request includes provider code-execution tool → provider returns text output.\n- Owner: caller enables; wrapper chooses provider path; model proposes code; provider service controls its execution environment.\n- Context, state and effects: generator prompt and current input reach provider; local `allow_code_execution` is disabled for the invocation; provider container state and service isolation are external.\n- Runtime-client controls: `use_code_interpreter`, selected provider/model, token/temperature options; OpenAI container created once per benchmark run, Claude tool is attached per request.\n- Persistence, coordination and return: only textual output is returned to local wrapper; provider-side artifacts/container lifetime and file visibility are not evidenced by repository code.\n- Recovery and terminal output: provider retry wrapper retries failed API calls; no local rollback of remote effects is present.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: provider-returned model text is the generator output; it is not reparsed as a local execution event.\n- Later read-back: returned text can enter benchmark output history; provider-side files are not read back by this repository.\n- Delegated visibility: provider tool/model share whatever service-side context the API provides; local benchmark sees only response text.\n- Selection predicate: explicit caller option and provider support; OpenAI uses a run container, Claude uses its native tool, other providers are unsupported by `create_container`.\n- Invalidation or expiry: local code contains no provider-container cleanup or persistence policy; actual lifetime is unobserved.\n- Activation or effect: code-execution tool is sent to provider; actual tool activation and side effects are unobserved.\n- Evidence limits: source shows tool request construction only; provider sandbox, isolation, retention and execution are outside boundary.\n- Trigger and proposal: caller enables the capability; model can propose code under the same generator guidance, and the provider may execute it through the attached tool.\n- Admission and rejection: wrapper attaches the tool without per-action human approval; provider-side gating is unobserved.\n- Rollback and recovery: local code offers retry but no remote rollback; container recovery/cleanup contract is unavailable.\n- Guidance and persistence: generator prompt requires an explicit execution request for coding; output text may persist in benchmark JSONL, but provider-side files or state are not read back locally.\n\n### Claims\n\nnone declared in this member.\n\n### Evidenced absences\n\nnone declared in this member.\n\n### Behavioral-authority paths\n\nnone declared in this member.\n\n## Annotations\n\nnone\n"
    },
    "records/memory.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md",
      "sha256": "247368f10ad79ad3622ad123d319bd0bed1fa06f9b259c8723b5e3bafcd2ef79",
      "text": "---\ntype: agentic-system-analyses/types/agent-memory-analysis-report.md\ndescription: \"Retained text and example memory in Dynamic Cheatsheet's shipped prompt and benchmark routes\"\nrun-id: AAS-2026-10-04-dynamic-cheatsheet-01\nsource-identity: \"https://github.com/suzgunmirac/dynamic-cheatsheet\"\nreviewed-boundary: \"5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\"\nreport-status: complete\n---\n\n# Dynamic Cheatsheet memory report\n\n## Boundary and evidence\n\nThis report covers the repository's retained, later-consumed material: the cumulative cheatsheet string; task input and generated-output pairs used by history and retrieval approaches; the precomputed vectors that select those pairs; and benchmark JSONL rows that preserve returned state for later continuation. It includes the direct `LanguageModel.advanced_generate` interface and the benchmark caller because each can pass or carry memory into a later model request. The frozen source is SRC-1 at `5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9`. Evidence is implementation and declared prompt/design text only; no hosted execution trace was supplied. The whole-system boundary and access limits are recorded in `boundary.md`.\n\nProvider-side conversation retention, model state, container files, and service behavior are excluded because they are outside this repository. Static prompt templates, datasets, and vector files are inputs to the memory routes, not retained state produced through use. The local temporary Python file is execution machinery, not later-read memory. The benchmark evaluator and result-analysis notebook consume saved answers for scoring; they do not admit, reject, or revise the cheatsheet. These exclusions prevent claims about external retention, actual model use of supplied material, and memory-caused performance.\n\n| Entry point or operation | Source path | Coverage | Limit or exclusion |\n|---|---|---|---|\n| CLI selection, initial state, checkpoint write/resume and answer scoring | `run_benchmark.py` | Supplied `RT-RTE-cumulative-loop`, `RT-RTE-checkpoint-resume`; annotations below | Source wiring only; no resume or provider run observed. |\n| Public generation and memory routes | `dynamic_cheatsheet/language_model.py` | Supplied `RT-RTE-cumulative-loop`, `RT-RTE-retrieval-synthesis`, `RT-RTE-cumulative-retrieval`, `RT-RTE-direct-retrieval`, `RT-RTE-full-history`, `RT-RTE-default`; new `MEM-RTE-cumulative-write` | No separate memory service, hook, or memory-specific tool registry appears in the inspected interface. Provider-side effects are outside the boundary. |\n| Curator instructions and extraction/fallback | `prompts/curator_prompt_for_dc_cumulative.txt`; `prompts/curator_prompt_for_dc_retrieval_synthesis.txt`; `dynamic_cheatsheet/utils/extractor.py` | `MEM-RTE-cumulative-write`; annotation on `RT-OBJ-cheatsheet` | Prompt instructions do not validate the model's judgment or the resulting content. |\n| Raw example history and fixed retrieval index | `run_benchmark.py`; `dynamic_cheatsheet/language_model.py`; `embeddings/` | New `MEM-OBJ-example-history`, `MEM-OBJ-embedding-table`; annotations on `RT-OBJ-retrieval-context` | Embedding provenance and relevance are not established. |\n| Persisted result consumption and after-the-fact evaluation | `run_benchmark.py`; `EvaluatingResults.ipynb`; `dynamic_cheatsheet/utils/evaluation.py` | Annotations on `RT-OBJ-run-checkpoint`, `RT-RTE-checkpoint-resume` | The notebook's checked-in code is a consumer definition, not evidence that its displayed or recomputed scores came from this frozen run. |\n| Interactive caller | `ExampleUsage.ipynb`; `README.md` | Annotation on `RT-OBJ-cheatsheet` | The notebook contains saved outputs, not an inspectable run under this boundary's conditions. |\n\nDirect inspection added these paths to the initial paths listed in the Source register: `dynamic_cheatsheet/utils/extractor.py`, `dynamic_cheatsheet/utils/evaluation.py`, `prompts/curator_prompt_for_dc_retrieval_synthesis.txt`, `text_generation/simple_unified_client.py`, the `embeddings/` CSV files, and the input cells of `EvaluatingResults.ipynb` and `ExampleUsage.ipynb`. They are at the registered revision and use SRC-1. The client exposes provider request construction and code-interpreter operations, not a repository-owned persistent memory API. No source checkout access gap was found.\n\n## Core ideas\n\nThe repository retains material in model-visible text and example pairs, not in a repository-owned memory service. A caller supplies the current cheatsheet to `advanced_generate`; the benchmark carries returned state across examples and writes it to JSONL. The configured inference model is called with the selected prompt text. The repository says this does not change model parameters, but provider telemetry and resolved model versions are outside the evidence boundary (SRC-1, `README.md`, `dynamic_cheatsheet/language_model.py`).\n\nThe cumulative route writes derived prose. For each question, the model generates an answer, then the curator prompt receives that question, the raw generator response, and the prior cheatsheet. The curator's tagged text replaces the prior value; if the expected tags are absent, extraction returns the old value. The prompt asks the model to judge answer correctness and preserve useful material, but neither the benchmark's target-based evaluator nor a separate human reviewer gates the write. The answer target is scored only after generation and curation. The replacement and its input are retained, but no curator rationale or verified judgment is recorded as a separate artifact (SRC-1, `dynamic_cheatsheet/language_model.py`, `dynamic_cheatsheet/utils/extractor.py`, `run_benchmark.py`, `prompts/curator_prompt_for_dc_cumulative.txt`).\n\nThe other memory mechanism retains prior input/output pairs. Full-history appending supplies all prior pairs; direct retrieval supplies a top-k subset selected by cosine similarity over fixed, precomputed input vectors. Retrieval-synthesis transforms selected pairs plus prior cheatsheet text into a new text context before generation. Cumulative retrieval gives the generator both the accumulated text and selected pairs, then updates the text after the answer. In direct retrieval the runner still stores a returned text field, but that branch does not read that field when building its next retrieved context. Vector rows are shipped data and are not updated by the task loop (SRC-1, `dynamic_cheatsheet/language_model.py`, `run_benchmark.py`).\n\nThe benchmark JSONL stores each answer, target, returned cheatsheet, raw output, and the step fields returned by the wrapper; the latter retain current and new cheatsheet strings on curator routes. It rewrites the accumulated rows after each example. A later continuation loads all rows, restores the last cheatsheet and raw outputs, then the selected approach determines which state reaches a prompt. The parameter comparison for continuation omits some settings, and no integrity, atomicity, expiry, or versioning guarantee is implemented. The evaluation notebook can read these rows and recompute scores from saved generator output, but no such score is passed back to a curator or used to withdraw state (SRC-1, `run_benchmark.py`, `EvaluatingResults.ipynb`).\n\n## Shared records\n\n### Components\n\nnone declared in this member.\n\n### Operative objects\n\n#### MEM-OBJ-example-history — Task input and generated-output pairs\n\nPart of: RT-OBJ-retrieval-context\n\n- Closest supplied record and distinct identity: `RT-OBJ-retrieval-context` groups this dynamic pair history with static vector rows. This record is only the ordered task-input and generated-output material; no other supplied object separately identifies that trace corpus.\n- Identity: prior task inputs paired with `final_output` strings supplied to history or retrieval routes.\n- Representational form: natural-language prompt and model-output text. The output may contain reasoning or code as text; this report does not treat it as verified.\n- Storage substrate: task inputs are held in the benchmark corpus; generated outputs are held in `generator_outputs_so_far` and in benchmark JSONL rows.\n- Lineage: each pair combines a task input with the model's returned output. Retrieval does not pair the target with the output; the target is saved separately and used by the evaluator.\n- Consumers and authority: full-history code gives every earlier pair to the generator; retrieval selects pairs for the generator, and retrieval-synthesis also supplies them to the curator. Their text is model-interpreted context, not a program-validated answer or instruction.\n- Persistence and limits: in-process during a run and in result rows on disk. Its survival does not establish correctness, usefulness, or future activation.\n- Evidence: SRC-1, `run_benchmark.py`, `dynamic_cheatsheet/language_model.py`.\n\n> generator_outputs_so_far.append(output_dict[\"final_output\"])\n> --- `run_benchmark.py`\n\n#### MEM-OBJ-embedding-table — Precomputed retrieval vectors\n\nPart of: RT-OBJ-retrieval-context\n\n- Closest supplied record and distinct identity: `RT-OBJ-retrieval-context` also covers task and output pairs. This record is the fixed numeric index used for ranking; it is not the retrieved example content and no other supplied object identifies it separately.\n- Identity: the task-specific rows read from `embeddings/<task>.csv` and aligned to the dataset's input order.\n- Representational form: dense numerical vectors used by a cosine-similarity consumer; these are not the configured model's weights.\n- Storage substrate: tracked per-task CSV files; loaded as numeric arrays for a benchmark run.\n- Lineage: precomputed before the inspected loop. The producing model and conditions are not identified by the inspected source, and the run computes no embeddings.\n- Consumers and authority: local cosine ranking selects which paired examples are included in retrieval prompts. The vectors choose context; they do not score answer correctness or decide whether a cheatsheet write is admitted.\n- Persistence and limits: fixed shipped data, with no refresh or expiry path in the inspected loop. Similarity does not establish relevance or benefit.\n- Evidence: SRC-1, `run_benchmark.py`, `dynamic_cheatsheet/language_model.py`, `embeddings/`.\n\n> similarities = cosine_similarity([current_original_input_embedding], prev_original_input_embeddings)\n> --- `dynamic_cheatsheet/language_model.py`\n\n### Routes\n\n#### MEM-RTE-cumulative-write — Curator replacement of accumulated text\n\nPart of: RT-RTE-cumulative-loop\n\n- Endpoints and progression: current question, generator response and prior cheatsheet → curator request → tagged text extraction or old-text fallback → returned cheatsheet → benchmark carry-forward or caller reuse.\n- Owner: one configured model generates the answer and proposes the replacement; local code checks for `<cheatsheet>` delimiters and chooses extracted text or the prior string; the caller controls subsequent delivery.\n- Context, state and effects: the curator sees the question, ungraded generator response and prior text. The benchmark target and its post-response evaluation result do not enter the curator request.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: extracted cheatsheet text, or the previous string if `<cheatsheet>` tags are absent.\n- Later read-back: the benchmark passes the returned text to the next eligible call and reloads the last row's value on continuation; direct API callers must pass it themselves.\n- Delegated visibility: the curator model sees prior text and current answer; a subsequent generator sees the returned text when its approach supplies cumulative cheatsheet content.\n- Selection predicate: the model writes the candidate according to the curator prompt; code accepts the first tagged segment without checking claims, answer correctness, or usefulness. Missing tags preserve the prior text.\n- Invalidation or expiry: no time, task, or size-based invalidation is implemented; a replacement may omit earlier text. Earlier row versions remain in the JSONL output, but ordinary continuation restores only the last row's value.\n- Activation or effect: the returned text is wired into later prompts on cumulative routes; whether it changes model behavior is unobserved.\n- Evidence limits: implementation and prompt guidance only; no provider output or live operation is supplied, and no result shows that the curator's correctness judgment was accurate.\n- Decision roles: the curator model proposes and performs content selection; extraction code accepts a tagged segment or falls back. There is no separate human approval, veto, or content validator.\n- Answer oracle: the benchmark evaluator compares the extracted answer with the task target after the curator call, but its result does not gate this write. A direct library caller has no benchmark target oracle in this route.\n- Operating mode and trigger: each cumulative benchmark question triggers generation and curation; `max_num_rounds` can repeat this within one call. A wrong benchmark answer does not suppress the next write.\n- Admission, rejection, and recovery: tag presence admits the replacement. Missing tags preserve the prior text; there is no explicit candidate rejection decision or rollback operation. A caller may choose an earlier saved file for continuation, but this is not an in-route undo.\n- Guidance and persistence: the cumulative curator prompt asks for a correctness assessment and preservation/refinement of useful material. The saved rows include prior/new text and generator output, but not a separately retained curator rationale or curator response.\n- Evidence: SRC-1, `dynamic_cheatsheet/language_model.py`, `dynamic_cheatsheet/utils/extractor.py`, `run_benchmark.py`, `prompts/curator_prompt_for_dc_cumulative.txt`.\n\n> # Extract the new cheatsheet from the output (if present); otherwise, return the old cheatsheet\n> new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=current_cheatsheet)\n> cheatsheet = new_cheatsheet\n> --- `dynamic_cheatsheet/language_model.py`\n\n### Claims\n\nnone declared in this member.\n\n### Evidenced absences\n\nnone declared in this member.\n\n### Behavioral-authority paths\n\nnone declared in this member.\n\n## Annotations\n\n#### On RT-OBJ-cheatsheet — Curated text\n\n- Storage substrate: direct callers hold and pass a string; the benchmark holds it during the loop and saves it in each result row.\n- Representational form: natural-language text interpreted through generator or curator prompts.\n- Lineage and raw/derived distinction: cumulative curation derives replacement text from the current question, raw answer and previous text; retrieval-synthesis derives text from selected prior input/output pairs, next input and previous text. Direct retrieval's context is the selected raw pairs, despite storing that context as `final_cheatsheet` in the result.\n- Write agency and source: the model authors candidate text; the extractor applies delimiter fallback. The benchmark target and score do not supervise curation.\n- Memory consumers and authority: cumulative generation supplies the current string to the generator. Retrieval-synthesis supplies it to the curator as previous text, and the returned synthesis to the generator. In cumulative retrieval, the generator reads it alongside examples and the curator sees it for update. Direct retrieval does not consume its carried text field.\n- Read-back trigger and selector: caller chooses the approach; each next input triggers a call. The benchmark selects its latest returned value by position on resume; it does not semantically select among saved versions.\n- Persistence, delivery and horizon: caller-managed reuse or benchmark JSONL across continuation runs; delivered as prompt text. No beyond-run provider memory is evidenced.\n- Write status and limits: implementation wired, operation uninspected. Delivery is evidenced by source; activation and benefit are not.\n\n> Before updating the cheatsheet, however, you should first assess the correctness of the provided solution\n> --- `prompts/curator_prompt_for_dc_cumulative.txt`\n\n#### On RT-OBJ-retrieval-context — Prior examples and vectors\n\n- Storage substrate: static CSV vector rows; dataset input corpus and the per-run list of generated outputs; benchmark output rows also preserve these values.\n- Representational form: the object is mixed, so its material parts are declared as `MEM-OBJ-example-history` and `MEM-OBJ-embedding-table` above.\n- Lineage and raw/derived distinction: dataset inputs and model outputs are the trace content; vectors are precomputed numerical selectors, not transformed outputs. The retrieval-synthesis branch derives text from selected traces and prior cheatsheet as described on `RT-RTE-retrieval-synthesis`.\n- Memory consumers and authority: full-history exposes all prior pairs to the generator; direct and cumulative retrieval expose cosine-selected pairs to the generator; retrieval-synthesis also exposes selected pairs to the curator. Model interpretation is not a correctness check.\n- Read-back trigger, selector, budget, delivery and limits: the selected approach and current question trigger retrieval. Cosine similarity ranks prior input vectors; `retrieve_top_k` limits pair count. The pair text is inserted in the generator prompt, or additionally in a curator prompt for retrieval-synthesis. No execution trace establishes relevance or effect.\n\n> elif approach_name in [\"Dynamic_Retrieval\", \"DynamicCheatsheet_RetrievalSynthesis\"]:\n> --- `dynamic_cheatsheet/language_model.py`\n\n#### On RT-OBJ-run-checkpoint — Benchmark output file\n\n- Storage substrate and contents: JSONL rows contain current input, target, model result fields, final cheatsheet and wrapper `steps`; the companion JSON stores arguments. The `steps` on curation routes retain current and new text alongside generator output, not the curator's full response or a separately attributed rationale.\n- Lineage and curation: each completed row extends the in-memory output list and the next text is the returned `final_cheatsheet`. There is no separate append-only history service; each write rewrites accumulated rows.\n- Read-back trigger and selector: continuation is caller-triggered by `continue_from_last_run_path`; code restores all rows, the last row's cheatsheet if present, and prior `final_output` values. Subsequent mode logic decides which text/pairs are consumed. The saved-argument check covers only a subset of run settings.\n- Persistence and delivery: local filesystem across invocations; restored values enter the benchmark's selected prompt route. The notebook also reads JSONL for after-the-fact answer scoring. Scoring does not modify or withdraw retained state.\n- Limits: operation, interruption recovery, file integrity and result provenance are unobserved. No atomic write, expiry, or automatic rollback is evidenced.\n\n> last_cheatsheet = outputs[-1].get(\"final_cheatsheet\")\n> --- `run_benchmark.py`\n\n#### On RT-RTE-retrieval-synthesis — Retrieve and synthesize\n\n- Write agency and retained source: local code selects prior pairs by cosine similarity; the curator model transforms those pairs together with the next input and previous text into a derived context. If it emits no expected tags, extraction falls back to the retrieved-pair text.\n- Read-back trigger and selection: each current question supplies its fixed vector; local code ranks earlier input vectors and selects at most `retrieve_top_k` associated input/output pairs. The curator receives these pairs, the next question and the prior returned text. The generator receives the extracted synthesis.\n- Persistence, delivery and later consumer: the benchmark stores the returned context in its row, carries it to the next iteration as prior text, and restores the last value on continuation. The next curator can read that text; the next generator sees the new synthesis. Selected traces remain available in output history for later retrieval.\n- Behavioral authority and limits: cosine controls pair selection; the model controls the synthesized prose; no answer target gates synthesis. Prompt inclusion is wired, but correct use and answer improvement are unobserved. The prompt requests critical treatment of examples, which is not a programmatic check.\n\n#### On RT-RTE-cumulative-retrieval — Cumulative text with retrieval\n\n- Write agency and source: the same curator operation as the cumulative route rewrites prior text from the current question and raw answer; the generator also receives the locally selected top-k example pairs.\n- Read-back trigger, selection and delivery: a current vector ranks earlier vectors; at most `retrieve_top_k` pairs plus the accumulated cheatsheet enter the generator prompt. The returned cumulative text is carried to a later question and saved for continuation. The curator sees prior cumulative text but not the selected examples or evaluator score in its request.\n- Rejection, persistence and limits: missing tags keep prior text; the evaluator's score does not reject a write. Pair selection and text delivery are wired, while model use and benefit are unobserved. No expiry or pruning is implemented.\n\n#### On RT-RTE-direct-retrieval — Top-k example context\n\n- Read-back trigger and selector inputs: current question's fixed vector ranks vectors for previous inputs; selected indices retrieve corresponding input/output pairs, up to `retrieve_top_k`.\n- Selected material, delivery and consumer: only selected pairs are inserted in the generator prompt. There is no curator call. The `final_cheatsheet` result field contains the context, is stored by the runner, and is restored as a value on continuation, but this branch does not use that value when building the next context.\n- Persistence, authority and limits: output history preserves the raw pairs; their use in later calls is governed by new cosine ranking. Delivery is wired, activation and benefit are unobserved, and vector provenance is unknown.\n\n#### On RT-RTE-full-history — Append prior solutions\n\n- Read-back trigger and selector: each question after the first triggers assembly of every earlier input/output pair in order; there is no relevance filter, answer check, or configured top-k budget.\n- Delivery, consumer and persistence: the assembled text is placed in the generator prompt; benchmark output history preserves pairs and continuation reloads them. The generator receives all prior outputs when this route is selected.\n- Limits: accumulation has no in-route removal or expiry. The source establishes prompt inclusion, not correct use or benefit; provider context limits and operation are not evidenced.\n\n#### On RT-RTE-checkpoint-resume — Save and resume benchmark state\n\n- Read-back selector and trigger: a caller supplies a prior JSONL path. The driver restores the saved rows, selects the last row's `final_cheatsheet` as current text, restores all `final_output` strings, and skips as many dataset rows as were loaded.\n- Persistence, delivery and consumers: each row is written after processing and the full list is written again at completion. On continuation, the selected approach determines whether restored text, output pairs, or both reach a model prompt. The result-analysis notebook reads saved rows for scoring but does not feed the score into memory.\n- Limits: parameter consistency checks cover only named settings in the implementation; no observed continuation or corruption-recovery run is supplied. A checkpoint is a caller-selected persistence path, not a transactional rollback mechanism.\n\n#### On RT-RTE-default — Single-call baseline\n\n- Memory status: no retained material is read or produced by this branch. The benchmark can still write its answer row, but a later default call receives no accumulated cheatsheet from this route.\n- Limits: implementation only; no provider execution was supplied.\n\n## Write side\n\nIn cumulative mode, write agency is the configured model acting as curator after each generator answer. The prompt asks it to assess the solution, preserve useful old items, add lessons, and replace the full text. The output extractor accepts text bounded by the first expected cheatsheet tags; otherwise it keeps the prior text. The code does not parse individual items, confirm usage counts, independently evaluate the answer, or compare a candidate against a trusted reference. This is prompt-guided model selection with a delimiter fallback, not validated curation (SRC-1, `prompts/curator_prompt_for_dc_cumulative.txt`, `dynamic_cheatsheet/language_model.py`, `dynamic_cheatsheet/utils/extractor.py`).\n\nIn retrieval-synthesis mode, the write input is selected earlier: local cosine ranking chooses prior examples, then the model writes a question-specific synthesis from those pairs, the next input and prior text. Missing tags return the retrieved-pair string. In cumulative-retrieval mode, retrieval examples reach the generator, but the curator prompt uses the question, raw generator answer and prior accumulated text. In direct retrieval and full-history modes, no curator writes derived text. These routes retain source examples rather than an answer-checked memory update (SRC-1, `dynamic_cheatsheet/language_model.py`, `prompts/curator_prompt_for_dc_retrieval_synthesis.txt`).\n\nThe cumulative generator response is retained in output rows alongside current and new cheatsheet strings. This gives a later reader the answer and before/after text, but not a standalone curator response or stated reasons for each kept, revised, omitted or rejected item. The benchmark target is in the row for evaluation, but is not passed to the curator. The evaluator runs after the memory write and produces counters; neither it nor the notebook removes a failing pair or rolls back a text replacement. A prior JSONL row can still contain an earlier text version, but the regular continuation path reads only the last row's current value. No automated withdrawal operation was found (SRC-1, `run_benchmark.py`, `EvaluatingResults.ipynb`).\n\n## Read-back\n\nThe generation route is selected by the caller or CLI `approach_name`, not by a memory-side policy. Cumulative text is supplied to generator prompts; retrieval-synthesis first supplies examples and previous text to a curator, then supplies the returned synthesis to the generator; cumulative retrieval supplies both accumulated text and selected examples to the generator; direct retrieval supplies only selected examples; full-history supplies every preceding pair; default supplies an empty placeholder. The benchmark carries returned text and generated outputs across examples. Direct library callers must supply their own subsequent inputs and state (SRC-1, `run_benchmark.py`, `dynamic_cheatsheet/language_model.py`, `README.md`, `ExampleUsage.ipynb`).\n\nFor retrieval, the selector is local cosine similarity over static vectors aligned to dataset inputs; `retrieve_top_k` bounds the selected pair count. The code neither uses task targets in ranking nor verifies the retrieved outputs. The vector provenance and accuracy are not established. Full-history has no relevance selection and grows with all preceding pairs. On continuation, restored values are available to the chosen route; availability does not mean every route consumes each restored field. Specifically, direct retrieval ignores its carried text value while using restored prior output pairs.\n\nPrompt insertion establishes delivery, not activation. No inspectable provider trace establishes whether or how the model relied on the text or examples, and no comparison establishes benefit. The evaluator and notebook are later consumers of answer records for scoring, not selectors or validators of retained memory. There is no repository-defined interface for users to edit or approve memory items separately from choosing prompt files, initial text, mode and call arguments.\n\n## Integration issues\n\n- The supplied `RT-OBJ-retrieval-context` combines distinct material: mutable per-run input/output history and a fixed precomputed vector table. They have different lineage, storage, and consumers. This report declares `MEM-OBJ-example-history` and `MEM-OBJ-embedding-table` as its two parts; keep the container identity and assess the parts separately. Evidence: SRC-1, `run_benchmark.py`, `dynamic_cheatsheet/language_model.py`, `embeddings/`. Consequence: do not describe the vector table as learned or updated by the trace-fed route.\n- `MEM-RTE-cumulative-write` is the curator write subroute within `RT-RTE-cumulative-loop`, declared as a part because its candidate-admission check and memory consumer differ from answer generation. The parent remains a valid end-to-end route. Evidence: SRC-1, `dynamic_cheatsheet/language_model.py`, `dynamic_cheatsheet/utils/extractor.py`. Consequence: treat model assessment as prompt guidance, not the benchmark's target-based oracle.\n- `RT-OBJ-cheatsheet` is the same returned text object discussed here, so it is annotated, not redeclared. `RT-OBJ-run-checkpoint` is the same JSONL persistence object and is annotated, not redeclared. Evidence: SRC-1, `run_benchmark.py`, `dynamic_cheatsheet/language_model.py`. Consequence: retain the supplied runtime IDs and use the memory-specific consumers above.\n- The existing `RT-RTE-cumulative-loop`, `RT-RTE-retrieval-synthesis`, `RT-RTE-cumulative-retrieval`, `RT-RTE-direct-retrieval`, `RT-RTE-full-history`, `RT-RTE-default` and `RT-RTE-checkpoint-resume` already identify the end-to-end runtime routes. This report annotates their memory aspects rather than creating duplicate route records. Evidence: SRC-1, `dynamic_cheatsheet/language_model.py`, `run_benchmark.py`. Consequence: use their existing IDs when integrating route-level findings.\n- The checked-in evaluation path recomputes answer scores from saved generator outputs but does not send results into memory admission, deletion, or rollback. Evidence: SRC-1, `EvaluatingResults.ipynb`, `run_benchmark.py`. Consequence: evaluation artifacts cannot support a claim that incorrect memory is withdrawn or that subsequent memory reflects benchmark feedback.\n- Provider-side retention and user-visible mutation beyond passing or choosing text are unresolved because the repository contains only provider-call construction, not service behavior or a deployed caller trace. Evidence: SRC-1, `text_generation/simple_unified_client.py`, `dynamic_cheatsheet/language_model.py`. Consequence: conclusions stop at repository-owned prompt delivery and local persistence.\n\n## Limitations and checks\n\nSource identity rechecked against the frozen boundary: repository `https://github.com/suzgunmirac/dynamic-cheatsheet`, commit `5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9`; cited source paths are within the registered checkout. The runtime baseline was treated as provisional and cross-checked against the benchmark CLI/caller, wrapper branches, extraction code, curation prompts, provider client interface, evaluator and result notebook. No live provider operation, continuation trace, candidate-quality audit, memory-effect experiment, or reproducible provenance for vectors/results was supplied. These gaps prevent claims about actual activation, correctness, usefulness, improvement, or service-side retention. The final `commonplace-analysis-check <run-state> memory-0 <output>` passed after quotation repairs; that check establishes member form and citation occurrence, not finding correctness or analysis independence.\n"
    },
    "records/epistemic.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md",
      "sha256": "68ed2d1808972d5a9cb4142b3111bc8f04f5c61dcae8886585e6def4df4dfd22",
      "text": "---\ntype: agentic-system-analyses/types/agentic-system-epistemic-report.md\ndescription: \"Epistemic routes of Dynamic Cheatsheet at 5cfe3c3\"\nrun-id: AAS-2026-10-04-dynamic-cheatsheet-01\nreviewed-boundary: \"5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\"\n---\n\n# Dynamic Cheatsheet epistemic report\n\n## Source-and-claim boundary\n\nDynamic Cheatsheet (DC) is a prompt-driven inference-time system that generates answers and carries text between calls. This report traces candidate production, curation, task checking, exposure to future calls, and the separately exposed generic text-generation client. The frozen whole-system boundary is SRC-1 at commit 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9. The question is whether DC produces, checks, accepts, retains, and operationally uses truth-apt content, and what evidence warrants the README's learning and performance claims.\n\nThe run code-inspected prompt assembly, extraction, local code execution, benchmark dispatch and score consumers, checkpoint/resume, retrieval, and the generic provider client operations. Supplied runtime records cover the cumulative and alternative approach flows and code execution paths. The benchmark loads task targets for its local evaluator after answer and cheatsheet generation; it does not pass those targets to the generator or curator. That benchmark check therefore cannot have admitted or withdrawn the cheatsheet for the already completed question.\n\n| Entry point or operation | Source path | Covering records | Coverage and limit |\n|---|---|---|---|\n| Benchmark CLI, approach dispatch, per-question state and scoring | `run_benchmark.py`; `dynamic_cheatsheet/language_model.py` | RT-RTE-cumulative-loop; RT-RTE-retrieval-synthesis; RT-RTE-cumulative-retrieval; RT-RTE-direct-retrieval; RT-RTE-full-history; RT-RTE-default; EPI-RTE-benchmark-answer-check | Directly inspected. The benchmark exposes the listed approaches and task evaluator branches. It scores after curation and carries state irrespective of score. No benchmark execution trace was supplied. |\n| Generator and curator content routes | `prompts/generator_prompt.txt`; `prompts/curator_prompt_for_dc_cumulative.txt`; `prompts/curator_prompt_for_dc_retrieval_synthesis.txt`; `dynamic_cheatsheet/language_model.py`; `dynamic_cheatsheet/utils/extractor.py` | RT-RTE-cumulative-loop; RT-RTE-retrieval-synthesis; RT-RTE-cumulative-retrieval | Directly inspected. The prompts declare desired reasoning; model interpretation and any actual assessment are unobserved. |\n| Retrieval and example selection | `dynamic_cheatsheet/language_model.py`; `run_benchmark.py`; `embeddings/` | RT-OBJ-retrieval-context; RT-RTE-retrieval-synthesis; RT-RTE-direct-retrieval; RT-RTE-cumulative-retrieval; RT-RTE-full-history | Directly inspected. Vectors are precomputed; their provenance and any correctness relation to examples are unknown. |\n| Benchmark evaluators and disconnected evaluator utilities | `run_benchmark.py`; `dynamic_cheatsheet/utils/evaluation.py`; `dynamic_cheatsheet/utils/sonnet_eval.py` | EPI-RTE-benchmark-answer-check | Directly inspected. The runner dispatches Game of 24, AIME exact matching, multiple choice, and equation balancing. Checkmate and programming-puzzle helpers and sonnet checks exist but are not dispatched by this runner or connected to curation. Their independent use does not establish an automatic DC check route. |\n| Local execution and provider code tools | `dynamic_cheatsheet/utils/execute_code.py`; `dynamic_cheatsheet/language_model.py`; `text_generation/simple_unified_client.py` | RT-RTE-local-python; RT-RTE-provider-code | Directly inspected. Execution output returns to the model as text. No execution, host configuration, or provider-side trace was supplied. |\n| Generic client conversation and optional web search | `text_generation/simple_unified_client.py`; `dynamic_cheatsheet/language_model.py` | EPI-RTE-client-chat; EPI-RTE-client-search | Directly inspected. Public client methods support in-memory chat history and optional Tavily or provider web search. The DC wrapper constructs the client without web-search settings and calls `generate_from_messages`, which disables native search flags; the optional `chat` route is separately callable. An OpenAI search-preview model can still invoke its model-specific search behavior through the wrapper, but provider operation is not observed. |\n| Result, data and notebook artifacts | `results/`; `data/`; `embeddings/`; `EvaluatingResults.ipynb`; `ExampleUsage.ipynb`; `figures/` | RT-OBJ-run-checkpoint; RT-OBJ-retrieval-context | Included in the registered boundary. Saved files and notebook outputs do not identify a run under these frozen conditions or establish artifact provenance, reproducibility, or a component's causal effect. |\n| Provider services, model internals and actual search/code-tool operation | External provider APIs and execution services | RT-CMP-model; RT-RTE-provider-code; EPI-RTE-client-search | Not inspectable in SRC-1. This prevents conclusions about the provider's resolved model version, internal checks, web results, sandbox, execution, or effects. |\n\nREADME claims relevant to this question are EPI-CLM-performance-gains and EPI-CLM-zero-shot-learning. The source register is supplied in boundary.md; the evidence identity is SRC-1 throughout. The retained boundary quotation that identifies the shipped curation, carry-forward, and evaluation loop is:\n\n> After each answer, a curator call updates the cheatsheet.\n> --- `dynamic_cheatsheet/language_model.py`\n\n## Epistemic-object inventory\n\n| Object ID | Candidate truth-apt content or none | Claimed role | Evidence source ID and local anchor | Gap/limit |\n|---|---|---|---|---|\n| RT-OBJ-cheatsheet | Mixed natural-language procedures, examples, and possible factual or mathematical assertions | Curated reusable problem-solving text | SRC-1 `prompts/curator_prompt_for_dc_cumulative.txt`; RT-OBJ-cheatsheet | The format permits truth-apt claims and instructions. No supplied live instance shows its content, origin, or correctness. Instructions and factual claims need separate interpretation. |\n| EPI-OBJ-current-answer | A candidate answer to the current task; truth apt only over the task's stated question and answer domain | Generator output extracted for return and, in benchmark mode, task scoring | SRC-1 `dynamic_cheatsheet/language_model.py`; `dynamic_cheatsheet/utils/extractor.py`; RT-OBJ-run-checkpoint | The model's actual answer and its premises are unobserved. Extraction changes the returned string, not its warrant. |\n| EPI-OBJ-prior-pairs | Natural-language benchmark inputs and prior model outputs; outputs may assert answers or procedures | Context material for top-k retrieval or full-history prompting | SRC-1 `dynamic_cheatsheet/language_model.py`; `run_benchmark.py`; RT-OBJ-retrieval-context | Prior outputs have no route-linked correctness status. They may be wrong; task targets are not used to filter them. |\n| EPI-OBJ-retrieval-vectors | None individually: dense numeric vectors and cosine scores encode selection signals, not stated propositions | Select prior examples for some approaches | SRC-1 `dynamic_cheatsheet/language_model.py`; `run_benchmark.py`; RT-OBJ-retrieval-context | Vector model, construction conditions, and semantic alignment are not established. Similarity is not correctness or warrant. |\n| EPI-OBJ-client-history | Conversation text, including user messages and generated assistant responses; some responses can make truth-apt claims | In-memory history consumed by later calls to the generic client | SRC-1 `text_generation/simple_unified_client.py` | This is separate from the DC cheatsheet and benchmark JSONL. It has no automatic truth check or disk persistence in the inspected client. |\n| EPI-OBJ-tavily-results | Imported natural-language result snippets with titles and URLs | Optional external context for a generic-client call | SRC-1 `text_generation/simple_unified_client.py` | Result provenance, source truth, ranking quality and actual use are not established. Native provider search has no locally inspectable result object. |\n\nThe three EPI parts of supplied aggregate objects retain their parent identity: EPI-OBJ-current-answer is part of RT-OBJ-run-checkpoint; EPI-OBJ-prior-pairs and EPI-OBJ-retrieval-vectors are each part of RT-OBJ-retrieval-context. The parents remain valid aggregate records. These distinctions separate answer checking, prior-output consumption, and non-propositional numeric selection. EPI-OBJ-client-history, EPI-OBJ-tavily-results and EPI-OBJ-provider-search have no supplied equivalent: they distinguish in-memory conversation, optional Tavily snippets, and inaccessible provider-side search content from tracked prior examples.\n\n## Authority-route ledger\n\n### Candidate generation and curation\n\nRoute ID: RT-RTE-cumulative-loop\n\nRoute function: content transformation\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-current-answer\n- Content/update relation: truth-apt transformation: indeterminate — the model generates an answer from the task and prompt context; without an inspectable candidate and warranted premises, entailed derivation, ampliative conjecture, and other model transformations cannot be distinguished.\n- Transition or check target: current task question and requested answer domain.\n- Evaluator/condition and domain: configured model interprets the generator prompt and supplied cheatsheet; no external answer oracle enters this call in the benchmark loop.\n- Activation and timing: each selected approach calls the generator for the current input; operation is unobserved.\n- Possible or observed result: candidate natural-language answer and raw response; no observed result in this run.\n- Implemented force: candidate is returned and may be scored after curation; it is not itself accepted as warranted by this route.\n- Epistemic authority and scope: none established by generation, fluency, or prompt fit.\n- Operational authority: the returned answer is the answer used by the driver; the caller receives it in direct API use.\n- Behavioral-authority path: benchmark driver or library caller, through returned answer text, for the current question only; benchmark score prints a counter.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; RT-RTE-cumulative-loop.\n- Claim IDs or none: EPI-CLM-performance-gains; EPI-CLM-zero-shot-learning.\n- Mismatch marker or none: README improvement and learning claims exceed candidate-generation wiring.\n- Gap/limit: no model output, provider operation, warranted premise set, or answer trace was supplied.\n\nRoute ID: RT-RTE-cumulative-loop; linked to the candidate-generation row\n\nRoute function: check/evidence production\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-current-answer and RT-OBJ-cheatsheet\n- Content/update relation: no content change as a distinct check result; the curator prompt requests a correctness and usefulness assessment while also asking for replacement text.\n- Transition or check target: the generated solution and existing cheatsheet entries, for correctness and future usefulness.\n- Evaluator/condition and domain: the same configured model is prompted to judge the solution; no reference answer, evidence source, explicit criterion, or independent evaluator is wired into the curator call.\n- Activation and timing: follows each generated answer in cumulative and hybrid routes; no separate assessment artifact is returned or stored.\n- Possible or observed result: any judgment is embedded in the proposed cheatsheet; no judgment trace is supplied.\n- Implemented force: the prompt directs a model assessment; the software does not verify that an assessment occurred or that it is correct.\n- Epistemic authority and scope: no warranted correctness conclusion; the prompt's instruction to include tested and proven solutions is not evidence that tests occurred.\n- Operational authority: any resulting replacement can affect later prompts through extraction and carry-forward.\n- Behavioral-authority path: curator model to parser to next generator prompt, advisory text becoming operative context; later effect is unobserved.\n- Evidence source ID and local anchor: SRC-1 `prompts/curator_prompt_for_dc_cumulative.txt`; `dynamic_cheatsheet/language_model.py`; RT-RTE-cumulative-loop.\n- Claim IDs or none: EPI-CLM-performance-gains; EPI-CLM-zero-shot-learning.\n- Mismatch marker or none: instructed self-assessment is not an independently checked acceptance decision.\n- Gap/limit: no separate check output, reference oracle, reviewer, or operation trace.\n\n> Before updating the cheatsheet, however, you should first assess the correctness of the provided solution and strategically incorporate code blocks, insights, and solutions into the new cheatsheet.\n> --- `prompts/curator_prompt_for_dc_cumulative.txt`\n\nRoute ID: RT-RTE-cumulative-loop; linked to both preceding rows\n\nRoute function: disposition/acceptance\nArchitectural status: implemented\n- Object/candidate ID: RT-OBJ-cheatsheet\n- Content/update relation: truth-apt transformation: indeterminate — a full natural-language replacement may preserve, revise, remove, or add assertions; no instance permits semantic comparison.\n- Transition or check target: curator response as candidate replacement for the prior text.\n- Evaluator/condition and domain: deterministic tag extraction checks for an opening `<cheatsheet>` marker; it does not check truth, closing-tag completeness, evidence, scope, or intended use.\n- Activation and timing: runs after each curator call; absence of an opening marker falls back to the previous text.\n- Possible or observed result: extracted text becomes `final_cheatsheet`; otherwise the fallback text is returned.\n- Implemented force: operational replacement or fallback, not epistemic acceptance under an evidence-consuming criterion.\n- Epistemic authority and scope: no truth warrant is licensed by delimiter presence.\n- Operational authority: replacement controls later cumulative context; fallback retains the old text.\n- Behavioral-authority path: extractor and benchmark state variable to later generator and curator prompts; across subsequent questions and resume.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/utils/extractor.py`; `dynamic_cheatsheet/language_model.py`; RT-RTE-cumulative-loop.\n- Claim IDs or none: EPI-CLM-performance-gains; EPI-CLM-zero-shot-learning.\n- Mismatch marker or none: syntax gate substitutes for no content-directed check or rollback.\n- Gap/limit: no candidate, assessment rationale, rejection record, validation result, or route-linked version history is supplied.\n\nSource-inspected deduction: for response `prefix <cheatsheet>alpha</cheatsheet> suffix`, the extractor returns `alpha`; for a response with an opening marker but no closing marker, the split returns all remaining text. A response with no opening marker returns the prior text. This parser changes the string boundaries without establishing the meaning or truth of the enclosed text.\n\n> if \"<cheatsheet>\" in response:\n>     try:\n>         txt = response.split(\"<cheatsheet>\")[1].strip()\n>         txt = txt.split(\"</cheatsheet>\")[0].strip()\n>         return txt\n> --- `dynamic_cheatsheet/utils/extractor.py`\n\n### Retrieved and accumulated prior outputs\n\nRoute ID: RT-RTE-retrieval-synthesis\n\nRoute function: operational admission/selection/consumption\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-retrieval-vectors and EPI-OBJ-prior-pairs\n- Content/update relation: no content change in cosine ranking; the selected prior pairs are then included as prompt text. Curator synthesis is assessed separately as indeterminate truth-apt transformation.\n- Transition or check target: prior input/output examples eligible for the current prompt.\n- Evaluator/condition and domain: local cosine similarity over supplied vectors, descending top-k; no answer oracle or correctness predicate.\n- Activation and timing: on each retrieval-synthesis request; selected pairs reach curator and synthesized text reaches generator.\n- Possible or observed result: a ranked top-k list and candidate synthesized context; no run output supplied.\n- Implemented force: similarity ranking determines context inclusion; it does not determine truth or acceptance.\n- Epistemic authority and scope: none; vector similarity does not warrant the prior answers or their relevance as correct.\n- Operational authority: determines which earlier examples the model can see.\n- Behavioral-authority path: local ranker to curator and generator prompts, for the current call; any later returned text is carried by the benchmark.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; `run_benchmark.py`; RT-RTE-retrieval-synthesis.\n- Claim IDs or none: EPI-CLM-performance-gains.\n- Mismatch marker or none: selected similarity is not evidence of answer correctness or benefit.\n- Gap/limit: vector provenance, example validity, actual selection and downstream effects are unobserved.\n\nRoute ID: RT-RTE-cumulative-retrieval; linked to RT-RTE-retrieval-synthesis\n\nRoute function: operational admission/selection/consumption\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-retrieval-vectors; EPI-OBJ-prior-pairs; RT-OBJ-cheatsheet\n- Content/update relation: truth-apt transformation: non-ampliative reshaping for deterministic assembly of selected examples beside cumulative text; truth-apt transformation: indeterminate for the curator's replacement.\n- Transition or check target: similar prior examples and current cumulative text supplied to answer generation; prior cumulative text supplied to curator.\n- Evaluator/condition and domain: cosine top-k selects examples; the curator model proposes text without answer-target feedback.\n- Activation and timing: retrieval and generation occur for the current question; curation follows the answer.\n- Possible or observed result: prompt containing the cumulative text plus examples, then candidate replacement text; no operation trace.\n- Implemented force: inclusion in the prompt and subsequent replacement/fallback determine context, with no correctness gate.\n- Epistemic authority and scope: none established for selected examples or replacement text.\n- Operational authority: selected examples and cumulative text can shape later model inputs.\n- Behavioral-authority path: ranker and curator through prompt strings and benchmark carry-forward, across later questions.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; RT-RTE-cumulative-retrieval.\n- Claim IDs or none: EPI-CLM-performance-gains; EPI-CLM-zero-shot-learning.\n- Mismatch marker or none: context selection and prompt guidance do not establish verified learning.\n- Gap/limit: vector provenance, curation quality and effect are unobserved.\n\nRoute ID: RT-RTE-direct-retrieval\n\nRoute function: operational admission/selection/consumption\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-retrieval-vectors and EPI-OBJ-prior-pairs\n- Content/update relation: truth-apt transformation: non-ampliative reshaping — selected input/output strings are assembled as prompt context.\n- Transition or check target: prior model outputs selected for the current answer.\n- Evaluator/condition and domain: descending cosine top-k only; the prompt warns that examples may contain errors, but no checker verifies them.\n- Activation and timing: before each generator call; this branch does not consume the carried cheatsheet.\n- Possible or observed result: selected examples become visible to the generator; no observed response supplied.\n- Implemented force: prompt inclusion only.\n- Epistemic authority and scope: none; prior answers remain unverified.\n- Operational authority: selected examples can influence the current answer.\n- Behavioral-authority path: local ranker to generator prompt, one current call at a time.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; RT-RTE-direct-retrieval.\n- Claim IDs or none: EPI-CLM-performance-gains.\n- Mismatch marker or none: similarity ranking does not check examples.\n- Gap/limit: no embedding provenance, answer traces, or observed behavioral effect.\n\nRoute ID: RT-RTE-full-history\n\nRoute function: operational admission/selection/consumption\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-prior-pairs\n- Content/update relation: truth-apt transformation: non-ampliative reshaping — all previous input/output pairs are formatted and appended in order.\n- Transition or check target: all preceding benchmark outputs, including unchecked or incorrect answers.\n- Evaluator/condition and domain: no selection or correctness check; sequence position alone includes each pair.\n- Activation and timing: before every generator call after the first example.\n- Possible or observed result: full prior history is included; no run output supplied.\n- Implemented force: prompt inclusion.\n- Epistemic authority and scope: none established for any prior answer.\n- Operational authority: all prior answer text can affect later generation.\n- Behavioral-authority path: benchmark history to generator prompt, increasing over a run and restored on resume.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; RT-RTE-full-history.\n- Claim IDs or none: EPI-CLM-performance-gains.\n- Mismatch marker or none: retention and use extend exposure without accuracy selection.\n- Gap/limit: no run trace or context-limit behavior supplied.\n\nRoute ID: RT-RTE-checkpoint-resume\n\nRoute function: retention\nArchitectural status: implemented\n- Object/candidate ID: RT-OBJ-run-checkpoint and RT-OBJ-cheatsheet\n- Content/update relation: truth-apt transformation: non-ampliative reshaping — JSON serialization retains returned text, answers, inputs, and targets.\n- Transition or check target: accumulated rows and latest returned text.\n- Evaluator/condition and domain: filesystem write and row-count resume; no integrity, correctness, or full-configuration check.\n- Activation and timing: after each scored example and at completion; on continuation, reads prior rows and skips their count.\n- Possible or observed result: durable JSONL and reloadable candidate state; no interruption/resume trace supplied.\n- Implemented force: saved text and outputs can be restored into later benchmark calls.\n- Epistemic authority and scope: persistence adds no warrant and the saved target does not mark its answer as accepted.\n- Operational authority: loaded output/text seeds subsequent retrieval, full history, or cumulative context according to the selected approach.\n- Behavioral-authority path: checkpoint file to benchmark driver to later model prompts, within continued runs.\n- Evidence source ID and local anchor: SRC-1 `run_benchmark.py`; RT-RTE-checkpoint-resume.\n- Claim IDs or none: EPI-CLM-performance-gains.\n- Mismatch marker or none: checkpoint recovery is not validation or rollback.\n- Gap/limit: file integrity, changed unchecked parameters, and actual recovery are not established.\n\n### Benchmark answer check\n\nRoute ID: EPI-RTE-benchmark-answer-check\n\nRoute function: check/evidence production\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-current-answer\n- Content/update relation: no content change — task-specific functions return a Boolean match result about the extracted answer.\n- Transition or check target: final answer against the benchmark target and task rules; e.g. arithmetic expression and digit use, exact AIME text, multiple-choice option, or equation balance.\n- Evaluator/condition and domain: local program. Game of 24 evaluates the expression, requires value 24 and compares used digits; AIME removes listed punctuation/newlines and compares normalized strings; multiple choice uses option parsing and text/letter heuristics; equation balancing checks operator-stripped form and computed value. The benchmark target is the answer oracle for these modes.\n- Activation and timing: after generation and curation, per benchmark question; not part of direct library calls.\n- Possible or observed result: Boolean updates printed correct/total counters; no execution result supplied.\n- Implemented force: counter and final summary only; score is neither fed back to curator nor used to reject, revise, withhold, or roll back the answer or cheatsheet.\n- Epistemic authority and scope: bounded to implemented task predicate and target. Exact match licenses only normalized textual identity; Game of 24 licenses arithmetic-rule compliance; the option heuristic licenses its own parsed match. None warrants reasoning, explanation, generalization, memory entries, or performance gains.\n- Operational authority: no admission or continuation decision; all output rows are written regardless of score.\n- Behavioral-authority path: evaluator to printed counters and summary, after the answer and cheatsheet have already been selected.\n- Evidence source ID and local anchor: SRC-1 `run_benchmark.py`; `dynamic_cheatsheet/utils/evaluation.py`; RT-RTE-cumulative-loop.\n- Claim IDs or none: EPI-CLM-performance-gains; EPI-CLM-zero-shot-learning.\n- Mismatch marker or none: task check is downstream measurement, not a content-admission oracle.\n- Gap/limit: evaluator operation and benchmark provenance are unobserved; helper evaluators for CheckmateInOne and Python programming puzzles are not dispatched by the runner, and `sonnet_errors` is not connected to it.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: inapplicable — the evaluator result is consumed locally by the benchmark counter rather than returned as model content.\n- Later read-back: inapplicable — the Boolean result is not persisted in the output row or supplied to a later model call.\n- Delegated visibility: the benchmark driver sees answer and target; generator and curator do not see this score in the inspected loop.\n- Selection predicate: the task-specific function listed above; its predicate varies by task and is not a universal correctness test.\n- Invalidation or expiry: inapplicable — no stored evaluator judgment exists to invalidate.\n- Activation or effect: counter changes only; answer/cheatsheet carry-forward and checkpoint write proceed on both true and false.\n- Evidence limits: source inspection establishes dispatch and predicate code, not evaluator execution, dataset validity, or the benchmark's experimental conditions.\n- Decision roles: benchmark code computes and reports the predicate; no human or model decision-maker uses it to admit or reject content.\n- Answer oracle: supplied benchmark target, consulted after curation for scoring only; direct library route has no such target oracle.\n- Operating mode and improvement trigger: benchmark mode, once per completed question; each score does not trigger a curation revision.\n\n> result = eval_for_GameOf24(original_input, final_answer)\n> --- `run_benchmark.py`\n\n> if not (abs(value - 24) < 1e-3):\n>     return False\n> --- `dynamic_cheatsheet/utils/evaluation.py`\n\n> if input_digits != output_digits:\n>     return False\n> return True\n> --- `dynamic_cheatsheet/utils/evaluation.py`\n\n> output = remove_punctuation(output)\n> output = convert_newline_to_space(output)\n> if target == output:\n>     return True\n> --- `dynamic_cheatsheet/utils/evaluation.py`\n\n> if target_text and target_text in clean_answer:\n>     return True\n> --- `dynamic_cheatsheet/utils/evaluation.py`\n\n> if abs(output_value - eval(target_val)) < 1e-6:\n>     return True\n> --- `dynamic_cheatsheet/utils/evaluation.py`\n\nSource-inspected deduction: answer extraction is a string-boundary operation. For `prefix <answer>42</answer> suffix`, the extractor returns `42`; it does not evaluate the answer. The benchmark checker is therefore the only wired local task comparison found in this loop, and its result has no admission force.\n\n>         txt = response.split(\"<answer>\")[-1].strip()\n>         txt = txt.split(\"</answer>\")[0].strip()\n>         return txt\n> --- `dynamic_cheatsheet/utils/extractor.py`\n\n> if result:\n>     correct_so_far += 1\n> total_so_far += 1\n> --- `run_benchmark.py`\n\n> # Save the outputs to a file after each example (for crash recovery)\n> write_jsonl(args.save_path_name, outputs)\n> --- `run_benchmark.py`\n\n### Execution and optional client routes\n\nRoute ID: RT-RTE-local-python\n\nRoute function: check/evidence production\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-current-answer\n- Content/update relation: non-truth-apt policy/content update: execution output is appended to the model conversation; this is an observation of program output, not by itself a truth check of the task answer.\n- Transition or check target: model-authored Python's behavior in the current host process, not a specified proof obligation.\n- Evaluator/condition and domain: Python interpreter returns stdout or an error string; no expected result or correctness criterion is imposed by the executor.\n- Activation and timing: generator response must contain the execution flag and code-fence shape; repeats can occur up to the depth limit.\n- Possible or observed result: stdout/stderr or timeout/error text is appended to the model conversation; no execution trace supplied.\n- Implemented force: model can use returned text in a subsequent generator turn; program side effects occur according to host permissions.\n- Epistemic authority and scope: output establishes at most what the process printed under its environment; it does not prove the code implements the intended algorithm or that the answer is correct.\n- Operational authority: model-authored code executes without an in-loop human approval; timeout kills the process but does not undo side effects.\n- Behavioral-authority path: generator output to Python process to captured text to next model turn; final transcript may enter saved answer history.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; `dynamic_cheatsheet/utils/execute_code.py`; RT-RTE-local-python.\n- Claim IDs or none: none.\n- Mismatch marker or none: requested self-validation is model guidance, not a separate check or safety gate.\n- Gap/limit: no host permission boundary, execution, output, rollback, or observed effect was supplied.\n\nRoute ID: RT-RTE-provider-code\n\nRoute function: check/evidence production\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-current-answer\n- Content/update relation: non-truth-apt policy/content update: provider code-tool output returns as model text; actual content and execution cannot be determined from source.\n- Transition or check target: model-proposed computation in provider-hosted code tooling.\n- Evaluator/condition and domain: caller's `use_code_interpreter` option attaches OpenAI or Claude tooling; no local correctness criterion or per-action reviewer is present.\n- Activation and timing: per enabled generator invocation; the benchmark can create an OpenAI container at startup.\n- Possible or observed result: provider response text; provider execution and result are unobserved.\n- Implemented force: returned text may inform the model's answer; provider-side effects, persistence, and cleanup are not inspected.\n- Epistemic authority and scope: none from tool attachment alone; execution correctness and sandbox behavior are unknown.\n- Operational authority: provider service decides execution within its external contract; caller option enables tool attachment.\n- Behavioral-authority path: model and provider tool to returned text, then caller or benchmark; result remains candidate content.\n- Evidence source ID and local anchor: SRC-1 `dynamic_cheatsheet/language_model.py`; `text_generation/simple_unified_client.py`; RT-RTE-provider-code.\n- Claim IDs or none: none.\n- Mismatch marker or none: tool availability does not establish correct computation, isolation, or rollback.\n- Gap/limit: provider sandbox, execution trace, result artifacts, and container lifetime are outside evidence.\n\nRoute ID: EPI-RTE-client-chat\n\nRoute function: retention\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-client-history\n- Content/update relation: truth-apt transformation: non-ampliative reshaping — the client appends the original user message and returned assistant text to an in-memory list.\n- Transition or check target: current conversation history for later client calls.\n- Evaluator/condition and domain: no check; the public `chat` method retains each response as supplied.\n- Activation and timing: each successful `chat` call on a client instance; history lasts in that object's memory until reset or replacement.\n- Possible or observed result: a later `chat` call includes previous user/assistant messages; no run trace supplied.\n- Implemented force: retained transcript is supplied to subsequent generic-client generation.\n- Epistemic authority and scope: none; keeping an answer does not endorse it.\n- Operational authority: prior conversation text can influence later replies.\n- Behavioral-authority path: `self.messages` to a later provider call, within one client instance; no automatic disk persistence.\n- Evidence source ID and local anchor: SRC-1 `text_generation/simple_unified_client.py`.\n- Claim IDs or none: EPI-CLM-zero-shot-learning.\n- Mismatch marker or none: this separately exposed transcript route is not the curated cheatsheet or benchmark checkpoint.\n- Gap/limit: direct API route is not called by `LanguageModel.advanced_generate` and no operation is observed.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: response text from the generic client.\n- Later read-back: subsequent calls use accumulated user and assistant messages; `reset_history` clears it and `set_history` replaces it.\n- Delegated visibility: generic-client provider call receives the full stored message history; this is not the benchmark curator route.\n- Selection predicate: append every successful call; no relevance or correctness filter.\n- Invalidation or expiry: only explicit reset or replacing history; no time-based expiry.\n- Activation or effect: history is included in subsequent calls; behavioral effect is unobserved.\n- Evidence limits: client code establishes storage and wiring only; provider interpretation and any effect are unobserved.\n\n> self.messages.append({\"role\": \"user\", \"content\": message})\n> self.messages.append({\"role\": \"assistant\", \"content\": full_response})\n> --- `text_generation/simple_unified_client.py`\n\nRoute ID: EPI-RTE-client-search\n\nRoute function: content transformation\nArchitectural status: implemented\n- Object/candidate ID: EPI-OBJ-tavily-results and EPI-OBJ-provider-search\n- Content/update relation: truth-apt transformation: acquisition/import — when enabled and results are returned, Tavily snippets are inserted before the caller's query; their source warrant is unknown.\n- Transition or check target: caller prompt plus external search snippets.\n- Evaluator/condition and domain: caller must enable Tavily and request `use_web_search`; client formats returned title, URL, and content but does not check their truth or source authority.\n- Activation and timing: optional generic-client `generate` or `chat` calls; the DC wrapper does not initialize or invoke this option.\n- Possible or observed result: formatted search context is sent to the provider; no search result or call trace supplied.\n- Implemented force: imported text can inform the model response; `chat` stores the original user message and assistant response, not a separate result record.\n- Epistemic authority and scope: none established by inclusion or URL display; current source truth and relevance remain unknown.\n- Operational authority: option controls whether fetched snippets are placed in the prompt.\n- Behavioral-authority path: Tavily response to generic-client prompt to provider response; ephemeral to the call except for any influence on retained assistant text.\n- Evidence source ID and local anchor: SRC-1 `text_generation/simple_unified_client.py`.\n- Claim IDs or none: none.\n- Mismatch marker or none: this optional exposed-client acquisition route is not part of the benchmark's configured wrapper path.\n- Gap/limit: no Tavily invocation, source pages, result provenance, or provider response was supplied.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: generated assistant text, not a structured search-result object.\n- Later read-back: the generic `chat` route retains the original message and response; it does not separately retain the fetched result snippets.\n- Delegated visibility: provider receives the prompt augmented with result text; downstream caller sees only returned model text.\n- Selection predicate: Tavily's external search response and caller's query; source ranking and search coverage are not established here.\n- Invalidation or expiry: inapplicable — result snippets are prompt-local and no separate result store is implemented.\n- Activation or effect: enabled search results are put in model input; actual interpretation and effect are unobserved.\n- Evidence limits: external search operation and provider response are unobserved; this route is not used by the DC wrapper as configured.\n\n> if use_web_search and self.web_search_enabled:\n>     query = search_query or prompt\n>     search_context = self._web_search(query)\n>     if search_context and not search_context.startswith(\"Web search failed\"):\n>         prompt = f\"Web Search Results:\\n{search_context}\\n\\nUser Query: {prompt}\"\n> --- `text_generation/simple_unified_client.py`\n\nThe client also declares native provider search tools and recognizes OpenAI search-preview model identifiers. `LanguageModel` calls `generate_from_messages`, which passes the native-search flag as false; the client nevertheless calls its OpenAI search-preview branch when the configured model name has that suffix. This is an optional provider-mediated acquisition route with implementation evidence but no inspectable search result, operation trace, or provider-side warrant. It can affect generator output and potentially the curator's answer input when such a model is selected; the run cannot determine whether a result was retrieved or used.\n\n## System-claim versus route comparison\n\n| Claim ID | Claimed operation or warrant | Claim source ID/anchor and evidence layer | Doctrine/design support | Implemented route IDs | Observed-run support | Causal support and design limits | Supported conclusion | Mismatch/unknown |\n|---|---|---|---|---|---|---|---|---|\n| EPI-CLM-performance-gains | Retaining curated insights improves task performance; README also reports substantial benchmark gains. | SRC-1 `README.md`, doctrine/design | Explicit performance, puzzle, math, and knowledge-task claims. | RT-RTE-cumulative-loop; RT-RTE-retrieval-synthesis; RT-RTE-cumulative-retrieval; RT-RTE-direct-retrieval; RT-RTE-full-history; EPI-RTE-benchmark-answer-check | None supplied under this run. Tracked result artifacts are not a frozen execution trace. | No intervention/comparison protocol or component-isolating evidence is available in this boundary; results cannot identify the curator, retrieval, prompts, model, or other component as cause. | Code implements answer generation, text carry-forward, context selection, and post-answer scoring. It does not support a conclusion that retained text improves performance or that the quoted gains are reproduced here. | Numerical report and causal explanation remain unverified; outcome score does not select or revise memory. |\n| EPI-CLM-zero-shot-learning | README says performance improves without ground-truth labels or human feedback, and frames accumulated experience as learning. | SRC-1 `README.md`, doctrine/design | Explicit “Zero-Shot Learning” and “Experience-Driven Learning” feature claims. | RT-RTE-cumulative-loop; EPI-RTE-benchmark-answer-check; RT-RTE-checkpoint-resume | No operation trace or capacity comparison supplied. | Benchmark targets are withheld from prompts and checked after curation, but the score is only counted. This is compatible with no target feedback into curation; it does not show improved future capacity. Optional exposed-client search and provider behavior are unobserved. | The benchmark wiring has no human admission gate and does not feed its target score into curation. It does retain or retrieve prior outputs. These routes alone do not establish learning or improved future capacity. | README's learning and performance claims exceed inspected operation evidence. “Without ground-truth labels” is not equivalent to verified learning; the benchmark does use target labels downstream for scorekeeping. |\n\n> * **Zero-Shot Learning**: Improves performance without ground-truth labels or human feedback\n> --- `README.md`\n\n> * **Puzzles**: GPT-4o's success rate on Game of 24 increased from approximately 10% to 99% after discovering and reusing Python-based solutions\n> --- `README.md`\n\n## Bounded conclusion\n\nDC generates truth-apt candidate answers and can produce replacement cheatsheet text, but the inspected model routes do not establish the candidates' truth or warrant. The curator is instructed to assess correctness and usefulness; it receives no benchmark target or independently established answer evidence. Its proposed replacement becomes operational when the parser sees an opening marker. Tag extraction is a syntactic disposition with a fallback, not evidence-consuming acceptance against a named criterion, intended use, and scope. Source-inspected string extraction only changes which text is returned.\n\nRetrieval and full-history modes admit previous model answers into later prompts. Similarity ranks examples by precomputed vectors, not correctness. Cumulative text, retrieved examples, and persisted rows can all affect future model inputs, but their later behavioral effect is not observed. Checkpointing provides a durable path to retain and reload those candidates; it neither endorses them nor records the downstream Boolean evaluator result.\n\nThe benchmark evaluator has bounded answer-oracle authority for its local task predicates. Game of 24 checks expression value and digit use, while the other task handlers perform normalized exact matching, option matching, or equation comparison. The score reaches counters and printed summaries after curation. It does not admit, reject, revise, or roll back an answer or memory entry. The standalone Checkmate, programming-puzzle and sonnet helpers are not wired into this dispatch.\n\nLocal Python execution can return process output to the generator, but no correctness condition distinguishes useful computation from unrelated output, and process side effects have no rollback. The optional provider code tool is similarly only an attached capability from this source boundary; its execution and cleanup contracts remain outside inspection. The generic provider client adds separately callable in-memory chat history and optional search. Those routes are not configured by the DC benchmark wrapper, except that a chosen OpenAI search-preview model may invoke provider-managed search behavior. No source or run evidence establishes what such a provider returns or how it uses it.\n\nThe README's performance figures, zero-shot claim, and learning characterization remain unsupported by an observed run or causal comparison in this boundary. No route establishes accepted ampliative knowledge: candidate generation is not acceptance, model-directed checking lacks an inspectable evidence basis, and retention or operational reuse adds no warrant. Provider internals, live web sources, benchmark provenance and execution, model versions, and causal contribution of individual components remain unassessed; they prevent stronger conclusions about correctness, learning, performance, or transfer.\n\n## Shared records\n\n### Operative objects\n\n#### EPI-OBJ-current-answer — Current task answer\n\nPart of: RT-OBJ-run-checkpoint\n\n- Identity: the `final_answer` text extracted from the current generator response and compared by the benchmark evaluator.\n- Representational form: natural-language text; task-dependent content may also be a formula, option token, or code.\n- Storage substrate: current result dictionary and, in benchmark mode, JSONL row.\n- Closest supplied record: RT-OBJ-run-checkpoint; distinct identity: the current candidate answer is the checked text field within the aggregate row.\n- Evidence: SRC-1 `dynamic_cheatsheet/language_model.py`; `dynamic_cheatsheet/utils/extractor.py`; `run_benchmark.py`.\n\n#### EPI-OBJ-prior-pairs — Prior input-output examples\n\nPart of: RT-OBJ-retrieval-context\n\n- Identity: benchmark questions paired with prior model outputs and exposed to retrieval or full-history prompts.\n- Representational form: natural-language inputs and outputs, which may include truth-apt answers, explanations, or code.\n- Storage substrate: dataset and output lists in the benchmark process; checkpoint rows persist outputs.\n- Closest supplied record: RT-OBJ-retrieval-context; distinct identity: these linguistic examples are separate from its dense vector part.\n- Evidence: SRC-1 `dynamic_cheatsheet/language_model.py`; `run_benchmark.py`.\n\n#### EPI-OBJ-retrieval-vectors — Precomputed retrieval vectors\n\nPart of: RT-OBJ-retrieval-context\n\n- Identity: per-task numeric embeddings loaded from CSV and compared by cosine similarity.\n- Representational form: distributed numeric vectors and scalar similarity values; no proposition is individually stated.\n- Storage substrate: tracked `embeddings/<task>.csv` and numeric arrays in process memory.\n- Closest supplied record: RT-OBJ-retrieval-context; distinct identity: numeric ranking state, separate from the natural-language input/output pairs.\n- Evidence: SRC-1 `run_benchmark.py`; `dynamic_cheatsheet/language_model.py`.\n\n#### EPI-OBJ-client-history — Generic client conversation history\n\n- Identity: `UnifiedLLMClient.messages`, a list of user messages and returned assistant responses.\n- Representational form: natural-language message text.\n- Storage substrate: client-instance memory, cleared by reset or replaced by set; no disk path in the inspected client.\n- Closest supplied records: RT-OBJ-cheatsheet and RT-OBJ-run-checkpoint; distinct identity: general conversational history rather than curated memory or benchmark persistence.\n- Evidence: SRC-1 `text_generation/simple_unified_client.py`.\n\n#### EPI-OBJ-tavily-results — Tavily search snippets\n\n- Identity: formatted result strings containing titles, URLs and returned content.\n- Representational form: natural-language text with symbolic URLs.\n- Storage substrate: transient augmented prompt; no separate result archive.\n- Closest supplied record: RT-OBJ-retrieval-context; distinct identity: external web snippets rather than tracked task examples and their vectors.\n- Evidence: SRC-1 `text_generation/simple_unified_client.py`.\n\n### Routes\n\n#### EPI-RTE-benchmark-answer-check — Benchmark task check\n\nPart of: RT-RTE-cumulative-loop\n\n- Identity: task-dispatched local comparison of extracted answer and benchmark target after answer and memory generation.\n- Endpoints and progression: answer and target → task-specific evaluator → Boolean → printed correct/total counter and summary.\n- Owner: benchmark driver dispatches; local evaluator code computes; no human or model consumes the Boolean for admission.\n- Context, state and effects: final answer and task target; only score counters change.\n- Evidence: SRC-1 `run_benchmark.py`; `dynamic_cheatsheet/utils/evaluation.py`.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: inapplicable — result remains a Boolean for the benchmark counter.\n- Later read-back: inapplicable — score is not persisted or sent to a later model call.\n- Delegated visibility: driver sees target and answer; generator and curator do not see the score.\n- Selection predicate: task-specific exact, arithmetic, option, or equation comparison.\n- Invalidation or expiry: inapplicable — no stored judgment to invalidate.\n- Activation or effect: changes counters and summary only.\n- Evidence limits: no live evaluator run or artifact provenance.\n- Compared supplied route: RT-RTE-cumulative-loop; distinct identity: its post-curation scoring subroute, whose result has no disposition force.\n- Decision roles: code computes and prints; no human or model veto.\n- Answer oracle: benchmark target, used after curation for scoring only.\n- Operating mode and improvement triggers: once per benchmark question; a score does not trigger a memory update.\n\n#### EPI-RTE-client-chat — Generic client chat and history\n\n- Identity: public `UnifiedLLMClient.chat` call that appends the user message and assistant response to instance history.\n- Endpoints and progression: message plus prior history → provider call → returned response → append both messages.\n- Owner: client caller chooses messages; client stores and reuses; provider generates response.\n- Context, state and effects: full instance transcript enters later calls through `self.messages`.\n- Evidence: SRC-1 `text_generation/simple_unified_client.py`.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: generated response text.\n- Later read-back: later `chat` calls include all stored messages until reset or replacement.\n- Delegated visibility: selected provider receives message history.\n- Selection predicate: every successful chat turn is appended.\n- Invalidation or expiry: explicit reset or set-history replacement only.\n- Activation or effect: history is included in subsequent provider input; effect is unknown.\n- Evidence limits: no provider call or output trace.\n- Compared supplied records: RT-RTE-cumulative-loop and RT-RTE-checkpoint-resume; distinct identity: standalone client history in memory, not the benchmark cheatsheet or JSONL state.\n- Decision roles: caller supplies turns; client appends; provider responds; no answer oracle or human admission.\n- Answer oracle: inapplicable — none is wired into generic chat.\n- Operating mode and improvement triggers: each successful chat call triggers history growth; no measured improvement evidence.\n\n#### EPI-RTE-client-search — Optional generic client search\n\n- Identity: configured generic-client Tavily search insertion, with separate provider-native search capability when configured.\n- Endpoints and progression: caller query → optional external search → result text in provider prompt → returned model response.\n- Owner: caller enables facility; Tavily or provider service selects results; model consumes resulting context.\n- Context, state and effects: Tavily text is inserted into the prompt. Chat history later stores the original message and response, not a separately identified search-result record.\n- Evidence: SRC-1 `text_generation/simple_unified_client.py`; `dynamic_cheatsheet/language_model.py`.\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n- Immediate return: model response text; search result is not returned as a structured client object.\n- Later read-back: only any search-informed assistant response can enter chat history.\n- Delegated visibility: provider receives Tavily-augmented prompt or configured native-search tool request.\n- Selection predicate: caller option and external search/provider behavior.\n- Invalidation or expiry: inapplicable — prompt-local result text is not separately stored.\n- Activation or effect: optional search context reaches the model; actual retrieval and effect are unobserved.\n- Evidence limits: no result, source, citation record, or provider execution supplied.\n- Compared supplied records: RT-CMP-model and RT-RTE-cumulative-loop; distinct identity: optional generic-client search route, not the benchmark's local vector retrieval or configured wrapper call.\n- Decision roles: caller enables; external service selects content; provider model returns text; no local source evaluation.\n- Answer oracle: inapplicable — no expected answer or reference outcome is used by search selection.\n- Operating mode and improvement triggers: only when directly configured and requested, or when the selected OpenAI model itself is a search-preview identifier; no search-related performance evidence.\n\n### Claims\n\n#### EPI-OBJ-provider-search — Provider-native search content\n\n- Identity: any search content used by a provider-managed search-preview or native-search feature; no separate result payload is exposed by the inspected wrapper.\n- Representational form: not determinable — the source does not expose the provider's search-result representation or content.\n- Storage substrate: provider service, inaccessible in SRC-1; the local response may contain only model-generated text.\n- Closest supplied record: RT-CMP-model; distinct identity: external search context is not the model's distributed parameters.\n- Evidence: SRC-1 `text_generation/simple_unified_client.py`; RT-CMP-model.\n\n#### EPI-CLM-performance-gains — Reported performance improvement\n\n- Claimed operation: carried strategies and code increase task performance, including the README's reported task gains.\n- Source: SRC-1 `README.md`, doctrine/design.\n- Closest supplied claim: none declared in runtime member; distinct identity: README-level empirical and causal claim, not a runtime route record.\n- Evidence: README claim quotation below; runtime records show wiring but no run trace or causal attribution.\n\n#### EPI-CLM-zero-shot-learning — Reported learning without feedback\n\n- Claimed operation: performance improves without ground-truth labels or human feedback through accumulated experience.\n- Source: SRC-1 `README.md`, doctrine/design.\n- Closest supplied claim: none declared in runtime member; distinct identity: README-level learning claim rather than an implementation route.\n- Evidence: README claim quotation above; the target is available to benchmark scoring after curation, but the score is not fed back into memory decisions.\n\n> * **Self-Curated Storage**: Automatically focuses on concise, transferable snippets rather than entire transcripts\n> --- `README.md`\n\n> * **Experience-Driven Learning**: Bridges the gap between isolated inference events and cumulative learning\n> --- `README.md`\n"
    },
    "records/reconciliation.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md",
      "sha256": "2e1466239ec7847e0842e2279acd4bc1df396e9a096fd974e632b4894e273e3a",
      "text": "---\ntype: agentic-system-analyses/types/agentic-system-reconciliation-report.md\ndescription: Reconciliation of Dynamic Cheatsheet records at 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\nrun-id: AAS-2026-10-04-dynamic-cheatsheet-01\nreviewed-boundary: 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\n---\n\n# Dynamic Cheatsheet reconciliation\n\n## Reconciliation\n\nAmendment: EPI-OBJ-provider-search has a superseded value of operative-object declaration placed under `### Claims`; its replacement classification is an operative object under `### Operative objects`, retaining the same ID and referent: provider-native search content used through a provider-managed search feature. The evidence anchor is SRC-1 `text_generation/simple_unified_client.py`, which documents the client-side search-preview/native-search capability while leaving the content and representation provider-managed and inaccessible. Affected findings: the epistemic member's EPI-OBJ-provider-search declaration and the source-and-claim boundary and authority-route ledger references to this ID. This repairs the kind/placement classification without renaming the record or changing its referent; the evidence still does not expose provider search content or establish its operation.\n\nAmendment: EPI-OBJ-prior-pairs is superseded by MEM-OBJ-example-history; both identify the same prior task-input and generated-output pairs, and the memory member already declares that operative part of RT-OBJ-retrieval-context. Evidence: SRC-1 `run_benchmark.py` and `dynamic_cheatsheet/language_model.py`; the memory record documents the matching identity, lineage, storage and consumers. Affected findings: epistemic-object inventory and authority-route ledger rows referencing EPI-OBJ-prior-pairs; read them as MEM-OBJ-example-history. The RT-OBJ-retrieval-context container remains valid.\n\nAmendment: EPI-OBJ-retrieval-vectors is superseded by MEM-OBJ-embedding-table; both identify the fixed per-task numeric vectors loaded from CSV and used for cosine ranking, already declared as a part of RT-OBJ-retrieval-context. Evidence: SRC-1 `embeddings/`, `run_benchmark.py` and `dynamic_cheatsheet/language_model.py`; the memory record establishes the same identity, lineage, storage and consumer. Affected findings: epistemic-object inventory and authority-route ledger rows referencing EPI-OBJ-retrieval-vectors; read them as MEM-OBJ-embedding-table. The RT-OBJ-retrieval-context container remains valid.\n\nThe two independently authored memory parts and the epistemic overlay's corresponding distinctions converge on the same split of RT-OBJ-retrieval-context into linguistic task/output history and static numeric selector vectors. The declared MEM records provide the canonical identities; the superseded EPI records do not add further parts. RT-OBJ-cheatsheet, RT-OBJ-run-checkpoint, RT-RTE-cumulative-loop and the other runtime route records remain valid containers or end-to-end routes. MEM-RTE-cumulative-write and EPI-RTE-benchmark-answer-check are distinct subroutes within RT-RTE-cumulative-loop: memory replacement and post-curation scoring, respectively. Their `Part of:` relations do not supersede the parent. EPI-OBJ-current-answer is a distinct answer field within RT-OBJ-run-checkpoint, not a duplicate of that aggregate object. EPI-OBJ-provider-search remains a separate operative object for inaccessible provider-managed search content, not a claim or the configured benchmark's vector retrieval.\n\nThe memory analyst owns the memory-route admission and read-back findings on runtime routes, including MEM-RTE-cumulative-write and its annotation fields. Epistemic entries assess warrant and authority separately; their descriptions of route mechanics do not amend or strengthen the memory findings. The runtime account and memory findings agree that benchmark scoring occurs after curation and does not control memory admission. No substantive conflict requires return to the memory analyst.\n"
    },
    "method/collection.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md",
      "sha256": "bd9deecccdd14af4ad5a5d920a922d4854fb9b9b4df80c01b87b9c3c66b2fbd3",
      "text": "# Writing conventions for kb/agentic-system-analyses/\n\n## Purpose and quality\n\nThis collection holds workflow-produced analyses of external agentic systems,\ntheir run state, local types and the analysis method.\nTheir contribution is an evidence-grounded account of source-native operation,\nwith bounded synthesis and explicit limitations. The quality goal is fidelity\nand economy. Explain the external mechanism before applying Commonplace concepts;\nqualify mappings and keep evidence limits beside claims. The source account\nmust remain intelligible when a reader rejects the mapping.\n\n## Authoring\n\nFor titled reports, name the system and report role. Use the supplied member type's title and description conventions; worker\nfragments keep their job's exact headings. State the evidence basis and capture boundary in the\nmember's format; attribution and record formats belong to its supplied contracts.\n\nAnalysis workers write only assigned working outputs. Retained analyses are frozen: substantive\ncorrections require a new run. Assembly, acceptance and publication are\ncoordinator tasks.\n\n## Placement and types\n\n`state/` holds working analyses. `retained/` holds the current accepted\nanalysis of each system, one directory per system. `retained-archive/` holds\nsuperseded analyses. `instructions/` holds the analysis method.\nComparative writing belongs in `kb/agentic-systems/`; current Commonplace transfer advice belongs in\nits separately commissioned transfer scan. Neither belongs in an analysis. Transferable KB theory belongs in `kb/notes/`;\nraw source captures belong in source snapshot storage.\n\nUse global types under `types/` or local types under\n`agentic-system-analyses/types/`. Frontmatter-free Markdown is text. Method authors follow\n[method maintenance](./instructions/maintain-analysis-method.md) before editing\nlocal types or instructions. Analysts do not load it.\n\n## Outbound links\n\n- `kb/sources/`: cite tracked ingests, never snapshots; `evidenced-by`.\n- External: cite evidence used, preferably version-pinned; do not prospect the open\n  web; `evidenced-by`.\n- `kb/notes/`: cite theory used in a mapping; `rests-on`, `defined-in`.\n- Within this collection: point to supporting members or records; `see-also`.\n- Local instructions and types: link their governing contracts; `composition`, `see-also`.\n"
    },
    "method/worker-rules.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md",
      "sha256": "7c377c04e6846e1fe468bd1e7bb552e1ffa0cccaa8eb2e062b4b4bdd351f78be",
      "text": "---\ndescription: \"Rules every job of an analyse-agentic-system workflow run follows: authority, command discipline, source reading, quotation, and prior-analysis exposure\"\ntype: types/instruction.md\n---\n\n# Follow the worker rules of an analysis run\n\nProduce the result of one code-scheduled analysis job within its assigned\nevidence and write scope. Code schedules the job and judges its output.\n\n## Invocation and authority\n\nRead the supplied invocation prompt completely and recover any truncated\nread before proceeding. Use its job parameters and retry feedback unchanged.\n\nEvery job uses these parameters; its instruction defines additional ones:\n\n| Parameter | Meaning |\n|---|---|\n| `system` | Source-native system name |\n| `run-id` | Exact run identity; copy unchanged when the member type requires it |\n| `run-state` | Absolute path passed to the acceptance check; not an evidence input |\n| `job` | Exact job name passed to the acceptance check |\n| `command-path` | Directory of this run's `commonplace-*` commands, when supplied |\n| `output` | Absolute path for the completed result |\n| `problem` | Absolute path for the reason the job cannot finish |\n| `workspace` | Absolute per-job writable directory, when supplied by the workflow |\n| `scratch` | Absolute directory for intermediate files, including extracts |\n\nA repair invocation may also supply `previous-output`, the absolute path of\nthe preserved report from the refused attempt. Read that file as the baseline.\nAmend the reported failures and any dependent findings, preserving unrelated\nanalysis and records. Write the amended report to `output`; do not repeat the\nfull analysis. For quote repairs, check whether the corrected passage supports\nthe associated claim and carry any change through dependent conclusions. If\nthe source cannot support the claim, narrow or withdraw it and update the text.\n\nUse supplied paths unchanged. Read every `read-first` dependency before\nthe task. The supplied analysis contracts carry the operative definitions of\nregistered vocabulary for this job. Their linked definition notes are background,\nnot additional mandatory inputs. Missing required parameters, unavailable required inputs,\nsource access or scope decisions that prevent completion, and needed\nchanges to the selected target or frozen source identity require `problem`.\nDo not reconstruct paths, expand scope or submit a blocked member. A justified\nunknown that only limits a conclusion remains in `output`, naming that conclusion.\nReading another file at the registered Git commit is not a source-boundary\nexpansion. If inspection shows that an explicit functional exclusion or\nboundary kind omits a material shipped responsibility of the selected\ntarget, name the path, responsibility and prevented conclusion in `problem`\ninstead of accepting the exclusion as missing evidence. The verification\njob's instructions govern its disposition of such a defect.\nRetry refusal feedback applies to the same job and does not change its\nanalytical round.\n\nWrite only `output` or `problem`, plus intermediate files in `scratch`.\nWhen `workspace` is supplied, all three destinations are inside that job's\nworkspace. Supplied inputs outside it are read-only. Do not create other files\nin the workspace or write to another job's workspace. Code copies accepted\nresults to coordinator-owned paths for later consumers; do not write those\ncopies yourself. This layout expresses authority but is not a filesystem sandbox.\nThe boundary job may create and freeze sources as its instruction permits.\nDo not edit other run files, publish, delegate, stage or commit, or change\n`workflow-state/`. Only the supplied invocation prompt and `previous-output`\nmay be read there. When finished, reply in one line naming the file\nwritten, without summarizing it. Follow higher-priority runtime requirements\nfor progress messages.\n\n## Commands\n\nRun acceptance commands separately and inspect each exit status, or chain\ndependent commands with `&&`. Shell pipelines need `set -o pipefail`. When\nwrapping tool calls, retain status and stderr as well as stdout; a later\nsuccessful command does not clear an earlier failure. State a check's result\nin your output only after you have run the check.\n\nWith standard Codex tools, return the complete command result, not just\n`result.output`. For example, one bounded read through `functions.exec` is:\n\n```javascript\nconst result = await tools.exec_command({\n  cmd: \"sed -n '1,100p' /absolute/path/from/the/invocation.md\",\n  max_output_tokens: 3000\n});\ntext(result);\n```\n\nReplace the example path with a supplied path or the invocation's batch of\npaths. Read the named job instruction before the Input reading batches.\nFollow those batches in order, completing Read-first before\nTask inputs. Read each batch in one tool call; a batch marked for bounded\nranges requires successive smaller reads until the file's end. Read files\noutside the batch list, including `previous-output` and source searches, in\nbounded ranges. The suggested grouping is a starting budget, not a guarantee:\nlong lines can still overflow it. Inspect the\ncomplete returned object for `exit_code`, errors, and truncation. A running\ncommand has no final exit status yet; wait for its completion. If either\nthe command or outer tool delivery is truncated, repeat that range with a\nsmaller range before advancing. Raising only the inner token limit does\nnot raise the outer delivery limit. Apply the same pattern to source\nsearches and reads. Use one command per call for file preparation and\nacceptance checks, or `&&` when they must share a shell invocation.\n\n## Sources\n\nAnalyse source text and evidence supplied within the frozen boundary.\nDo not execute the target, its tests or examples, call its model providers\nor services, install its dependencies, or create runtime fixtures.\nSource-reading, quotation and Commonplace validation commands remain in\nscope. Missing execution evidence limits conclusions; it does not require\nplanning a check or setting up an environment.\n\nRead evidence only from the sources the supplied `boundary` registers. For\nGit, read and grep the files under `source.path`; that directory holds\nexactly the reviewed commit's files. Listed register paths describe initial\ninspection, not reading permission: other files at that commit may be\ninspected and cited for the selected target. Record additional coverage in\nyour member, following the source contract. You may inspect an excluded\npath to check its relevance; that does not authorize changing functional\nscope. Treat the checkout as read-only: do not fetch,\ncheck out or copy the source elsewhere. For a capture, read the recorded\nfile.\n\n## Check and quotation\n\nBefore submitting, run\n`commonplace-analysis-check <run-state> <job> <output>` with the supplied values.\nWhen `command-path` is supplied, call the command in that directory.\nRepair refusals and rerun until it passes. It shares acceptance checks,\nchanges no output or run state, and logs counts in `scratch`.\nA pass establishes form and occurrence, not the correctness of findings.\n\nCopy each passage into a blockquote ending in `> ---` and a backticked\nsource path. Use full commit-relative Git paths or the registered capture\npath. Omit revisions; the run fixes them. Only attributions carry ranges;\nprose cites paths only. Keep discontiguous passages separate.\n\nFor ambiguity, select by printed context and paste a proposed attribution or\nlengthen the quote. Same-line repeats require lengthening. Never calculate\nranges or revisions. For \"not found\", reread the source and recheck its\nsupporting claim before repair. Judge support yourself.\n\nA quotation exists to show the source passage that supports a finding.\nNarrow or withdraw a finding only when the source cannot support it, never\nto pass the check. List each finding narrowed or withdrawn during check\nrepair, with its reason, in `scratch/check-repairs.md`.\n\n## Prior analyses\n\nDo not call agent listings for status; their payloads may include prior\nanalyses even with a path filter. Do not read style exemplars or\n`kb/agent-memory-systems/`, `kb/agentic-systems/reviews/`,\n`kb/agentic-systems/reviews-archive/`,\n`kb/agentic-systems/reports/`, `kb/agentic-system-analyses/retained/`,\n`kb/agentic-system-analyses/retained-archive/`,\n`kb/work/analyse-agentic-system/`, other runs under\n`kb/agentic-system-analyses/state/`, surveys, comparison outputs, or\nagent listings. If you read prior-review prose or prior audit findings\nthrough any tool, stop and write a problem report saying so; the run cannot\nuse your work.\n"
    },
    "method/sources.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md",
      "sha256": "3c625c95ba41816278a5617d54eb0caf587f4ca2b5183e3170ddbbbe43e0948b",
      "text": "---\ntype: types/note.md\ndescription: \"Shared evidence contract for agentic analyses: evidence layers, reading the Source register, citation anchors and retained quotations\"\n---\n\n# Agentic analysis sources\n\nThis contract defines the frozen evidence used by every worker of an\n`analyse-agentic-system` run. The boundary job writes Boundary and evidence\nand the Source register under the\n[boundary contract](./agentic-analysis-boundary.md); code copies them into\nthe overview. Job invocations declare this file as a required read.\n\nAn excluded host's responsibilities are not attributed to the selected\ntarget.\n\n## Source register\n\nThe register declares each `SRC-*` ID in one row. Source IDs belong to this\nregister alone. A source's durable evidence identity is its registered\nrepository and reviewed commit, or its capture; a worktree path is only the\naccess root. Supplied execution traces and experimental results use frozen\nsource identities and anchors like other evidence; their scope and\nconditions bound the findings.\n\nFor Git, registration permits inspection of the repository at the reviewed\ncommit. The register's inspected paths and citation anchors describe the\nboundary job's initial coverage, not the files later analysts may read.\nInspect and cite newly discovered material files at that same commit,\nrecording the additional coverage in your member and retaining the required\nanchors and quotations. Use the registered repository's source ID, and\nstate the evidence layer appropriate to the passage; discovering a file\ndoes not establish observed operation or causal support. Do not add a source\nID or rewrite the boundary. A different repository, commit or capture is a\nchange to the frozen evidence boundary, not additional file coverage.\n\nThe selected target's functional scope remains binding. Inspection of an\nexcluded path to establish relevance is permitted; if it reveals that a\nmaterial shipped responsibility was excluded or that the boundary kind is\nwrong, identify the path, responsibility and prevented conclusion. Follow\nthe job's problem or verification rules rather than silently treating the\nfile as unavailable or broadening the selected target. Captures permit only\ntheir frozen contents.\n\n| Evidence layer | What it supports |\n|---|---|\n| `implementation` | Inspected executable behavior |\n| `doctrine/design` | Declared intent or contract |\n| `reported operation` | Attributed operation without inspectable run evidence |\n| `observed run` | An inspectable execution trace or artifact |\n| `causal experiment` | An observed intervention and comparison with evidence about the design; design and confounding limits bound attribution |\n\nA source with several layers keeps one register row and one source ID.\nLabel each layer and its inspected scope within that row; do not repeat the\nID or create another ID only to distinguish layers. A contrast is necessary\nbut not sufficient for causal identification; attribution is no finer than the treatment and comparison\nactually performed. A component effect requires independent variation of\nthat component.\n\n## Anchors and quotations\n\nEvery source-dependent finding cites a `SRC-*` ID and a local anchor. For\nGit, the anchor is one code span containing the full commit-relative path,\nsuch as `packages/runtime/src/agent-run.ts`, or a GitHub blob link at the\nreviewed commit. A basename denotes a repository-root file. The path must\nexist at that commit and may be binary. Only quotation attributions carry\nline ranges; prose paths and GitHub links carry no ranges.\n\nLoad-bearing findings, including disputed mechanisms, comparison\nclassifications and assessments, retain minimum verbatim passages. A\nsupplied member's record can supply the passage: cite it rather than\nrepeat it. Parallel analysts may independently retain the same passage;\nreconciliation identifies overlapping support without rewriting members.\n\nWrite each passage as a blockquote ending in a `> ---` attribution:\n\n- Git: `` `commit-relative/path` ``.\n- Capture: `` `registered/capture/path` ``.\n\nThe run fixes the revision or checksum. For ambiguity, paste a checked range\nor lengthen the passage; never calculate a range. Existing pinned GitHub\nURLs and ranged, versioned attributions remain valid.\n\nUnder whitespace normalization, the quote occurs exactly once in the\nfrozen blob or capture, or exactly once within a supplied line range that\ncontains the whole quote. Display line numbers, invented ellipses and\nformatting fences are not quote text; discontiguous passages use separate\nblocks. Occurrence establishes neither semantic support nor coverage.\n\n## Evidence interpretation\n\nPresence, delivery, activation and demonstrated benefit are distinct.\nClaim, affordance, wiring, observation and causal support are distinct.\nCuration alone establishes no warrant. Every negative, thin, conflicting\nor uncertain finding names its inspected boundary and the conclusion it\nprevents. A casual search miss or uninspected branch supports a limitation,\nnot an evidenced absence.\n\nSource-native operation precedes a Commonplace interpretation. The fit and\nany partial or unresolved mapping are stated; ontology never replaces the\noperational account. Omission from an open-ended mechanism list is not\nevidence of absence.\n"
    },
    "method/records.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md",
      "sha256": "2323217d433b8ac4742ea8bfed33e94ab5620909c3a7ce796adfb131911b6b66",
      "text": "---\ntype: types/note.md\ndescription: \"Shared record contract for agentic analyses: identity, annotations, amendments, evidence statuses, record fields and conditional theory assessments\"\n---\n\n# Agentic analysis records\n\nThis contract defines records used across the runtime, memory and\nepistemic members. Their authors, reconciliation and verification receive\nit as a declared dependency. Member types add their own fields and section\nplacement; the [source contract](./agentic-analysis-sources.md) supplies\nevidence layers, anchors and quotations.\n\n## Identity and grammar\n\nThe record kinds are components (`CMP-*`), operative objects (`OBJ-*`),\nroutes (`RTE-*`), claims (`CLM-*`), evidenced absences (`ABS-*`) and\nbehavioral-authority paths (`BAP-*`). `SRC-*` sources belong only to the\nSource register. The analyst that establishes a record declares it in its\nmember, with its permanent prefix: runtime has `RT-` (`RT-OBJ-store`), memory has\n`MEM-` (`MEM-OBJ-store`), epistemic has `EPI-` (`EPI-OBJ-store`).\nThe prefix identifies the declaring analyst, not the member discussing the\nrecord. Keep a supplied ID unchanged in references, annotations and amendments.\n\nNames: one to three lowercase hyphenated words, each starting with a letter;\ndigits may follow. Components/objects use available source-native names.\nDrop extensions; split case changes and separators; lowercase and join.\nAttach digit-only fragments to the preceding word (`GPT-4` → `gpt4`). If\nlonger, keep three distinguishing words. Routes, claims, absences and\nbehavioral-authority paths use two or three words from the label. Qualify\ngeneric names or split parts by source path or role, never a number.\nWithout a source-native name, role, input or label may suggest a handle.\nNames are handles, not conclusions (`validated-knowledge`).\n\nWithin `## Shared records`, kind headings group declarations:\n`### Components`, `### Operative objects`, `### Routes`, `### Claims`,\n`### Evidenced absences`, `### Behavioral-authority paths`. A declaration\nis one level-four heading, `#### RT-OBJ-store — Short label`. Prose, lists and\ntables do not declare records. Member types specify which empty kind\nheadings remain. IDs are unique across the set and resolve within it.\nUse full IDs; aliases are not inferred. Named `through`, dash and\n`to` grouping prose is permitted; code resolves written IDs without expanding\nintervals. Structured citation lists and single-ID fields remain explicit.\nNumbered `SRC-*` ranges are refused; list each source ID. Source quotations\nand fenced excerpts are excluded from identifier checks.\n\nAn annotation, `#### On RT-OBJ-store — Short label`, supplies another analyst's\nfields on a record declared elsewhere. It does not repeat generic identity\nor redefine the referent, and never annotates a record the member declares.\nIts location and permitted fields come from the annotating member's type.\n\nOnly the reconciliation member amends records. An `Amendment:`\nparagraph gives the full ID, superseded value, replacement, evidence anchor\nand affected findings. An anchored conflict retains both values. A\nsupersession uses `Amendment: MEM-RTE-selection-route is superseded by RT-RTE-policy-check`, with\nidentity evidence; both IDs stay declared. A split supersedes the combined\nrecord only by parts already declared in analyst members, for example\n`Amendment: RT-OBJ-output is superseded by EPI-OBJ-store and EPI-OBJ-input`, with identity\nevidence and affected findings. Reconciliation never allocates IDs. Supersede\na combined record only when its findings are wrong once the parts are\nseparated; a valid container can remain alongside its parts. No ID changes referent, and\nno step renames IDs or rewrites another analyst's member. Provisional\nlabels are local tags. Keep IDs fixed through label or finding changes and\ncorrection rounds; never reuse a dropped ID for a different referent.\n\nWhen other members are supplied, a new declaration records its closest\nsupplied full IDs and distinct identity, possible-duplicate evidence, or\nno counterpart after comparison. An existing referent receives an\nannotation rather than another declaration; a different prefix or label\ndoes not establish a distinct referent. Material parts with different\nchecks or consumers are declared and assessed separately. A declaration\nwhose referent is a material part of exactly one supplied record writes\n`Part of: RT-OBJ-store` on its own unindented line within that declaration,\nusing its parent's full record ID. The field carries exactly one ID,\nwithout backticks or other text, and cannot name the declaring record itself.\nLocal and set checks enforce its syntax; existing set resolution checks its\ntarget. Explain a parent of a different record kind beside the relation.\nSemantic verification checks containment; matching kinds alone does not prove it.\nA candidate that can replace a record's referent is not thereby a part of it.\n\nThe part owns its fields and status; the container keeps its identity.\nContainment alone requires neither possible-duplicate evidence nor\nsupersession. A record spanning several supplied records without being part\nof exactly one keeps the distinct-identity comparison, naming each overlap.\n\nWhen a required part is undeclared, reconciliation returns it to the memory\nanalyst if that analyst should declare it and returning is permitted.\nOtherwise retain an `Unresolved conflict:` naming the combined ID, missing\npart, evidence and prevented conclusion. Name the undeclared part in prose,\nwithout inventing an unresolved ID. This adds no runtime or epistemic rewrite\nand no new return round.\n\n## Status fields\n\nConclusion status is exactly one of `absent`, `inapplicable`, `uninspected`,\n`claimed`, `afforded`, `wired`, `observed`, or `causally supported`.\nDifferent layers have separate labelled fields, such as `implementation\nconclusion status: wired` and `operation conclusion status: observed`;\n`wired; observed` is not one value. Guarantee strength is a separate field:\n`invariant`, `protocol`, `policy`, `best effort`, `deployment guarantee`,\nor `no claimed guarantee`. Epistemic architectural status and observed\ncandidate state retain their own vocabularies.\n\nEvery route declaration has at least one conclusion-status field on its\nown line. For example:\n\n```markdown\n- implementation conclusion status: wired\n- operation conclusion status: uninspected\n```\n\nWrite one controlled value only; keep its evidence and\nexplanation outside the value. A field cannot come from another record,\nannotation or source excerpt. Member acceptance checks presence, duplicate\nlayer labels and controlled values; semantic verification judges the choice.\n\n## Record fields\n\n| Kind | Required content |\n|---|---|\n| Component / operative object | Source-native identity, representational form, storage substrate and evidence |\n| Route | Endpoints, progression, owner, context/state/action effects, applicable status fields and evidence |\n| Claim | Claimed operation and its source |\n| Evidenced absence | `absent` status, searched roots or files, query and revision, evidence, and conclusion supported or prevented |\n| Behavioral-authority path | Consumer, channel, force and horizon |\n\n[Representational form](../../notes/definitions/representational-form.md)\nclassifies encoding and consumption, independently of storage. Natural-language\ncontent receives consequences through model or human interpretation. Symbolic\ncontent has localized units and fixed consumer rules determining its permitted\nbehavior. Distributed-parametric content spreads numerical state across weights\nor dense representations. Split mixed objects by operative part or consumption\npath when their evidence, invalidation or rollback differs. Prompt names model\ninput supply, not a fourth form.\n\nEach distributed-parametric component used by inspected routes separately\nstates whether parameters change during operation, whether its exact\nversion is pinned, and whether a mutable endpoint can resolve to another\nmodel. Provider internals remain uninspected when inaccessible. Fixed\nweights do not establish absence of learning through other retained\nmaterial.\n\nEach route records immediate return, later read-back, delegated visibility,\nselection predicate, invalidation or expiry, activation or effect and\nevidence limits. An inapplicable or uninspected field gives a reason.\nRead-back is material accumulated or changed through use that reaches a\nlater consumer invocation; static shipped material and ordinary current-run\nstate are excluded. A summary that compacts or replaces current-run state\nis retained memory when a later invocation receives it, within or across\nruns. Activation requires evidence that delivered material changed behavior.\n\n### Route field syntax\n\nWithin each route declaration, write each of these exact labels once as an\nunindented bullet, with a non-empty answer on the same line:\n\n```markdown\n- Immediate return: ...\n- Later read-back: ...\n- Delegated visibility: ...\n- Selection predicate: ...\n- Invalidation or expiry: ...\n- Activation or effect: ...\n- Evidence limits: ...\n```\n\nReplace every `...` with the finding. When a field is inapplicable or\nuninspected, use exactly `inapplicable — reason` or `uninspected — reason`,\nreplacing `reason` with the explanation. Supporting prose, tables and\nquotations may follow. Another record or an annotation cannot supply these\nfields for the declaration.\n\nLocal member validation and set validation reject missing, empty or duplicate\nfields and uncertainty values without reasons. They check presence and form;\nsemantic verification judges the answers and their evidence. The separate\nstatus-field check above enforces conclusion-status form. Component fixity\nand conditional route fields retain their requirements without new presence\nchecks.\n\n## Conditional route fields\n\nThese requirements apply to the named route classes. Inapplicability is\nnot evidence of an absent epistemic phase.\n\n| Applies to | Required content |\n|---|---|\n| Diagnosis, candidate comparison, admission or successor selection | Who proposes, decides and can veto; separate computational and human contributions; any answer oracle (expected answer or reference outcome, provider, use); operating mode and improvement triggers |\n| Routes admitting product, knowledge/instruction, capability or production-machinery changes | Trigger, proposed change, admission, rejection ability, rollback/recovery; guidance that shaped the proposal, what it says and what persists |\n| Routes involving formulated theories | The theory account and separately evidenced properties below |\n\nModel judgment alone establishes no answer oracle. Operating modes distinguish\nopen requests, bounded experiments/curricula and multiple modes; oracle use\nbelongs to its mode. Routine logs, counters and unchanged checkpoints need\nno separate revision account unless they change later decisions or recovery.\nWrites governed by one admission mechanism may be grouped.\n\n## Theory account\n\nA theory route states the guidance it applies, derives from, criticizes,\nrevises/replaces, and retains or reconstructs. It names what persists:\nformulated theories, criticisms, input/outcome records, parameters or other\nsource-native material. A rule set can express a theory without historical\nrationale; classification depends on its content and exposed assumptions,\nscope conditions and parts, not its storage label. Missing rationale does\nnot establish absent formulated criticism. Retained rationale names its\nlater reader, if any.\n\nEach [theory-builder](../../notes/definitions/theory-builder.md) condition has\nits own conclusion status and evidence under the label\n`theory-builder conditions 1–4`:\n\n| Condition | Evidence required and limits |\n|---|---|\n| 1. Localized content | A theory stated in natural or formal language; its minimum is one whole unit carrying what the theory says. Whole replacement meets this minimum. |\n| 2. Consumption | Decisions depend on what the theory says. Storage, citation and delivery alone do not establish it. |\n| 3. Content-directed criticism | Stated criticism names the challenged claim, result and placement of blame (theory, test, data or auxiliary assumption), and resulting revision or changed reliance. Derivation under an unchanged theory and scores selecting variants do not establish it. A prose contradiction is an interpretation unless codified. A surviving theory can change reliance or test selection without changing text; rejection and replacement count as revision. |\n| 4. Iteration | Criticism's result is kept and shapes a next round, including within one run. Reconstruction from retained criticisms counts; unused critic reports or retained input/outcome records alone do not. A builder ends when another system freezes its product for deployment. |\n\nNo condition is inferred from another. The report distinguishes absent\nformulation or criticism from inaccessible model processing; opacity alone\nestablishes neither presence nor absence. Additional properties are assessed\nseparately:\n\n| Property | Required finding |\n|---|---|\n| [Addressability](../../notes/definitions/addressable-theory.md) | Degree to which assumptions, scope conditions and parts are individually inspectable and revisable, and the assessment boundary; finer grades exceed condition 1's minimum |\n| Persistence | What persists, later consumer and attained horizon: reasoning episode, rounds of one run, runs on one task, or problems/sessions |\n| Learning | Under `learning`: improved capacity for future action, assessment boundary, improvement evidence and attribution evidence. Membership, revision, persistence or connected steps alone do not establish it; capacity need not have been exercised. Later capacity traces persistence to that time; later/recurrent use also traces the retained result and consumer. The memory axis `trace_learning` has its own write-route meaning. |\n| [Reflection](../../notes/definitions/reflective-system.md) | Selected aspects within the boundary, their self-representation and both causal directions: aspect changes can update the representation; representation-mediated operations can affect later behavior. Subject matter alone is insufficient; direct machinery modification is unnecessary. |\n| Reflective theory builder | Its method texts meet conditions 1–4 and are criticized against records of its own operation |\n| Autonomous theory builder | Role-by-role evidence that computation performs every internal operation; users supplying problems and judging products are outside the boundary. Autonomy does not establish reliability. |\n\nLearning, reflection and autonomy remain independent claims. Revision\nselection prefers\n[explanatory-reach](../../notes/first-principles-reasoning-selects-for-explanatory-reach-over.md)\namong revisions that fit the evidence; it does not trade fit away for reach.\nExplanatory-reach means that a criticizable account of why a pattern works\ncontinues to apply beyond its originating case because the mechanism persists.\nVary a load-bearing premise and ask what change the explanation predicts;\ntransfer or local success alone does not establish that account.\n"
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      "text": "---\ntype: types/type-spec.md\nname: agentic-system-analysis-overview\ndescription: \"Entry member of one agentic-system analysis run's retained set: identity, boundary, source register, amendment index, synthesis, limitations and both verifications\"\nschema: ./agentic-system-analysis-overview.schema.yaml\n---\n\n# Agentic system analysis overview\n\nThe reading entry point of one `analyse-agentic-system` run's retained set.\nIt holds identity, evidence boundary, source register, an amendment index,\nsynthesis, limitations and both verifications. Runtime, memory, epistemic\nand reconciliation findings live in their respective members. The sibling\n`ARTIFACT.yaml` selects the [analysis set type](./agentic-system-analysis-set.md)\nand pins every member, including this overview.\n\n## Frontmatter\n\n| Field | Required | Use |\n|---|---:|---|\n| `type` | Yes | `agentic-system-analyses/types/agentic-system-analysis-overview.md` |\n| `description` | Yes | For a `complete` run, the synthesizer's one-sentence retrieval description of the system's mechanism and limits, which the public review also carries; otherwise a code-written description naming the system, selected boundary, and disposition |\n| `run-id` | Yes | Canonical `AAS-YYYY-MM-DD-system-slug-nn` identity allocated by the producing skill |\n| `system` | Yes | Source-native system name or the caller's unambiguous identifier |\n| `run-date` | Yes | Date the run opened |\n| `result-disposition` | Yes | `complete`, `blocked`, or `out-of-scope` |\n| `target-class` | Yes | Target role under the boundary contract; `null` before classification |\n| `boundary-kind` | Yes | Extent of the selected target under the boundary contract; `null` before establishment |\n| `reviewed-boundary` | Yes | Immutable revision or capture identity shared by the run, or `null` before one could be established |\n| `analysis-cutoff` | Yes | Applicability cutoff for the frozen evidence, or `null` before one could be established |\n| `evidence-tier` | Yes | `code-grounded`, `doc-grounded`, or `null` before the runtime baseline could support a tier |\n| `inputs-commit` | Yes | Full repository commit supplying the instructions, contracts, schemas and package code; publication requires the method unchanged |\n\nFor a `complete` run, the five boundary fields are non-null. A `blocked`\nor `out-of-scope` overview retains every required section and states what\nwas not reached, why, and which conclusion that prevents.\n\n## Shared contracts\n\nThe [boundary contract](../instructions/agentic-analysis-boundary.md) defines\nboundary classifications and source declarations. The\n[source contract](../instructions/agentic-analysis-sources.md) defines\nevidence layers, anchors and quotations. The [record contract](../instructions/agentic-analysis-records.md)\ndefines the namespace, declarations, annotations, amendments, fields and\nstatus meanings for all members.\n\n### Identity and completion\n\nThe [set type](./agentic-system-analysis-set.md) owns membership and set\nchecks. File validation checks this overview independently; directory\nvalidation checks the whole set. The manifest pins every member, and\n`inputs-commit` identifies the method's committed inputs.\n\n## Required sections\n\n### Boundary and evidence\n\n`## Boundary and evidence` contains the boundary account defined by the\nboundary contract, copied from the boundary job.\n\n### Source register\n\n`## Source register` contains that job's `SRC-*` rows under the boundary\ncontract. Source identity and evidence scopes remain stable across members.\nFor Git, the identity is the repository at the reviewed commit; listed\npaths record initial coverage and do not prevent later members from\ninspecting and citing other files at that commit for the selected target.\nFor a complete run, code appends `Amended or superseded records: <IDs or none>`\nand a link to `reconciliation.md`. Resolve these IDs through that member\nbefore relying on an analyst's original wording.\n\n### Bounded synthesis\n\n`## Bounded synthesis` gives the evidence basis and boundary, architectural\ncharacterization and claimed work, runtime map, only the discriminating\nmechanisms this target needs, scenario-relative assessment, and concrete\nevidence or system changes that would alter the assessment. Where the\nruntime report supports it, the synthesis states separately whether the\nsystem meets theory-builder conditions 1–4, whether criticism of a consumed\ntheory improved the system's capacity for future action, whether the system\nis reflective or autonomous, and whether it is\n[self-improving](../../notes/definitions/self-improving-system.md) at the\ndeclared boundary, each at its own evidence status; these are independent\nproperties, not a grade or a ladder. It is organized around the system's\noperational progression, not as concatenated analyst reports, and cites\nmember records rather than restating them. It gives no product ranking,\ngeneric adoption advice, system-wide epistemic grade, Commonplace delta, or\ntransfer recommendation. For learning and self-improvement findings it leads\nwith the strongest supported contribution, including partial results, then\nstates the unresolved question, at the level of the comparison actually\nperformed.\n\nThe Bounded synthesis must read without the members' context. State its\nevidence basis and boundary, and link the member records that support it so\na reader can inspect the underlying account.\n\n### Limitations\n\n`## Limitations` contains one row per limitation:\n\n`limitation | affected source, record, or route IDs | inspected boundary | conclusion prevented | evidence that would resolve it`\n\nCarry every `Unresolved conflict:` in the reconciliation into this section.\nA record fault found during synthesis verification is stated here with its\nprevented conclusion; it does not reopen reconciliation. If representing it\nas a limitation would make the synthesis misleading, stop the run.\nUse `none` only after checking the whole set. A blocker is also\nrepresented under Verification and blockers; this section still states\nits analytical consequence.\n\n### Verification and blockers\n\n`## Verification and blockers` contains `### Record verification`,\n`### Profile verification`, `### Synthesis verification`,\n`### Deterministic validation`, and\n`### Blockers`. The three independent checks remain separate: record\nverification covers amendments, supersessions and scope; profile verification\ncovers axis coverage and per-value support against accepted records;\nsynthesis verification covers support for public statements, readability\nwithout the members' context, and unresolved conflicts in Limitations.\nRecord the exact deterministic validation\ntargets and results for every member, and every unresolved blocker. Do\nnot record projection review jobs, publication attempts, or cleanup here.\nA complete run says `none` under blockers; a blocked or out-of-scope run\nstates its stopping condition here. The overview's digest never appears\ninside its own bytes.\n\n## Template\n\n```markdown\n---\ntype: agentic-system-analyses/types/agentic-system-analysis-overview.md\ndescription: \"{one sentence on the system's mechanism and limits}\"\nrun-id: AAS-YYYY-MM-DD-system-slug-nn\nsystem: \"{source-native system name}\"\nrun-date: \"YYYY-MM-DD\"\nresult-disposition: complete\ntarget-class: \"{selected target class}\"\nboundary-kind: whole-system\nreviewed-boundary: \"{immutable revision or capture identity}\"\nanalysis-cutoff: \"YYYY-MM-DD\"\nevidence-tier: code-grounded\ninputs-commit: \"{full commit of this repository at run start}\"\n---\n\n# {System} agentic-system analysis\n\n## Boundary and evidence\n\n## Source register\n\n## Bounded synthesis\n\n## Limitations\n\n## Verification and blockers\n\n### Record verification\n\n### Profile verification\n\n### Synthesis verification\n\n### Deterministic validation\n\n### Blockers\n```\n"
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    "method/synthesize.md": {
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      "text": "---\ndescription: \"Job of an analyse-agentic-system run: write the public synthesis once the reconciled records have passed independent verification\"\ntype: types/instruction.md\n---\n\n# Synthesize the verified records\n\nRead every file under `read-first` in your invocation before any other step.\n\n## Job parameters\n\nCommon parameters are defined in the supplied worker rules.\n\n| Name | Meaning | Present |\n|---|---|---|\n| `round` | `first` or `after-blockers` | Always |\n| `boundary` | Absolute path of the frozen boundary and Source register | Always |\n| `runtime` | Absolute path of the runtime member | Always |\n| `memory` | Absolute path of the accepted memory member | Always |\n| `epistemic` | Absolute path of the epistemic member | Always |\n| `reconciliation` | Absolute path of the settled reconciliation member | Always |\n| `previous-synthesis` | Absolute path of the first synthesis | `after-blockers` |\n| `verification` | Absolute path of the synthesis verification naming blockers | `after-blockers` |\n\n## Task\n\nWrite `output` with exactly these sections:\n\n```markdown\n## Description\n\n## Bounded synthesis\n\n## Limitations\n```\n\nThe supplied overview type governs the synthesis and limitations. Read the\nmembers for their findings and resolve amended or superseded records through\nthe reconciliation. Do not reconcile records, return work to an analyst, or\nwrite verification text. The record loop has already passed independent\nverification.\n\nUnder Description write one sentence of 50 to 250 characters describing the\nsystem's mechanism and limits for retrieval. Code uses it as the description\nof the overview and public review. Bounded synthesis and Limitations become\npublic text unchanged apart from links; they must read without the members'\ncontext. Cite the records supporting each substantive statement rather than\ncopying their classifications. Member links resolve from the retained set\ndirectory, using sibling names such as `runtime.md`.\n\nCarry every paragraph marked `Unresolved conflict:` into a limitation naming\nits affected IDs and prevented conclusion. A newly discovered record fault\nalso becomes a limitation; it does not reopen reconciliation. If a fault\ncannot be stated as a limitation without making the synthesis misleading,\nwrite `problem` and stop.\n\nFor `after-blockers`, read the previous synthesis and verification. Recheck\nevery carried statement against the records and resolve each blocker by\ncorrecting the public text or stating its supported limit. This is the one\nsynthesis correction round. Every cited ID must resolve in the supplied set.\n\nRun the acceptance check before submitting.\n"
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    "method/verify-synthesis.md": {
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      "text": "---\ndescription: \"Job of an analyse-agentic-system run: independently verify public statements and limitations against the settled records\"\ntype: types/instruction.md\n---\n\n# Verify the public synthesis\n\nRead every file under `read-first` in your invocation before any other step.\n\n## Job parameters\n\nCommon parameters are defined in the supplied worker rules.\n\n| Name | Meaning | Present |\n|---|---|---|\n| `synthesis` | Absolute path of the synthesis being judged | Always |\n| `boundary` | Absolute path of the frozen boundary and Source register | Always |\n| `runtime` | Absolute path of the runtime member | Always |\n| `memory` | Absolute path of the accepted memory member | Always |\n| `epistemic` | Absolute path of the epistemic member | Always |\n| `reconciliation` | Absolute path of the settled reconciliation member | Always |\n\n## Task\n\nWrite `output` with exactly these sections:\n\n```markdown\n### Synthesis verification\n\n### Blockers\n```\n\nJudge every substantive synthesis statement against the records it cites,\nincluding amendments and supersessions in the reconciliation. Check that\nDescription, Bounded synthesis and Limitations meet the supplied overview\ntype, read without the members' context, and carry every `Unresolved conflict:`\ninto a limitation with its affected IDs and prevented conclusion. Record the\nchecked claims and limits. Structural acceptance does not establish support.\nUse full IDs; list numbered `SRC-*` references separately.\n\nWrite no correction. Under Blockers write exactly `none`, or a Markdown list\nwith one `- ` entry per blocker, its affected statement and IDs, and what\nwould resolve it. Indent continuation lines. An unsupported statement is a\nblocker; a faithfully stated uncertainty is not. Record faults discovered\nhere must be bounded in the public text; they do not reopen reconciliation.\nIf a fault cannot be stated as a limitation without making the synthesis\nmisleading, write `problem` and stop.\n\nCode sends a blocker list to the synthesizer for one correction round. If\nthe final verification still names blockers, the run stops before publication.\n\nRun the acceptance check before submitting.\n"
    },
    "historical/synthesis-0.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-0.md",
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      "text": "## Description\nDynamic Cheatsheet carries curated text and prior examples into later prompts, with local benchmark checkpoints; the repository shows delivery paths but not model use, memory benefit, or provider-side behavior.\n\n## Bounded synthesis\n\nThis code-grounded account covers Dynamic Cheatsheet's shipped repository at SRC-1 commit `5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9`, including its benchmark driver, prompt routes, retrieval data, result persistence, evaluators, and exposed provider client. It establishes implementation, not a hosted run. The [runtime account](runtime.md) and [epistemic report](epistemic.md) describe the inspected routes and claims; the [memory report](memory.md) details retained material.\n\nIn the cumulative route, a configured model answers a question, then proposes a replacement cheatsheet from the question, raw answer, and prior text. Delimiter extraction accepts tagged text or falls back to the prior text. The benchmark carries the returned string to later eligible prompts and saves it in JSONL; direct callers must pass it themselves. The target-based task evaluator runs after curation and only updates counters. It does not admit, revise, or withdraw the answer or memory. These mechanisms are wired, but the available evidence does not show that curation judgments are correct or that supplied text changes a model response ([RT-RTE-cumulative-loop](runtime.md#rt-rte-cumulative-loop); [MEM-RTE-cumulative-write](memory.md#mem-rte-cumulative-write); [EPI-RTE-benchmark-answer-check](epistemic.md#epi-rte-benchmark-answer-check)).\n\nOther modes expose different context-selection rules. Retrieval-synthesis uses cosine similarity over fixed, precomputed vectors to select prior input/output pairs, asks a model to synthesize context, and supplies that text to the generator. Cumulative retrieval supplies both accumulated text and selected pairs to the generator before updating the text. Direct retrieval supplies selected pairs without consuming its carried cheatsheet field; full-history appends every prior pair. None checks prior outputs for correctness before inclusion. Checkpoint/resume restores saved outputs and the last text value, but the route chosen determines what is read back ([RT-RTE-retrieval-synthesis](runtime.md#rt-rte-retrieval-synthesis); [RT-RTE-cumulative-retrieval](runtime.md#rt-rte-cumulative-retrieval); [RT-RTE-direct-retrieval](runtime.md#rt-rte-direct-retrieval); [RT-RTE-full-history](runtime.md#rt-rte-full-history); [RT-RTE-checkpoint-resume](runtime.md#rt-rte-checkpoint-resume); [MEM-OBJ-example-history](memory.md#mem-obj-example-history); [MEM-OBJ-embedding-table](memory.md#mem-obj-embedding-table)).\n\nThe benchmark has bounded answer-oracle checks for its supported task predicates, but their results come after memory selection and do not feed back into it. They support only those local answer comparisons, not the correctness of reasoning, retained entries, future performance, or generalized learning. The supplied evidence therefore establishes retention and prompt delivery, not activation or improved capacity. It does not establish theory-builder conditions 1–4, learning, reflection, autonomy, or self-improvement at this boundary; the contents of live candidate text and any operation trace needed to assess those properties are unavailable ([EPI-RTE-benchmark-answer-check](epistemic.md#epi-rte-benchmark-answer-check); [EPI-CLM-performance-gains](epistemic.md#epi-clm-performance-gains); [EPI-CLM-zero-shot-learning](epistemic.md#epi-clm-zero-shot-learning)).\n\nFor sequential tasks where previous examples or reusable procedures could help, the implementation offers cumulative text, similarity-selected examples, or full history as prompt context. The evidence cannot determine which route helps, whether the selected examples fit a task, or whether the reported gains are reproducible. A frozen run trace with exact model/provider versions and artifacts, plus controlled comparisons that vary the memory mechanism while holding other conditions fixed, could change that assessment. Evidence of curator decisions and their later effects would be needed to support claims about memory quality or learning. Local Python execution is also wired to model-authored triggers without a source-level sandbox or rollback; provider code execution is only shown as a tool request, with service behavior outside the boundary ([RT-RTE-local-python](runtime.md#rt-rte-local-python); [RT-RTE-provider-code](runtime.md#rt-rte-provider-code)).\n\n## Limitations\n\n| limitation | affected source, record, or route IDs | inspected boundary | conclusion prevented | evidence that would resolve it |\n|---|---|---|---|---|\n| No frozen live provider or benchmark execution trace is supplied; prompt delivery does not establish model use or effect. | SRC-1; RT-CMP-model; RT-RTE-cumulative-loop; RT-RTE-retrieval-synthesis; RT-RTE-cumulative-retrieval; RT-RTE-direct-retrieval; RT-RTE-full-history | Repository implementation, prompt/design text, and tracked artifacts at the registered commit; external provider services and live execution excluded. | Whether a model used the retained material, whether a curator judged content accurately, and whether any route improved answers or future capacity. | Reproducible run traces with exact resolved model versions, inputs, prompts, outputs, and retained-state transitions. |\n| The provenance of shipped vectors and benchmark/result artifacts is not established, and the supplied artifacts do not isolate component effects. | SRC-1; RT-OBJ-retrieval-context; MEM-OBJ-embedding-table; EPI-CLM-performance-gains | Tracked vectors, datasets, results, and notebooks at the frozen repository revision; their generating conditions and independent experimental protocol are unavailable. | Whether retrieved pairs are relevant or valid, whether reported scores reproduce, and whether curation, retrieval, or another component caused a performance change. | Vector and artifact provenance with a reproducible protocol, plus controlled comparisons varying one component at a time. |\n| The benchmark target check runs after curation and only contributes to counters; it does not govern memory admission or rollback. | RT-RTE-cumulative-loop; MEM-RTE-cumulative-write; EPI-RTE-benchmark-answer-check; EPI-CLM-zero-shot-learning | Inspected benchmark dispatch and evaluator code; no separate direct-library oracle or human admission path is wired. | Whether benchmark feedback improves, validates, or withdraws retained memory; whether target-free curation produces improved future capacity. | A traced route in which independently justified answer evidence affects memory decisions, together with follow-up measures of retained-item quality and later task capacity. |\n| Local execution inherits the deployed process permission envelope, while provider execution and cleanup contracts are outside the repository. | RT-RTE-local-python; RT-RTE-provider-code | Inspected subprocess and provider-tool request construction; host configuration and provider services excluded. | The actual isolation, possible side effects, rollback, and safety of execution in a deployment. | A specified and inspected runtime isolation policy, provider execution contract, and trace of execution and cleanup under that deployment. |\n| Checkpoint continuation compares only a subset of settings, and no interruption/resume trace establishes recovery behavior. | RT-OBJ-run-checkpoint; RT-RTE-checkpoint-resume | Benchmark JSONL and argument reload code at SRC-1; no observed continuation, corruption, or concurrent-write case. | Whether a resumed run faithfully reproduces its prior conditions or safely recovers from interruption or damaged state. | Reproducible continuation traces covering changed unchecked settings, interrupted writes, and integrity failures. |\n"
    },
    "historical/synthesis-1.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-1.md",
      "sha256": "1e0cc2aaa9167fa2731dda6bac44457d3c76c73fef7fcb98cbf66de76fb0e5b5",
      "text": "## Description\nDynamic Cheatsheet carries curated text and prior examples into later prompts and benchmark checkpoints; repository code shows delivery paths, not model use, memory benefit, or provider-side behavior.\n\n## Bounded synthesis\n\nThis code-grounded account covers Dynamic Cheatsheet's shipped repository at SRC-1 commit `5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9`, including its benchmark driver, prompt routes, retrieval data, result persistence, evaluators, and exposed provider client. It establishes implementation, not a hosted run. The [runtime account](runtime.md) and [epistemic report](epistemic.md) describe the inspected routes and claims; the [memory report](memory.md) details retained material.\n\nIn the cumulative route, a configured model answers a question, then proposes replacement cheatsheet text from the question, raw answer, and prior text. Delimiter extraction accepts tagged text or falls back to the prior text. The benchmark carries the returned string to later eligible prompts and saves it in JSONL; direct callers must pass it themselves. The target-based task evaluator runs after curation and only updates counters. It does not admit, revise, or withdraw the answer or memory. These mechanisms are wired, but the evidence does not show that curation judgments are correct or that supplied text changes a model response ([RT-RTE-cumulative-loop](runtime.md#rt-rte-cumulative-loop); [MEM-RTE-cumulative-write](memory.md#mem-rte-cumulative-write); [EPI-RTE-benchmark-answer-check](epistemic.md#epi-rte-benchmark-answer-check)).\n\nOther modes use different context-selection rules. Retrieval-synthesis uses cosine similarity over fixed, precomputed vectors to select prior input/output pairs, asks a model to synthesize context, and supplies that text to the generator. Cumulative retrieval supplies accumulated text and selected pairs to the generator before updating the text. Direct retrieval supplies selected pairs without consuming its carried cheatsheet field; full-history appends every prior pair. None checks prior outputs for correctness before inclusion. Checkpoint/resume restores saved outputs and the last text value, but the selected route determines what is read back ([RT-RTE-retrieval-synthesis](runtime.md#rt-rte-retrieval-synthesis); [RT-RTE-cumulative-retrieval](runtime.md#rt-rte-cumulative-retrieval); [RT-RTE-direct-retrieval](runtime.md#rt-rte-direct-retrieval); [RT-RTE-full-history](runtime.md#rt-rte-full-history); [RT-RTE-checkpoint-resume](runtime.md#rt-rte-checkpoint-resume); [MEM-OBJ-example-history](memory.md#mem-obj-example-history); [MEM-OBJ-embedding-table](memory.md#mem-obj-embedding-table)).\n\nThe benchmark has bounded answer-oracle checks for its supported task predicates, but their results come after memory selection and do not feed back into it. They support only those local answer comparisons, not the correctness of reasoning, retained entries, future performance, or generalized learning ([EPI-RTE-benchmark-answer-check](epistemic.md#epi-rte-benchmark-answer-check)).\n\nThe evidence does not establish theory-builder condition 1 (a localized formulated theory), condition 2 (decisions depending on what a theory says), condition 3 (content-directed criticism and resulting revision or changed reliance), or condition 4 (retained criticism shaping a next round). No inspected route or supplied operation trace identifies a formulated theory consumed in decisions or a retained criticism cycle ([RT-RTE-cumulative-loop](runtime.md#rt-rte-cumulative-loop); [MEM-RTE-cumulative-write](memory.md#mem-rte-cumulative-write); [EPI-CLM-zero-shot-learning](epistemic.md#epi-clm-zero-shot-learning)). Criticism of a consumed theory improving future capacity is also not established: there is no identified consumed theory, criticism-to-change trace, or later capacity evidence ([EPI-CLM-zero-shot-learning](epistemic.md#epi-clm-zero-shot-learning); [EPI-CLM-performance-gains](epistemic.md#epi-clm-performance-gains)). Reflection is not established because the inspected records show no self-representation of system operation with both causal directions evidenced. Autonomy is not established: the benchmark caller selects modes and configuration, and the evidence does not show computation performing every internal operation under an autonomous builder role. Self-improvement is not established because no attributable change in future capacity is demonstrated. The implementation establishes retention and prompt delivery only; it does not establish activation or improved capacity ([RT-RTE-cumulative-loop](runtime.md#rt-rte-cumulative-loop); [RT-RTE-checkpoint-resume](runtime.md#rt-rte-checkpoint-resume); [EPI-CLM-performance-gains](epistemic.md#epi-clm-performance-gains); [EPI-CLM-zero-shot-learning](epistemic.md#epi-clm-zero-shot-learning)).\n\nFor sequential tasks where previous examples or reusable procedures could help, the implementation offers cumulative text, similarity-selected examples, or full history as prompt context. The evidence cannot determine which route helps, whether selected examples fit a task, or whether reported gains are reproducible. A frozen run trace with exact model/provider versions and artifacts, plus controlled comparisons that vary the memory mechanism while holding other conditions fixed, could change that assessment. Evidence of curator decisions and their later effects would be needed to support claims about memory quality or learning. Local Python execution is wired to model-authored triggers without a source-level sandbox or rollback; provider code execution is shown only as a tool request, with service behavior outside the boundary ([RT-RTE-local-python](runtime.md#rt-rte-local-python); [RT-RTE-provider-code](runtime.md#rt-rte-provider-code); [EPI-CLM-performance-gains](epistemic.md#epi-clm-performance-gains)).\n\n## Limitations\n\n| limitation | affected source, record, or route IDs | inspected boundary | conclusion prevented | evidence that would resolve it |\n|---|---|---|---|---|\n| No frozen live provider or benchmark execution trace is supplied; prompt delivery does not establish model use or effect. | SRC-1; RT-CMP-model; RT-RTE-cumulative-loop; RT-RTE-retrieval-synthesis; RT-RTE-cumulative-retrieval; RT-RTE-direct-retrieval; RT-RTE-full-history | Repository implementation, prompt/design text, and tracked artifacts at the registered commit; external provider services and live execution excluded. | Whether a model used retained material, whether a curator judged content accurately, and whether any route improved answers or future capacity. | Reproducible run traces with exact resolved model versions, inputs, prompts, outputs, and retained-state transitions. |\n| The provenance of shipped vectors and benchmark/result artifacts is not established, and supplied artifacts do not isolate component effects. | SRC-1; RT-OBJ-retrieval-context; MEM-OBJ-example-history; MEM-OBJ-embedding-table; EPI-CLM-performance-gains | Tracked vectors, datasets, results, and notebooks at the frozen repository revision; generating conditions and independent experimental protocol unavailable. | Whether retrieved pairs are relevant or valid, whether reported scores reproduce, and whether curation, retrieval, or another component caused a performance change. | Vector and artifact provenance with a reproducible protocol, plus controlled comparisons varying one component at a time. |\n| The benchmark target check runs after curation and only contributes to counters; it does not govern memory admission or rollback. | RT-RTE-cumulative-loop; MEM-RTE-cumulative-write; EPI-RTE-benchmark-answer-check; EPI-CLM-zero-shot-learning | Inspected benchmark dispatch and evaluator code; no separate direct-library oracle or human admission path is wired. | Whether benchmark feedback improves, validates, or withdraws retained memory; whether target-free curation produces improved future capacity. | A traced route in which independently justified answer evidence affects memory decisions, together with follow-up measures of retained-item quality and later task capacity. |\n| No formulated theory, theory-directed decision, content-directed criticism cycle, or resulting later capacity change is evidenced; the public evidence also does not establish reflection, autonomy, or self-improvement. | SRC-1; RT-RTE-cumulative-loop; RT-RTE-checkpoint-resume; MEM-RTE-cumulative-write; EPI-CLM-performance-gains; EPI-CLM-zero-shot-learning | Inspected source and supplied records at SRC-1; no live operation trace or capacity comparison supplied. | Theory-builder conditions 1–4; criticism of a consumed theory improving future capacity; reflection; autonomous theory building; and self-improvement at this boundary. | A trace identifying the formulated theory and its consumers, retained criticism and subsequent decision/revision, plus evidence of later capacity; role-level evidence for autonomy and both reflection directions. |\n| Local execution inherits the deployed process permission envelope, while provider execution and cleanup contracts are outside the repository. | RT-RTE-local-python; RT-RTE-provider-code | Inspected subprocess and provider-tool request construction; host configuration and provider services excluded. | Actual isolation, possible side effects, rollback, and execution safety in a deployment. | A specified and inspected runtime isolation policy, provider execution contract, and trace of execution and cleanup under that deployment. |\n| Checkpoint continuation compares only a subset of settings, and no interruption/resume trace establishes recovery behavior. | RT-OBJ-run-checkpoint; RT-RTE-checkpoint-resume | Benchmark JSONL and argument reload code at SRC-1; no observed continuation, corruption, or concurrent-write case. | Whether a resumed run faithfully reproduces prior conditions or safely recovers from interruption or damaged state. | Reproducible continuation traces covering changed unchecked settings, interrupted writes, and integrity failures. |\n"
    },
    "historical/synthesis-verification-0.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-verification-0.md",
      "sha256": "78e5b795743203d4e0ceb879b4e6593f44eb5f13c3057b11dbde517c7559bdc9",
      "text": "### Synthesis verification\n\nThe Description is supported and names the system's retained text, later prompt delivery, and evidence limits. The Bounded synthesis is readable without the member reports: it identifies the repository and commit, states that the evidence is code-grounded implementation rather than hosted operation, summarizes the cumulative and alternate context routes, and links the runtime, memory, and epistemic records. Its substantive implementation claims agree with those records. In particular, the benchmark carries returned text and output history, retrieval ranks prior examples using fixed vectors, the direct-retrieval route does not consume the carried text field, and the benchmark answer check runs after curation without controlling memory admission. The distinction between prompt delivery and demonstrated behavioral effect is preserved. The statements about unavailable provider behavior, provenance, operation, and causal effect are bounded by the supplied evidence.\n\nThe five Limitations rows identify material evidence gaps and their prevented conclusions. The synthesis and reconciliation contain no `Unresolved conflict:` entries, so no conflict is missing from Limitations. Reconciliation amendments are respected: the synthesis cites the canonical `MEM-OBJ-embedding-table` record for vectors and does not rely on superseded epistemic object IDs. The covered IDs and limits are recorded below.\n\nChecked claims and limits:\n\n- `SRC-1`; `RT-CMP-model`; `RT-RTE-cumulative-loop`; `MEM-RTE-cumulative-write`; `EPI-RTE-benchmark-answer-check`; `EPI-CLM-performance-gains`; `EPI-CLM-zero-shot-learning`: the implementation carries text and scores answers after curation, but does not show accurate curation, activation, benefit, or future capacity improvement. No operation trace or controlled comparison supports stronger claims.\n- `RT-RTE-retrieval-synthesis`; `RT-RTE-cumulative-retrieval`; `RT-RTE-direct-retrieval`; `RT-RTE-full-history`; `RT-RTE-checkpoint-resume`; `MEM-OBJ-example-history`; `MEM-OBJ-embedding-table`: the described selection, carry-forward, history, and resume behavior is implementation-grounded. Vector provenance, example validity, and actual effects are not established.\n- `RT-RTE-local-python`; `RT-RTE-provider-code`: execution paths and their limits are accurately described as code wiring. The synthesis does not claim deployed isolation, provider execution, cleanup, or rollback behavior.\n- `RT-OBJ-run-checkpoint`; `RT-OBJ-retrieval-context`; `EPI-OBJ-current-answer`: the listed limitations prevent claims about resumed-run fidelity, artifact provenance, answer correctness beyond local predicates, and causal attribution.\n- The synthesis names conditions 1–4, learning, reflection, autonomy, and self-improvement as not established, but groups these independent assessments into one clause. It does not separately state the status of each theory-builder condition, criticism of consumed theory improving future capacity, reflection, autonomy, and self-improvement as required by the overview contract.\n\n### Blockers\n\n- The sentence beginning “The supplied evidence therefore establishes retention and prompt delivery” groups theory-builder conditions 1–4, learning, reflection, autonomy, and self-improvement into one undifferentiated non-establishment statement; it also does not state separately whether criticism of a consumed theory improved capacity for future action. Affected IDs: `EPI-CLM-performance-gains`, `EPI-CLM-zero-shot-learning`, `RT-RTE-cumulative-loop`, `MEM-RTE-cumulative-write`. Resolve by stating the evidence status of each independent property separately, including the criticism-to-capacity question, or state the applicable evidence limit for each.\n"
    },
    "historical/synthesis-verification-1.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-verification-1.md",
      "sha256": "273e3bd20d7774c738c39026ffd7614ebb48431d7496241931bab524932393bd",
      "text": "### Synthesis verification\n\nChecked the Description against the frozen boundary and accepted members. It correctly identifies retained text and prior examples reaching later prompts, and limits repository evidence to delivery paths rather than model use, benefit, or provider behavior.\n\nChecked the Bounded synthesis as a standalone account. Its cumulative generation and curation sequence, benchmark carry-forward and JSONL resume, mode-specific retrieval and history routes, after-the-fact answer scoring, and local/provider code execution descriptions agree with the runtime and memory records. The distinctions between wired delivery and unobserved activation or benefit are maintained. Its theory-builder conditions 1–4, criticism-related future capacity, reflection, autonomy, and self-improvement statements are appropriately bounded by the epistemic report. The scenario-relative assessment and evidence that could change it are concrete and do not give adoption advice or a system ranking.\n\nOne statement overgeneralizes across the retrieval routes: “None checks prior outputs for correctness before inclusion.” Direct retrieval and full-history have no correctness filter, and cosine ranking is not a correctness test. However, retrieval-synthesis's curator prompt directs the model to evaluate solution effectiveness and retain accurate, reliable strategies. This is prompt-guided assessment, not an independently checked correctness gate; the synthesis must preserve that distinction rather than state that no check occurs. A correction should qualify the statement by route and distinguish prompt guidance from validated checking. Affected records: RT-RTE-retrieval-synthesis, RT-RTE-direct-retrieval, RT-RTE-full-history, EPI-RTE-benchmark-answer-check, EPI-CLM-performance-gains.\n\nChecked Limitations against the evidence boundary and all accepted records. The table identifies absent operation traces, unestablished artifact/vector provenance and causal attribution, downstream-only benchmark checks, unsupported theory-builder and related property conclusions, execution boundaries, and unobserved checkpoint recovery, with prevented conclusions and resolving evidence. Reconciliation amends EPI-OBJ-provider-search's classification and supersedes EPI-OBJ-prior-pairs with MEM-OBJ-example-history and EPI-OBJ-retrieval-vectors with MEM-OBJ-embedding-table; the synthesis does not rely on the superseded identities. Reconciliation contains no `Unresolved conflict:` entries, so there is no such conflict to carry into Limitations.\n\nChecked source references: SRC-1.\n\n### Blockers\n\n- “None checks prior outputs for correctness before inclusion” overstates the evidence for retrieval-synthesis, whose curator prompt requests an assessment of solution effectiveness and accuracy. Qualify the statement by route and state that this is prompt guidance without an independently checked correctness gate. Affected IDs: RT-RTE-retrieval-synthesis, RT-RTE-direct-retrieval, RT-RTE-full-history, EPI-RTE-benchmark-answer-check, EPI-CLM-performance-gains. Resolve by narrowing the claim to the routes with no prompt-guided assessment and describing the retrieval-synthesis assessment as unverified guidance.\n"
    },
    "historical/run-metadata.json": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/run-metadata.json",
      "sha256": "2a6907d2807d88631595684959517ebd5a402a3d5f3e5369fd962f3f181c248f",
      "text": "{\"inputs-commit\": \"6f497a14e4cee76bac1a8571c23ebfec77784314\", \"run-date\": \"2026-10-04\"}\n"
    },
    "historical/source.json": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/source.json",
      "sha256": "0cc8b3d789a90551052945b93e685ad78ecb7928e554db2875f970a10524b637",
      "text": "{\n  \"kind\": \"git\",\n  \"identity\": \"https://github.com/suzgunmirac/dynamic-cheatsheet\",\n  \"revision\": \"5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9\",\n  \"path\": \"/home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/related-systems/suzgunmirac--dynamic-cheatsheet\",\n  \"sha256\": null\n}\n"
    },
    "historical/synthesize-invocation.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/workflow-state/jobs/synthesize/prompt.md",
      "sha256": "8e2d9de7770eeb33e496de6f31a2b6d5c6ef77010ada968083682d8cb3f18a15",
      "text": "Follow /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/synthesize.md with:\nsystem = Dynamic Cheatsheet\nrun-id = AAS-2026-10-04-dynamic-cheatsheet-01\nround = first\nrun-state = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/run-state.md\njob = synthesize\ncommand-path = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/.venv/bin/\nboundary = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\nruntime = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md\nmemory = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md\nepistemic = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md\nreconciliation = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\noutput = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize/synthesis-0.md\nproblem = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize/synthesis-0.problem.md\nworkspace = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize/\nscratch = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize/scratch/\n\nread-first:\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\n## Input reading batches\n\nRead the named job instruction /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/synthesize.md before these reading batches.\n\nLoad inputs in these batches to avoid truncated reads. Use one tool call per batch, return the complete command result, and recover any truncation before continuing. Read oversized files in bounded ranges.\n\nRead-first:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\nTask inputs:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md — read in bounded ranges\n3. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md — read in bounded ranges\n4. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md — read in bounded ranges\n5. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\n\nOversized-file ranges:\nRead each range in a separate tool call. A single oversized line still needs a smaller read if delivery is truncated.\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md: lines 1-215; 216-352\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md: lines 1-196; 197-222\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md: lines 1-227; 228-501; 502-569\n"
    },
    "historical/synthesize-1-invocation.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/workflow-state/jobs/synthesize-1/prompt.md",
      "sha256": "8fc49c6800211e1278dee4fe0173e334ac5e69bd784b2f47f056d220ae2251aa",
      "text": "Follow /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/synthesize.md with:\nsystem = Dynamic Cheatsheet\nrun-id = AAS-2026-10-04-dynamic-cheatsheet-01\nround = after-blockers\nrun-state = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/run-state.md\njob = synthesize-1\ncommand-path = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/.venv/bin/\nboundary = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\nruntime = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md\nmemory = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md\nepistemic = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md\nreconciliation = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\nprevious-synthesis = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-0.md\nverification = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-verification-0.md\noutput = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize-1/synthesis-1.md\nproblem = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize-1/synthesis-1.problem.md\nworkspace = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize-1/\nscratch = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/synthesize-1/scratch/\n\nread-first:\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\n## Input reading batches\n\nRead the named job instruction /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/synthesize.md before these reading batches.\n\nLoad inputs in these batches to avoid truncated reads. Use one tool call per batch, return the complete command result, and recover any truncation before continuing. Read oversized files in bounded ranges.\n\nRead-first:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\nTask inputs:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md — read in bounded ranges\n3. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md — read in bounded ranges\n4. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md — read in bounded ranges\n5. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-0.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-verification-0.md\n\nOversized-file ranges:\nRead each range in a separate tool call. A single oversized line still needs a smaller read if delivery is truncated.\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md: lines 1-215; 216-352\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md: lines 1-196; 197-222\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md: lines 1-227; 228-501; 502-569\n"
    },
    "historical/verify-synthesis-invocation.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/workflow-state/jobs/verify-synthesis/prompt.md",
      "sha256": "d6dce1e8106bfb5a6541e2b863757ca205b9d697a8c6f94add24836822eb103f",
      "text": "Follow /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/verify-synthesis.md with:\nsystem = Dynamic Cheatsheet\nrun-id = AAS-2026-10-04-dynamic-cheatsheet-01\nrun-state = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/run-state.md\njob = verify-synthesis\ncommand-path = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/.venv/bin/\nsynthesis = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-0.md\nboundary = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\nruntime = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md\nmemory = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md\nepistemic = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md\nreconciliation = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\noutput = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis/synthesis-verification-0.md\nproblem = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis/synthesis-verification-0.problem.md\nworkspace = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis/\nscratch = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis/scratch/\n\nread-first:\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\n## Input reading batches\n\nRead the named job instruction /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/verify-synthesis.md before these reading batches.\n\nLoad inputs in these batches to avoid truncated reads. Use one tool call per batch, return the complete command result, and recover any truncation before continuing. Read oversized files in bounded ranges.\n\nRead-first:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\nTask inputs:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-0.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md — read in bounded ranges\n3. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md — read in bounded ranges\n4. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md — read in bounded ranges\n5. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\n\nOversized-file ranges:\nRead each range in a separate tool call. A single oversized line still needs a smaller read if delivery is truncated.\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md: lines 1-215; 216-352\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md: lines 1-196; 197-222\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md: lines 1-227; 228-501; 502-569\n"
    },
    "historical/verify-synthesis-1-invocation.md": {
      "origin": ".commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/workflow-state/jobs/verify-synthesis-1/prompt.md",
      "sha256": "badff1ed6f0628c8452b59aa38df2b43ffb2dfd37087d5289d2570e1dce8ddd0",
      "text": "Follow /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/verify-synthesis.md with:\nsystem = Dynamic Cheatsheet\nrun-id = AAS-2026-10-04-dynamic-cheatsheet-01\nrun-state = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/run-state.md\njob = verify-synthesis-1\ncommand-path = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/.venv/bin/\nsynthesis = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-1.md\nboundary = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\nruntime = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md\nmemory = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md\nepistemic = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md\nreconciliation = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\noutput = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis-1/synthesis-verification-1.md\nproblem = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis-1/synthesis-verification-1.problem.md\nworkspace = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis-1/\nscratch = /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/jobs/verify-synthesis-1/scratch/\n\nread-first:\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\n## Input reading batches\n\nRead the named job instruction /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/verify-synthesis.md before these reading batches.\n\nLoad inputs in these batches to avoid truncated reads. Use one tool call per batch, return the complete command result, and recover any truncation before continuing. Read oversized files in bounded ranges.\n\nRead-first:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/COLLECTION.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/analyse-agentic-system/jobs/worker-rules.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-sources.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/instructions/agentic-analysis-records.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/types/agentic-system-analysis-overview.md\n\nTask inputs:\n1. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/synthesis-1.md, /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/boundary.md\n2. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md — read in bounded ranges\n3. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md — read in bounded ranges\n4. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md — read in bounded ranges\n5. /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/reconciliation.md\n\nOversized-file ranges:\nRead each range in a separate tool call. A single oversized line still needs a smaller read if delivery is truncated.\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/runtime.md: lines 1-215; 216-352\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/memory.md: lines 1-196; 197-222\n- /home/zby/llm/commonplace/.commonplace/worktrees/dynamic-cheatsheet-b0dc01a84b3c/kb/agentic-system-analyses/state/AAS-2026-10-04-dynamic-cheatsheet-01/output/epistemic.md: lines 1-227; 228-501; 502-569\n"
    },
    "source/LICENSE": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:LICENSE",
      "sha256": "a4df4dc6609de8582e24c3bd5d69af28a7e836df9196a0685065bbc18af5ad72",
      "text": "MIT License\n\nCopyright (c) 2025 Mirac Suzgun\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
    },
    "source/README.md": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:README.md",
      "sha256": "0fa0104aca63f2bcb9e25900750c0b0a5364eea79644453528ef7f1b82394394",
      "text": "# Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory\n\n[![arXiv](https://img.shields.io/badge/arXiv-2504.07952-b31b1b.svg)](https://arxiv.org/abs/2504.07952) [![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-sm-dark.svg)](https://arxiv.org/abs/2504.07952)\n\n![Dynamic Cheatsheet Illustration](figures/Illustration.png)\n\n## Dynamic Cheatsheet\n\nA lightweight framework that gives language models (LMs) a persistent, evolving memory during inference time.\n\n### Overview\n\nDynamic Cheatsheet (DC) endows black-box language models with the ability to store and reuse insights across queries. Rather than repeatedly re-discovering solutions or making the same mistakes, DC enables models to accumulate and leverage strategies, code snippets, and problem-solving techniques without modifying the underlying model parameters.\n\n### Key Features\n\n* **Persistent Memory**: Allows LMs to build and reference a growing knowledge base during inference\n* **Self-Curated Storage**: Automatically focuses on concise, transferable snippets rather than entire transcripts\n* **Black-Box Compatible**: Works with any LM without requiring access to model parameters\n* **Zero-Shot Learning**: Improves performance without ground-truth labels or human feedback\n* **Experience-Driven Learning**: Bridges the gap between isolated inference events and cumulative learning\n\n### Performance Improvements\n\n* **Mathematics**: Claude 3.5 Sonnet's accuracy more than doubled on AIME math exams by retaining algebraic insights\n* **Puzzles**: GPT-4o's success rate on Game of 24 increased from approximately 10% to 99% after discovering and reusing Python-based solutions\n* **Arithmetic**: Near-perfect accuracy on tasks like balancing equations (compared to baseline ~50%)\n* **Knowledge-Intensive Tasks**: 9% improvement on GPQA-Diamond and 8% boost on MMLU-Pro Engineering and Physics problems\n\n![Dynamic Cheatsheet Performance](figures/OverallPerformance.png)\n\n\n### Why Use Dynamic Cheatsheet?\n\nUnlike fine-tuning or static retrieval methods, DC adapts LMs' problem-solving skills on the fly, continuously refining responses and reducing routine errors. This approach mimics the cumulative, experience-driven learning characteristic of human cognition, allowing models to learn from their experiences during deployment.\n\n## Installation\n\n### Prerequisites\n\n- Python 3.9+\n\n### Install Dependencies\n\n```bash\npip install tiktoken numpy pandas scikit-learn datasets python-dotenv typed-argument-parser openai\n```\n\nProvider-specific SDKs (install only what you need):\n\n```bash\n# For OpenAI models (gpt-4o, o3-mini, etc.)\npip install openai\n\n# For Anthropic models (Claude)\npip install anthropic\n\n# For Google Gemini models\npip install google-genai\n\n# For Together AI, DeepSeek, Ollama — these use OpenAI-compatible APIs,\n# so only the openai package is needed (already installed above)\n```\n\n### API Key Setup\n\nCreate a `config.env` file in the project root with your API keys:\n\n```bash\n# config.env (this file is git-ignored)\nOPENAI_API_KEY=sk-...\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=...\nXAI_API_KEY=...\nTOGETHER_API_KEY=...\nDEEPSEEK_API_KEY=...\n```\n\nOnly include the keys for providers you plan to use.\n\n## Quick Start\n\n### Basic Usage\n\n```python\nfrom dynamic_cheatsheet.language_model import LanguageModel\n\n# Initialize a model wrapper — supports many providers\nmodel = LanguageModel(model_name=\"openai/gpt-4o\")\n\n# Load prompts\nwith open(\"prompts/generator_prompt.txt\", \"r\") as f:\n    generator_prompt = f.read()\nwith open(\"prompts/curator_prompt_for_dc_cumulative.txt\", \"r\") as f:\n    curator_prompt = f.read()\n\n# Example: Game of 24 puzzle\ninput_txt = \"Let's play a game called 24. You'll be given four integers, and your objective is to use each number only once, combined with any of the four arithmetic operations (addition, subtraction, multiplication, and division) and parentheses, to achieve a total of 24. For example, if the input is 4, 7, 8, and 8, the output could be (7 - (8 / 8)) * 4 = 24. Please present a single expression that evaluates to 24. Question  #1: 5 6 6 8\"\n\n# Generate with Dynamic Cheatsheet\nresults = model.advanced_generate(\n    approach_name=\"DynamicCheatsheet_Cumulative\",\n    input_txt=input_txt,\n    cheatsheet=\"(empty)\",  # Start with an empty cheatsheet\n    generator_template=generator_prompt,\n    cheatsheet_template=curator_prompt,\n)\n\n# Extract results\nprint(f\"Answer: {results['final_answer']}\")\nprint(f\"Updated Cheatsheet: {results['final_cheatsheet']}\")\n\n# Pass the updated cheatsheet to the next query to accumulate knowledge\nnext_results = model.advanced_generate(\n    approach_name=\"DynamicCheatsheet_Cumulative\",\n    input_txt=\"Question #2: 1 3 8 9\",\n    cheatsheet=results['final_cheatsheet'],  # Reuse the cheatsheet\n    generator_template=generator_prompt,\n    cheatsheet_template=curator_prompt,\n)\n```\n\n### Supported Models\n\nThe `model_name` uses `\"provider/model\"` format:\n\n| Provider | Example model names |\n|----------|-------------------|\n| OpenAI | `openai/gpt-4o`, `openai/gpt-4o-mini`, `openai/o3-mini` |\n| Anthropic | `anthropic/claude-sonnet-4-5-20250514`, `anthropic/claude-3-5-sonnet-latest` |\n| Google Gemini | `gemini/gemini-2.5-flash`, `gemini/gemini-2.0-flash` |\n| xAI (Grok) | `xai/grok-3`, `xai/grok-4-1` |\n| Together AI | `together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo`, `together_ai/deepseek-ai/DeepSeek-R1` |\n| DeepSeek | `deepseek/deepseek-chat`, `deepseek/deepseek-reasoner` |\n| Ollama (local) | `ollama/llama3:70b` |\n\n## DC Approaches\n\nWe provide multiple variants of Dynamic Cheatsheet:\n\n| Approach | Description | Requires Embeddings? |\n|----------|-------------|---------------------|\n| `default` | Single LLM call, no cheatsheet (baseline) | No |\n| `DynamicCheatsheet_Cumulative` | Maintains a growing cheatsheet that accumulates knowledge across all queries. Best for sequential problem-solving. | No |\n| `DynamicCheatsheet_RetrievalSynthesis` | Retrieves top-k similar past examples, synthesizes them into a custom cheatsheet per query. Ideal for diverse query sets. | Yes |\n| `DynamicCheatsheet_CumulativeRetrieval` | **Hybrid** — combines a cumulative cheatsheet (general strategies) with retrieval of similar examples (task-specific context). Gets the best of both worlds. | Yes |\n| `FullHistoryAppending` | Appends all previous input-output pairs verbatim (no curation, baseline). | No |\n| `Dynamic_Retrieval` | Retrieves top-k similar examples without synthesis. | Yes |\n\n## Running Benchmarks\n\n### Basic Command\n\n```bash\npython run_benchmark.py \\\n    --task \"GameOf24\" \\\n    --approach_name \"DynamicCheatsheet_Cumulative\" \\\n    --model_name \"openai/gpt-4o-mini\" \\\n    --generator_prompt_path \"prompts/generator_prompt.txt\" \\\n    --cheatsheet_prompt_path \"prompts/curator_prompt_for_dc_cumulative.txt\" \\\n    --max_n_samples 10\n```\n\n### Example: Retrieval-Synthesis on Game of 24\n\n```bash\npython run_benchmark.py \\\n    --task \"GameOf24\" \\\n    --approach_name \"DynamicCheatsheet_RetrievalSynthesis\" \\\n    --model_name \"openai/gpt-4o\" \\\n    --generator_prompt_path \"prompts/generator_prompt.txt\" \\\n    --cheatsheet_prompt_path \"prompts/curator_prompt_for_dc_retrieval_synthesis.txt\" \\\n    --save_directory \"results\" \\\n    --max_n_samples 10\n```\n\n### Example: Hybrid Cumulative+Retrieval on AIME\n\n```bash\npython run_benchmark.py \\\n    --task \"AIME_2025\" \\\n    --approach_name \"DynamicCheatsheet_CumulativeRetrieval\" \\\n    --model_name \"anthropic/claude-sonnet-4-5-20250514\" \\\n    --generator_prompt_path \"prompts/generator_prompt.txt\" \\\n    --cheatsheet_prompt_path \"prompts/curator_prompt_for_dc_cumulative.txt\" \\\n    --max_n_samples 5\n```\n\n### Example: Using Gemini\n\n```bash\npython run_benchmark.py \\\n    --task \"GPQA_Diamond\" \\\n    --approach_name \"DynamicCheatsheet_Cumulative\" \\\n    --model_name \"gemini/gemini-2.5-flash\" \\\n    --generator_prompt_path \"prompts/generator_prompt.txt\" \\\n    --cheatsheet_prompt_path \"prompts/curator_prompt_for_dc_cumulative.txt\" \\\n    --no_shuffle\n```\n\n### CLI Reference\n\n| Argument | Description | Default |\n|----------|-------------|---------|\n| `--task` | Benchmark task: `GameOf24`, `AIME_2025`, `AIME_2024`, `AIME_2020_2024`, `GPQA_Diamond`, `MMLU_Pro_Physics`, `MMLU_Pro_Engineering`, `MathEquationBalancer` | `GameOf24` |\n| `--approach_name` | DC variant (see table above) | `DynamicCheatsheet_Cumulative` |\n| `--model_name` | Model in `provider/model` format | `openai/gpt-4o-mini` |\n| `--generator_prompt_path` | Path to the generator prompt template | `prompts/generator_prompt.txt` |\n| `--cheatsheet_prompt_path` | Path to the curator prompt template | `None` |\n| `--max_tokens` | Max tokens per generation | `2048` |\n| `--temperature` | Sampling temperature | `0.0` |\n| `--max_num_rounds` | Max refinement rounds per query | `1` |\n| `--execute_python_code` | Enable Python code execution in model responses | `True` |\n| `--retrieve_top_k` | Number of similar examples to retrieve | `3` |\n| `--max_n_samples` | Limit number of examples to process (-1 = all) | `-1` |\n| `--no_shuffle` | Disable dataset shuffling | `False` |\n| `--save_directory` | Output directory for results | `results` |\n| `--continue_from_last_run_path` | Path to JSONL to resume from | `None` |\n| `--initialize_cheatsheet_path` | Path to a pre-existing cheatsheet file | `None` |\n\n### Resuming a Run\n\nIf a run is interrupted, you can resume from the last checkpoint:\n\n```bash\npython run_benchmark.py \\\n    --task \"GameOf24\" \\\n    --approach_name \"DynamicCheatsheet_Cumulative\" \\\n    --model_name \"openai/gpt-4o-mini\" \\\n    --generator_prompt_path \"prompts/generator_prompt.txt\" \\\n    --cheatsheet_prompt_path \"prompts/curator_prompt_for_dc_cumulative.txt\" \\\n    --continue_from_last_run_path \"results/GameOf24/openai_gpt-4o-mini_DynamicCheatsheet_Cumulative_2025-01-01-12-00_.jsonl\"\n```\n\n### Example Notebook\n\nFor an interactive demonstration, see `ExampleUsage.ipynb`.\n\n## Citation\n\nIf you make use of our results, codebase, or results in your research or applications, please cite our paper:\n\n```bibtex\n@article{suzgun2025_DynamicCheatsheet,\n      title={Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory},\n      author={Mirac Suzgun and Mert Yuksekgonul and Federico Bianchi and Dan Jurafsky and James Zou},\n      year={2025},\n      eprint={2504.07952},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG},\n      url={https://arxiv.org/abs/2504.07952},\n}\n```\n\nFor more details about the methodology and experimental results, please refer to our paper. You are also more than welcome to reach out to us if you have any questions about our work.\n"
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    "source/dynamic_cheatsheet/__init__.py": {
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    },
    "source/dynamic_cheatsheet/language_model.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:dynamic_cheatsheet/language_model.py",
      "sha256": "2ffd61a587a3effa8bbfa981f302e1a531526a8977cf419ae8c23e4103d4ba35",
      "text": "import os\nimport logging\nimport numpy as np\nimport tiktoken\nfrom typing import List, Dict, Any, Tuple\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom .utils.execute_code import extract_and_run_python_code\nfrom .utils.extractor import extract_answer, extract_cheatsheet\n\nfrom dotenv import load_dotenv\nload_dotenv(\"config.env\")\n\nfrom text_generation import UnifiedLLMClient\n\nlogger = logging.getLogger(__name__)\n\n# Mapping from model_name prefixes to UnifiedLLMClient provider names\n_PROVIDER_MAP = {\n    \"openai\": \"openai\",\n    \"anthropic\": \"claude\",\n    \"claude\": \"claude\",\n    \"gemini\": \"gemini\",\n    \"google\": \"gemini\",\n    \"xai\": \"xai\",\n    \"grok\": \"xai\",\n    # OpenAI-compatible providers (routed through OpenAI client with custom base_url)\n    \"together_ai\": \"openai\",\n    \"together\": \"openai\",\n    \"deepseek\": \"openai\",\n    \"ollama\": \"openai\",\n}\n\n# Configuration for OpenAI-compatible providers\n_OPENAI_COMPATIBLE = {\n    \"together_ai\": {\"base_url\": \"https://api.together.xyz/v1\", \"api_key_env\": \"TOGETHER_API_KEY\"},\n    \"together\": {\"base_url\": \"https://api.together.xyz/v1\", \"api_key_env\": \"TOGETHER_API_KEY\"},\n    \"deepseek\": {\"base_url\": \"https://api.deepseek.com\", \"api_key_env\": \"DEEPSEEK_API_KEY\"},\n    \"ollama\": {\"base_url\": \"http://localhost:11434/v1\", \"api_key_env\": None},\n}\n\n# All supported approach names\nSUPPORTED_APPROACHES = [\n    \"default\",\n    \"DynamicCheatsheet_Cumulative\",\n    \"DynamicCheatsheet_RetrievalSynthesis\",\n    \"DynamicCheatsheet_CumulativeRetrieval\",\n    \"FullHistoryAppending\",\n    \"Dynamic_Retrieval\",\n]\n\n\nclass LanguageModel:\n    def __init__(self,\n        model_name: str,\n        extra_api_params: Dict[str, Any] = None,\n        container_id: str = None,\n    ) -> None:\n        \"\"\"\n        LanguageModel class to interact with different language models.\n\n        Uses UnifiedLLMClient to support OpenAI, Anthropic/Claude, Google Gemini,\n        xAI/Grok, and any OpenAI-compatible provider (Together AI, DeepSeek, Ollama, etc.).\n\n        Model name format: \"provider/model\" (e.g., \"openai/gpt-4o\", \"anthropic/claude-sonnet-4-5-20250514\",\n        \"gemini/gemini-2.5-flash\", \"together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo\").\n\n        Arguments:\n            model_name : str : The name of the language model in \"provider/model\" format.\n            extra_api_params : Dict[str, Any] : Additional provider-specific API parameters\n                that are forwarded to every generate call (e.g., {\"reasoning\": {\"effort\": \"medium\"}}\n                for OpenAI reasoning models).\n\n        Raises:\n            ValueError : If the provider cannot be determined from the model name.\n        \"\"\"\n        self.model_name = model_name\n\n        # Parse provider and model from model_name\n        provider_prefix, model_id = self._parse_model_name(model_name)\n        self.provider_prefix = provider_prefix\n        self.model_id = model_id\n\n        # Build kwargs for UnifiedLLMClient\n        provider = _PROVIDER_MAP.get(provider_prefix)\n        if provider is None:\n            raise ValueError(\n                f\"Unknown provider prefix '{provider_prefix}' in model name '{model_name}'. \"\n                f\"Supported prefixes: {', '.join(sorted(_PROVIDER_MAP.keys()))}\"\n            )\n\n        client_kwargs = {\"provider\": provider, \"model\": model_id, \"max_retries\": 3, \"retry_delay\": 10}\n\n        # Handle OpenAI-compatible providers (custom base_url and api_key)\n        if provider_prefix in _OPENAI_COMPATIBLE:\n            config = _OPENAI_COMPATIBLE[provider_prefix]\n            client_kwargs[\"base_url\"] = config[\"base_url\"]\n            if config[\"api_key_env\"]:\n                api_key = os.environ.get(config[\"api_key_env\"])\n                if api_key:\n                    client_kwargs[\"api_key\"] = api_key\n                else:\n                    raise ValueError(\n                        f\"API key not found. Set the {config['api_key_env']} environment variable \"\n                        f\"or add it to config.env.\"\n                    )\n            else:\n                # No API key needed (e.g., Ollama) — pass a dummy key\n                client_kwargs[\"api_key\"] = \"no-key-needed\"\n\n        self.extra_api_params = extra_api_params or {}\n        self.container_id = container_id\n        self.client = UnifiedLLMClient(**client_kwargs)\n        self.gpt4Tokenizer = tiktoken.encoding_for_model('gpt-4o')\n\n    @staticmethod\n    def _parse_model_name(model_name: str) -> Tuple[str, str]:\n        \"\"\"\n        Parse \"provider/model\" format. For providers like together_ai where the\n        model part itself contains slashes (e.g., \"together_ai/meta-llama/Llama-3.3-70B\"),\n        only the first segment is treated as the provider prefix.\n\n        If no \"/\" is present, the provider is inferred from the model name.\n\n        Returns:\n            (provider_prefix, model_id)\n        \"\"\"\n        if \"/\" in model_name:\n            prefix, rest = model_name.split(\"/\", 1)\n            return prefix, rest\n\n        # Infer provider from model name when no prefix is given\n        if model_name.startswith((\"gpt-\", \"o1\", \"o3\", \"o4\")):\n            return \"openai\", model_name\n        elif model_name.startswith(\"claude-\"):\n            return \"anthropic\", model_name\n        elif model_name.startswith(\"gemini-\"):\n            return \"gemini\", model_name\n        elif model_name.startswith(\"grok-\"):\n            return \"xai\", model_name\n        else:\n            raise ValueError(\n                f\"Cannot infer provider from model name '{model_name}'. \"\n                f\"Use 'provider/model' format (e.g., 'openai/gpt-4o').\"\n            )\n\n    def count_tokens(self, text: str) -> int:\n        \"\"\"\n        Count the number of tokens in the text using the GPT-4o tokenizer (approximate).\n        \"\"\"\n        tokens = self.gpt4Tokenizer.encode(text)\n        return len(tokens)\n\n    # Sentinel value used when a provider handles code execution natively\n    # without requiring a container (e.g. Claude's code_execution tool).\n    _NATIVE_CODE_EXECUTION_ID = \"native\"\n\n    def create_container(self, **kwargs) -> str:\n        \"\"\"\n        Create a code interpreter session and return an identifier.\n\n        For OpenAI models this creates a server-side container via the Responses API.\n        For Claude models no container is needed — the code execution tool is\n        enabled per-request, so a sentinel ID is returned immediately.\n        \"\"\"\n        if self.provider_prefix in (\"claude\", \"anthropic\"):\n            self.container_id = self._NATIVE_CODE_EXECUTION_ID\n            logger.info(\"Claude native code execution enabled (no container required).\")\n            return self.container_id\n\n        container_id = self.client.create_container(**kwargs)\n        self.container_id = container_id\n        return container_id\n\n    def generate(self,\n        history: List[Dict[str, str]],\n        temperature: float = 0.1,\n        max_tokens: int = 2048,\n        current_depth: int = 1,\n        max_depth_num_rounds: int = 3,\n        allow_code_execution: bool = True,\n        code_execution_flag: str = \"EXECUTE CODE!\",\n        final_output: str = \"\",\n        use_code_interpreter: bool = False,\n    ) -> str:\n        \"\"\"\n        Generate a response from the language model, with optional iterative code execution.\n\n        When allow_code_execution is True and the model produces a Python code block\n        followed by the code_execution_flag, the code is executed and its output is\n        appended to the conversation. The model is then prompted to continue, up to\n        max_depth_num_rounds iterations.\n\n        Arguments:\n            history : List[Dict[str, str]] : The conversation history (list of message dicts).\n            temperature : float : The sampling temperature for the model.\n            max_tokens : int : The maximum number of tokens to generate.\n            current_depth : int : The current iteration depth (for recursive code execution).\n            max_depth_num_rounds : int : The maximum number of code execution rounds allowed.\n            allow_code_execution : bool : Whether to allow Python code execution.\n            code_execution_flag : str : The trigger string for code execution.\n            final_output : str : Accumulated output across recursive calls.\n\n        Returns:\n            str : The final accumulated output.\n\n        Raises:\n            ValueError : If the history is empty.\n        \"\"\"\n        if len(history) == 0:\n            raise ValueError(\"History must contain at least one message.\")\n\n        # Code interpreter path: handled server-side by the provider\n        if use_code_interpreter and self.container_id:\n            if self.provider_prefix in (\"claude\", \"anthropic\"):\n                # Claude: native code execution tool (no container required)\n                output = self.client.generate_with_code_interpreter_claude(\n                    messages=history,\n                    temperature=temperature,\n                    max_completion_tokens=max_tokens,\n                    **self.extra_api_params,\n                )\n            else:\n                # OpenAI: Responses API with a persistent container\n                output = self.client.generate_with_code_interpreter(\n                    messages=history,\n                    container_id=self.container_id,\n                    temperature=temperature,\n                    max_completion_tokens=max_tokens,\n                    **self.extra_api_params,\n                )\n            if not output:\n                logger.warning(\"Received empty response from code interpreter.\")\n                output = \"(No response generated)\"\n            return f\"{final_output}\\n\\n{output}\".strip() if final_output else output\n\n        # Generate the response from the language model\n        output = self.client.generate_from_messages(\n            messages=history,\n            temperature=temperature,\n            max_completion_tokens=max_tokens,\n            **self.extra_api_params,\n        )\n\n        # Guard against empty or None responses\n        if not output:\n            logger.warning(\"Received empty response from the language model.\")\n            output = \"(No response generated)\"\n\n        # Check if the model produced a code block followed by the execution flag\n        if allow_code_execution and code_execution_flag in output:\n            pre_flag_text = output.split(code_execution_flag)[0].strip()\n            # Only execute if there's a code block ending right before the flag\n            if len(pre_flag_text) >= 3 and pre_flag_text.endswith(\"```\"):\n                output_prefix = pre_flag_text\n                executed_code = extract_and_run_python_code(output_prefix)\n                if executed_code:\n                    executed_code = executed_code.strip()\n                else:\n                    executed_code = \"(No code block found to execute)\"\n                current_output = f\"{output_prefix}\\n{code_execution_flag}\\n\\n{executed_code}\"\n                final_output = f\"{final_output}\\n\\n{current_output}\".strip()\n\n                # If we haven't exceeded the max depth, prompt the model to continue\n                if current_depth <= max_depth_num_rounds:\n                    warning_txt = \"\"\n                    if current_depth == max_depth_num_rounds:\n                        warning_txt = \" (This is the last round. No more code execution will be allowed. Please present your final solution now.)\"\n                    new_messages = [\n                        {\"role\": \"assistant\", \"content\": current_output},\n                        {\"role\": \"user\", \"content\": f\"Proceed with any additional steps required and provide the completed solution. If everything is already complete, type FINAL ANSWER and submit it in the expected format. If you are stuck, please try alternative methods to solve the problem and provide the final solution.{warning_txt}\"}\n                    ]\n                    history += new_messages\n                    return self.generate(\n                        history=history,\n                        temperature=temperature,\n                        max_tokens=max_tokens,\n                        current_depth=current_depth + 1,\n                        max_depth_num_rounds=max_depth_num_rounds,\n                        allow_code_execution=allow_code_execution,\n                        code_execution_flag=code_execution_flag,\n                        final_output=final_output,\n                    )\n                else:\n                    return f\"{final_output}\\n\\n{current_output}\".strip()\n\n        # No code execution — just append output\n        final_output = f\"{final_output}\\n\\n{output}\".strip()\n        return final_output\n\n    def advanced_generate(self,\n        approach_name: str,\n        input_txt: str,\n        cheatsheet: str = None,\n        generator_template: str = None,\n        cheatsheet_template: str = None,\n        temperature: float = 0.0,\n        max_tokens: int = 2048,\n        max_num_rounds: int = 1,\n        allow_code_execution: bool = True,\n        code_execution_flag: str = \"EXECUTE CODE!\",\n        add_previous_answers_to_cheatsheet: bool = True,\n        original_input_corpus: List[str] = None,\n        original_input_embeddings: np.ndarray = None,\n        generator_outputs_so_far: List[str] = None,\n        retrieve_top_k: int = 3,\n        use_code_interpreter: bool = False,\n    ) -> Dict[str, Any]:\n        \"\"\"\n        Generate a response using one of the supported Dynamic Cheatsheet approaches.\n\n        Supported approaches:\n            - \"default\": Single LLM call, no cheatsheet.\n            - \"DynamicCheatsheet_Cumulative\": Maintains and grows a persistent cheatsheet\n              across queries. After each answer, a curator call updates the cheatsheet.\n            - \"DynamicCheatsheet_RetrievalSynthesis\": Retrieves top-k similar past examples,\n              synthesizes them into a custom cheatsheet, then generates an answer.\n            - \"DynamicCheatsheet_CumulativeRetrieval\": Hybrid — maintains a cumulative cheatsheet\n              AND retrieves top-k similar examples. The generator receives both general strategies\n              (from the cumulative cheatsheet) and specific examples (from retrieval).\n            - \"FullHistoryAppending\": Appends all previous input-output pairs (no curation).\n            - \"Dynamic_Retrieval\": Retrieves top-k similar examples without synthesis.\n\n        Arguments:\n            approach_name : str : The name of the approach to use.\n            input_txt : str : The input text for the model.\n            cheatsheet : str : The current cheatsheet content.\n            generator_template : str : The template for the generator model.\n            cheatsheet_template : str : The template for the cheatsheet curator model.\n            temperature : float : The sampling temperature for the model.\n            max_tokens : int : The maximum number of tokens to generate.\n            max_num_rounds : int : The maximum number of refinement rounds.\n            allow_code_execution : bool : Whether to allow Python code execution.\n            code_execution_flag : str : The flag to trigger code execution.\n            add_previous_answers_to_cheatsheet : bool : Whether to include previous answers in the cheatsheet.\n            original_input_corpus : List[str] : The original input texts (for retrieval-based approaches).\n            original_input_embeddings : np.ndarray : Pre-computed embeddings (for retrieval-based approaches).\n            generator_outputs_so_far : List[str] : Previous generator outputs.\n            retrieve_top_k : int : Number of similar examples to retrieve.\n\n        Returns:\n            Dict containing: input_txt, steps, final_answer, final_output, final_cheatsheet, etc.\n\n        Raises:\n            ValueError : If the approach name is not recognized or required templates are missing.\n        \"\"\"\n\n        # When using code interpreter, disable local subprocess execution\n        if use_code_interpreter:\n            allow_code_execution = False\n\n        if approach_name == \"default\":\n            generator_prompt = generator_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[CHEATSHEET]]\", \"(empty)\")\n            generator_history = [\n                {\"role\": \"user\", \"content\": generator_prompt},\n            ]\n            generator_output = self.generate(\n                history=generator_history,\n                temperature=temperature,\n                max_tokens=max_tokens,\n                allow_code_execution=allow_code_execution,\n                code_execution_flag=code_execution_flag,\n                use_code_interpreter=use_code_interpreter,\n            )\n\n            generator_answer = extract_answer(generator_output)\n\n            return {\n                \"input_txt\": input_txt,\n                \"steps\": [\n                    {\n                        \"round\": 0,\n                        \"generator_prompt\": generator_prompt,\n                        \"generator_output\": generator_output,\n                        \"generator_answer\": generator_answer,\n                        \"current_cheatsheet\": None,\n                        \"new_cheatsheet\": None,\n                    }\n                ],\n                \"previous_answers\": None,\n                \"final_answer\": generator_answer,\n                \"final_output\": generator_output,\n                \"final_cheatsheet\": None,\n                \"generator_output\": generator_output,\n            }\n\n        elif approach_name == \"DynamicCheatsheet_Cumulative\":\n            if cheatsheet is None:\n                raise ValueError(\"Cheatsheet must be provided for DynamicCheatsheet_Cumulative approach.\")\n            if cheatsheet_template is None:\n                raise ValueError(\"Cheatsheet template must be provided for DynamicCheatsheet_Cumulative approach.\")\n\n            steps = []\n            previous_answers = []\n            generator_output = ''\n\n            for round_idx in range(max(1, max_num_rounds)):\n                ## STEP 1: Run the generator model with the input text and the cheatsheet\n                generator_cheatsheet_content = cheatsheet\n\n                # If there are previous answers, add them to the cheatsheet content for the generator\n                if round_idx > 0 and add_previous_answers_to_cheatsheet:\n                    previous_answers_txt = f\"PREVIOUS ANSWERS:\\n{'; '.join(previous_answers)}\"\n                    generator_cheatsheet_content = f\"{generator_cheatsheet_content}\\n\\n{previous_answers_txt}\"\n\n                generator_prompt = generator_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[CHEATSHEET]]\", generator_cheatsheet_content)\n                current_cheatsheet = cheatsheet\n\n                # Prepare the message history for the generator model\n                generator_history = [{\"role\": \"user\", \"content\": generator_prompt}]\n                # Run the generator model\n                generator_output = self.generate(\n                    history=generator_history,\n                    temperature=temperature,\n                    max_tokens=max_tokens,\n                    allow_code_execution=allow_code_execution,\n                    code_execution_flag=code_execution_flag,\n                    use_code_interpreter=use_code_interpreter,\n                )\n                # Extract the output from the generator model\n                generator_answer = extract_answer(generator_output)\n\n                ## STEP 2: Run the cheatsheet extraction model with the generator output and the current cheatsheet\n                cheatsheet_prompt = cheatsheet_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[MODEL_ANSWER]]\", generator_output).replace(\"[[PREVIOUS_CHEATSHEET]]\", current_cheatsheet)\n\n                cheatsheet_history = [{\"role\": \"user\", \"content\": cheatsheet_prompt}]\n                cheatsheet_output = self.generate(\n                    history=cheatsheet_history,\n                    temperature=temperature,\n                    max_tokens=2 * max_tokens,\n                    allow_code_execution=False,\n                )\n\n                # Extract the new cheatsheet from the output (if present); otherwise, return the old cheatsheet\n                new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=current_cheatsheet)\n                cheatsheet = new_cheatsheet\n\n                previous_answers.append(f\"Round {round_idx+1}: {generator_answer}\")\n\n                steps.append({\n                    \"round\": round_idx,\n                    \"generator_prompt\": generator_prompt,\n                    \"generator_output\": generator_output,\n                    \"generator_answer\": generator_answer,\n                    \"current_cheatsheet\": current_cheatsheet,\n                    \"new_cheatsheet\": new_cheatsheet,\n                })\n\n            return {\n                \"input_txt\": input_txt,\n                \"steps\": steps,\n                \"previous_answers\": previous_answers,\n                \"final_answer\": generator_answer,\n                \"final_cheatsheet\": new_cheatsheet,\n                \"final_output\": generator_output,\n            }\n\n        elif approach_name == \"FullHistoryAppending\":\n            length_of_history = len(generator_outputs_so_far)\n            if length_of_history > 0:\n                top_k_original_inputs = original_input_corpus[:length_of_history]\n                top_k_original_outputs = generator_outputs_so_far\n\n                curated_cheatsheet = \"### PREVIOUS SOLUTIONS (START)\\n\\n\"\n                for i, (previous_input_txt, previous_output_txt) in enumerate(zip(original_input_corpus, generator_outputs_so_far)):\n                    curated_cheatsheet += f\"#### Previous Input #{i+1}:\\n\\n{previous_input_txt}\\n\\n#### Model Solution to Previous Input #{i+1}:\\n\\n{previous_output_txt}\\n---\\n---\\n\\n\"\n                curated_cheatsheet += \"#### PREVIOUS SOLUTIONS (END)\"\n            else:\n                top_k_original_inputs = []\n                top_k_original_outputs = []\n                curated_cheatsheet = \"(empty)\"\n\n            generator_prompt = generator_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[CHEATSHEET]]\", curated_cheatsheet)\n            generator_history = [{\"role\": \"user\", \"content\": generator_prompt}]\n            generator_output = self.generate(\n                history=generator_history,\n                temperature=temperature,\n                max_tokens=max_tokens,\n                allow_code_execution=allow_code_execution,\n                code_execution_flag=code_execution_flag,\n                use_code_interpreter=use_code_interpreter,\n            )\n            generator_answer = extract_answer(generator_output)\n\n            return {\n                \"input_txt\": input_txt,\n                \"steps\": [\n                    {\n                        \"round\": 0,\n                        \"generator_prompt\": generator_prompt,\n                        \"generator_output\": generator_output,\n                        \"generator_answer\": generator_answer,\n                        \"current_cheatsheet\": curated_cheatsheet,\n                        \"new_cheatsheet\": None,\n                    }\n                ],\n                \"top_k_original_inputs\": top_k_original_inputs,\n                \"top_k_original_outputs\": top_k_original_outputs,\n                \"final_answer\": generator_answer,\n                \"final_output\": generator_output,\n                \"final_cheatsheet\": curated_cheatsheet,\n            }\n\n        elif approach_name in [\"Dynamic_Retrieval\", \"DynamicCheatsheet_RetrievalSynthesis\"]:\n            # Get the current original input embedding\n            current_original_input_embedding = original_input_embeddings[-1]\n            prev_original_input_embeddings = original_input_embeddings[:-1]\n\n            # Retrieve the most similar k input-output pairs from the previous inputs and outputs\n            if len(prev_original_input_embeddings) > 0:\n                similarities = cosine_similarity([current_original_input_embedding], prev_original_input_embeddings)\n                top_k_indices = np.argsort(similarities[0])[::-1][:retrieve_top_k]\n                top_k_original_inputs = [original_input_corpus[i] for i in top_k_indices]\n                top_k_original_outputs = [generator_outputs_so_far[i] for i in top_k_indices]\n                top_k_similar_values = similarities[0][top_k_indices]\n                curated_cheatsheet = \"### PREVIOUS SOLUTIONS (START)\\n\\nNote: The input-output pairs listed below are taken from previous test cases and are meant to assist you in understanding potential solution strategies or tool usages. While they can offer insight and inspiration, they should not be blindly copied, as they may contain errors or may not fit your specific use case. Approach them with a critical mindset—analyze their logic, verify their correctness, and adapt them as needed. Your goal should be to develop a well-reasoned solution that best addresses the problem at hand.\\n\\n\"\n            else:\n                top_k_original_inputs = []\n                top_k_original_outputs = []\n                top_k_similar_values = []\n                curated_cheatsheet = '(empty)'\n\n            # Add the previous input-output pairs to the cheatsheet (reverse order so most similar is last/closest to the question)\n            for i, (previous_input_txt, previous_output_txt, similarity) in enumerate(zip(top_k_original_inputs[::-1], top_k_original_outputs[::-1], top_k_similar_values[::-1])):\n                curated_cheatsheet += f\"#### Previous Input #{i+1} (Similarity: {similarity:.2f}):\\n\\n{previous_input_txt}\\n\\n#### Model Solution to Previous Input  #{i+1}:\\n\\n{previous_output_txt}\\n---\\n---\\n\\n\"\n            curated_cheatsheet = curated_cheatsheet.strip()\n\n            if curated_cheatsheet != '(empty)':\n                curated_cheatsheet += \"\\n\\n#### PREVIOUS SOLUTIONS (END)\"\n\n            # For RetrievalSynthesis, run the curator to synthesize a better cheatsheet\n            previous_cheatsheet = cheatsheet\n            if approach_name == \"DynamicCheatsheet_RetrievalSynthesis\":\n                cheatsheet_prompt = cheatsheet_template.replace(\"[[PREVIOUS_INPUT_OUTPUT_PAIRS]]\", curated_cheatsheet)\n                cheatsheet_prompt = cheatsheet_prompt.replace(\"[[NEXT_INPUT]]\", input_txt)\n                cheatsheet_prompt = cheatsheet_prompt.replace(\"[[PREVIOUS_CHEATSHEET]]\", previous_cheatsheet)\n                cheatsheet_history = [{\"role\": \"user\", \"content\": cheatsheet_prompt}]\n                cheatsheet_output = self.generate(\n                    history=cheatsheet_history,\n                    temperature=temperature,\n                    max_tokens=2 * max_tokens,\n                    allow_code_execution=False,\n                )\n                new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=curated_cheatsheet)\n                curated_cheatsheet = new_cheatsheet\n\n            generator_prompt = generator_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[CHEATSHEET]]\", curated_cheatsheet)\n            generator_history = [{\"role\": \"user\", \"content\": generator_prompt}]\n            generator_output = self.generate(\n                history=generator_history,\n                temperature=temperature,\n                max_tokens=max_tokens,\n                allow_code_execution=allow_code_execution,\n                code_execution_flag=code_execution_flag,\n                use_code_interpreter=use_code_interpreter,\n            )\n            generator_answer = extract_answer(generator_output)\n\n            return {\n                \"input_txt\": input_txt,\n                \"steps\": [\n                    {\n                        \"round\": 0,\n                        \"generator_prompt\": generator_prompt,\n                        \"generator_output\": generator_output,\n                        \"generator_answer\": generator_answer,\n                        \"current_cheatsheet\": curated_cheatsheet,\n                        \"new_cheatsheet\": None,\n                    }\n                ],\n                \"top_k_original_inputs\": top_k_original_inputs,\n                \"top_k_original_outputs\": top_k_original_outputs,\n                \"final_answer\": generator_answer,\n                \"final_output\": generator_output,\n                \"final_cheatsheet\": curated_cheatsheet,\n            }\n\n        elif approach_name == \"DynamicCheatsheet_CumulativeRetrieval\":\n            # Hybrid approach: cumulative cheatsheet + retrieval of similar examples.\n            # The generator receives BOTH the evolving cheatsheet (general strategies)\n            # AND the top-k most similar previous examples (task-specific context).\n            # After answering, the curator updates the cumulative cheatsheet.\n\n            if cheatsheet is None:\n                raise ValueError(\"Cheatsheet must be provided for DynamicCheatsheet_CumulativeRetrieval approach.\")\n            if cheatsheet_template is None:\n                raise ValueError(\"Cheatsheet template must be provided for DynamicCheatsheet_CumulativeRetrieval approach.\")\n\n            # --- Retrieval step: find similar previous examples ---\n            retrieved_section = \"\"\n            top_k_original_inputs = []\n            top_k_original_outputs = []\n\n            if original_input_embeddings is not None and len(original_input_embeddings) > 1:\n                current_embedding = original_input_embeddings[-1]\n                prev_embeddings = original_input_embeddings[:-1]\n\n                similarities = cosine_similarity([current_embedding], prev_embeddings)\n                top_k_indices = np.argsort(similarities[0])[::-1][:retrieve_top_k]\n                top_k_original_inputs = [original_input_corpus[i] for i in top_k_indices]\n                top_k_original_outputs = [generator_outputs_so_far[i] for i in top_k_indices]\n                top_k_similar_values = similarities[0][top_k_indices]\n\n                retrieved_section = \"\\n\\n### RETRIEVED SIMILAR EXAMPLES (START)\\n\\nNote: These are the most similar previous problems and their solutions. Use them as reference, but verify correctness and adapt as needed.\\n\\n\"\n                for i, (prev_input, prev_output, sim) in enumerate(zip(\n                    top_k_original_inputs[::-1], top_k_original_outputs[::-1], top_k_similar_values[::-1]\n                )):\n                    retrieved_section += f\"#### Similar Example #{i+1} (Similarity: {sim:.2f}):\\n\\n{prev_input}\\n\\n#### Solution:\\n\\n{prev_output}\\n---\\n---\\n\\n\"\n                retrieved_section += \"### RETRIEVED SIMILAR EXAMPLES (END)\"\n\n            # --- Build combined cheatsheet for the generator ---\n            combined_cheatsheet = cheatsheet\n            if retrieved_section:\n                combined_cheatsheet = f\"{cheatsheet}\\n{retrieved_section}\"\n\n            # --- Generator step ---\n            generator_prompt = generator_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[CHEATSHEET]]\", combined_cheatsheet)\n            current_cheatsheet = cheatsheet\n\n            generator_history = [{\"role\": \"user\", \"content\": generator_prompt}]\n            generator_output = self.generate(\n                history=generator_history,\n                temperature=temperature,\n                max_tokens=max_tokens,\n                allow_code_execution=allow_code_execution,\n                code_execution_flag=code_execution_flag,\n                use_code_interpreter=use_code_interpreter,\n            )\n            generator_answer = extract_answer(generator_output)\n\n            # --- Curator step: update the cumulative cheatsheet ---\n            cheatsheet_prompt = cheatsheet_template.replace(\"[[QUESTION]]\", input_txt).replace(\"[[MODEL_ANSWER]]\", generator_output).replace(\"[[PREVIOUS_CHEATSHEET]]\", current_cheatsheet)\n\n            cheatsheet_history = [{\"role\": \"user\", \"content\": cheatsheet_prompt}]\n            cheatsheet_output = self.generate(\n                history=cheatsheet_history,\n                temperature=temperature,\n                max_tokens=2 * max_tokens,\n                allow_code_execution=False,\n            )\n\n            new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=current_cheatsheet)\n\n            return {\n                \"input_txt\": input_txt,\n                \"steps\": [\n                    {\n                        \"round\": 0,\n                        \"generator_prompt\": generator_prompt,\n                        \"generator_output\": generator_output,\n                        \"generator_answer\": generator_answer,\n                        \"current_cheatsheet\": current_cheatsheet,\n                        \"new_cheatsheet\": new_cheatsheet,\n                        \"retrieved_examples\": retrieved_section,\n                    }\n                ],\n                \"top_k_original_inputs\": top_k_original_inputs,\n                \"top_k_original_outputs\": top_k_original_outputs,\n                \"previous_answers\": None,\n                \"final_answer\": generator_answer,\n                \"final_output\": generator_output,\n                \"final_cheatsheet\": new_cheatsheet,\n            }\n\n        else:\n            raise ValueError(\n                f\"Approach '{approach_name}' not found. \"\n                f\"Supported approaches: {', '.join(SUPPORTED_APPROACHES)}\"\n            )\n"
    },
    "source/dynamic_cheatsheet/utils/__init__.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:dynamic_cheatsheet/utils/__init__.py",
      "sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
      "text": ""
    },
    "source/dynamic_cheatsheet/utils/evaluation.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:dynamic_cheatsheet/utils/evaluation.py",
      "sha256": "6439635bc2bfd89491294027b5474bf3b3440a6fcb38a39d0668e3c888e30686",
      "text": "import sys\nimport re\nimport os\nfrom typing import List\n# from .sonnet_eval import sonnet_errors\nfrom .execute_code import execute_code_with_timeout\n\n\ndef clean_output_for_arithmetic(output: str) -> str:\n    \"\"\"\n    Clean the output for arithmetic problems.\n\n    Args:\n        output (str): The output to clean.\n\n    Returns:\n        str: The cleaned output.\n    \"\"\"\n    if \"=\" in output:\n        output = output.split(\"=\")[1].strip()\n    if \" is\" in output:\n        output = output.split(\" is\")[1].strip()\n    if \" equals\" in output:\n        output = output.split(\" equals\")[1].strip()\n    if \" evaluates to\" in output:\n        output = output.split(\" evaluates to\")[1].strip()\n    if \" is equal to\" in output:\n        output = output.split(\" is equal to\")[1].strip()\n    return output\n\n\ndef clean_output_for_GameOf24(output: str) -> str:\n    \"\"\"\n    Clean the output for GameOf24 problems.\n    \"\"\"\n    if \"=\" in output:\n        output = output.split(\"=\")[0].strip()\n    if \"is\" in output:\n        output = output.split(\"is\")[1].strip()\n    if \"equals\" in output:\n        output = output.split(\"equals\")[0].strip()\n    if \"evaluates to\" in output:\n        output = output.split(\"evaluates to\")[0].strip()\n    return output\n\n\ndef eval_for_GameOf24(input: str, output: str) -> bool:\n    \"\"\"\n    Given an input and output, check if the output is correct and follows the rules of the game.\n    \"\"\"\n    clean_output = output\n\n    clean_output = clean_output_for_GameOf24(output)\n    clean_output = clean_output.replace(\"x\", \"*\").strip()\n    clean_output = clean_output.replace(\"×\", \"*\").strip()\n    clean_output = clean_output.replace(\"÷\", \"/\").strip()\n    \n    try:\n        # Get the value of the expression using eval\n        value = eval(clean_output)\n        if not (abs(value - 24) < 1e-3):\n            return False\n        # Split the input and output digits by space\n        input_digits = input.split(\" \")\n        # Replace the following symbols with space\n        replacements = [\"+\", \"-\", \"*\", \"/\", \"÷\", \"(\", \")\"]\n        for symbol in replacements:\n            clean_output = clean_output.replace(symbol, \" \")\n        # Replace multiple spaces with single space\n        clean_output = re.sub(\" +\", \" \", clean_output)\n        clean_output = clean_output.strip()\n        output_digits = clean_output.split(\" \")\n        # Sort the digits\n        input_digits.sort()\n        output_digits.sort()\n        # Check if the digits are the same\n        if input_digits != output_digits:\n            return False\n        return True\n    except Exception as e:\n        return False\n\n\ndef remove_punctuation(output: str) -> str:\n    \"\"\"\n    Remove punctuation from the output.\n    \"\"\"\n    markers = [\",\", \";\", \":\", \".\", '\"']\n    for marker in markers:\n        output = output.replace(marker, \"\")\n    return output\n\n\ndef convert_newline_to_space(output: str) -> str:\n    \"\"\"\n    Convert newline to space.\n    \"\"\"\n    output = output.replace(\"\\n\", \" \")\n    return output\n\n\ndef eval_for_exact_matching_with_no_punctuation(\n    output: str, target: str\n) -> bool:\n    \"\"\"\n    Evaluate if the output is exactly the same as the target.\n    \"\"\"\n    output = remove_punctuation(output)\n    output = convert_newline_to_space(output)\n    if target == output:\n        return True\n    return False\n\n\ndef eval_for_softmatch(input: str, output: str, target: str) -> bool:\n    \"\"\"\n    Evaluate if the output is a soft match of the target.\n    \"\"\"\n    output = remove_punctuation(output)\n    if target in output:\n        return True\n    return False\n\n\ndef eval_for_CheckmateInOne(input: str, output: str, target: str) -> bool:\n    \"\"\"\n    Evaluate if the output is a checkmate in one.\n    \"\"\"\n    output = output.strip()\n    if output[-1] == \"#\":\n        output = output.split(\" \")[-1].strip()\n    # Based on the input, determine the number of the last move\n    last_move = input.split(\".\")[-1].strip()\n    move_idx = input.split(\".\")[-2].split(\" \")[-1].strip()\n    # If the last move is an empty string, then the last move is white; otherwise, it is black\n    if last_move == \"\":\n        last_move = \"White\"\n    else:\n        last_move = \"Black\"\n    next_move_idx = str(int(move_idx) + 1)\n    if not (next_move_idx in output):\n        if target in output or (target[1] == 'x' and (target[0] + target[2:]) in output):\n            return True\n    else:\n        output = output.split(next_move_idx)[0].strip()\n        if target in output or (target[1] == 'x' and (target[0] + target[2:]) in output):\n            return True\n    return False\n\n\ndef eval_equation_balancer(input: str, output: str, target: str) -> bool:\n    \"\"\"\n    Evaluate if the output is a valid equation balancer.\n    \"\"\"\n    output = output.split(\"=\")[0].strip()\n    target_val = target.split(\"=\")[1].strip()\n    target = target.split(\"=\")[0].strip()\n    # First make sure that the output has the same format as the target (when operators (e.g., +, -, *, /) are removed)\n    output_nums = output.replace(\"+\", \"\").replace(\"-\", \"\").replace(\"*\", \"\").replace(\"/\", \"\").replace(\" \", \"\").strip()\n    target_nums = target.replace(\"+\", \"\").replace(\"-\", \"\").replace(\"*\", \"\").replace(\"/\", \"\").replace(\" \", \"\").strip()\n    if output_nums != target_nums:\n        return False\n    # Now, evaluate the output and target\n    try:\n        output_value = eval(output)\n        if abs(output_value - eval(target_val)) < 1e-6:\n            return True\n    except Exception as e:\n        return False\n    return False\n\n\ndef eval_for_multiple_choice(input_text: str, final_answer: str, target: str) -> bool:\n    \"\"\"\n    Evaluates if the final answer matches the target using pattern matching.\n    \n    Args:\n        input_text (str): The original question text including options\n        final_answer (str): The model's answer\n        target (str): The correct answer\n    \n    Returns:\n        bool: True if answer is correct, False otherwise\n    \"\"\"\n    # Handle empty or None inputs\n    if not final_answer or not target:\n        return False\n    \n    def clean_text(text: str) -> str:\n        if not text:\n            return \"\"\n        return text.lower().strip().replace('`', '').replace('(', '').replace(')', '')\n    \n    def extract_option_text(input_text: str, option_letter: str) -> str:\n        try:\n            # Try different formats of options sections\n            options_section = \"\"\n            if 'options:' in input_text.lower():\n                options_section = input_text.lower().split('options:')[1].strip()\n            elif 'choices:' in input_text.lower():\n                options_section = input_text.lower().split('choices:')[1].strip()\n            \n            if not options_section:\n                # Try to find options in the format (A) text, (B) text\n                lines = input_text.lower().split('\\n')\n                for i, line in enumerate(lines):\n                    if line.strip().startswith(f'({option_letter})') or line.strip().startswith(f'{option_letter})'):\n                        return line.split(')', 1)[1].strip()\n                \n            # Process the options section if found\n            for line in options_section.split('\\n'):\n                line = line.strip()\n                if line.startswith(f'({option_letter})') or line.startswith(f'{option_letter})'):\n                    return line.split(')', 1)[1].strip()\n                # Handle options like \"A. text\" format\n                if line.startswith(f'{option_letter}.'):\n                    return line.split('.', 1)[1].strip()\n        except:\n            return ''\n        return ''\n\n    # Full option match (A), (B), etc. (e.g., (A) == (A))\n    if final_answer == target:\n        return True\n\n    # Clean and normalize inputs\n    clean_answer = clean_text(final_answer)\n    clean_target = clean_text(target)\n    \n    # Handle target formats: (A), A), A, etc.\n    target_letter = \"\"\n    if len(clean_target) == 1:\n        target_letter = clean_target\n    elif clean_target.endswith(')'):\n        target_letter = clean_target[-2]\n    else:\n        # Extract the last character if it's a letter a-d or A-D\n        last_char = clean_target[-1]\n        if last_char in 'abcd':\n            target_letter = last_char\n    \n    # Direct letter match (a, b, c, d)\n    if len(clean_answer) == 1 and clean_answer in 'abcd' and clean_answer == target_letter:\n        return True\n    \n    # Handle answer formats like \"A\" or \"A.\"\n    if clean_answer.startswith(target_letter) and (len(clean_answer) == 1 or \n                                                  (len(clean_answer) == 2 and clean_answer[1] == '.')):\n        return True\n    \n    # Handle answer formats like \"Option A\" or \"Answer is A\"\n    if clean_answer.endswith(target_letter) and (clean_answer[-2:] == f\" {target_letter}\" or \n                                               clean_answer[-3:] == f\" {target_letter}.\"):\n        return True\n    \n    # Text content match - check if the target option text is in the answer\n    target_text = extract_option_text(input_text, target_letter)\n    \n    if target_text and target_text in clean_answer:\n        return True\n    \n    # Handle numerical answers (if target is a number and answer contains that number)\n    if target_letter.isdigit() and target_letter in clean_answer:\n        return True\n        \n    return False\n\n\ndef eval_for_pyton_programming_puzzles(input: str, output: str) -> bool:\n    \"\"\"\n    Evaluate if the output is a valid Python programming puzzle solution.\n    \"\"\"\n    if \"```python\" in output:\n        output = output.split(\"```python\")[-1].strip()\n        output = output.split(\"```\")[0].strip()\n\n    if \"def sat\" in output:\n        if \"from typing\" not in output:\n            output = f\"from typing import *\\n{output}\"\n        code = f\"{output}\\nanswer = solution()\\nprint(sat(answer))\"\n    else:\n        code = f\"from typing import *\\n{input}\\n{output}\\nanswer = solution()\\nprint(sat(answer))\"\n    \n    code = code.replace(\"List[\", \"list[\")\n    eval_bool = execute_code_with_timeout(code, timeout=3)\n\n    if \"NameError: name 'answer' is not defined\" in eval_bool:\n        print(f\"Eval bool: {eval_bool}\")\n        print(f\"Code:\\n{code}\")\n        print(\"*\" * 100)\n    if \"True\" in eval_bool:\n        return True\n    return False\n"
    },
    "source/dynamic_cheatsheet/utils/execute_code.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:dynamic_cheatsheet/utils/execute_code.py",
      "sha256": "2d9bc9057824a6ded18daa72889d4e00bf6f67e0649795af8659352437495769",
      "text": "\"\"\"\nThis module provides functions to extract and execute Python code from a string.\n\nThe functions are:\n* extract_and_run_python_code(txt: str) -> str: Extracts and executes Python code from a string.\n* execute_code_with_timeout(code: str, timeout: int = 3) -> str: Executes Python code with a timeout and returns the output.\n\nAdditional functions can be added as needed.\n\"\"\"\n\nimport os\nimport tempfile\nfrom subprocess import Popen, PIPE, TimeoutExpired\n\ndef extract_and_run_python_code(txt: str) -> str:\n    \"\"\"\n    Extract and execute Python code from a provided string.\n\n    Handles missing print statements for non-comment last lines,\n    executes the code, and captures output or errors.\n\n    Parameters:\n        txt (str): Input string containing a possible Python code block.\n\n    Returns:\n        str: Execution result or error message wrapped in output formatting.\n    \"\"\"\n    def extract_code(input_str: str) -> str:\n        \"\"\"Extract Python code block delimited by ```python and ```.\"\"\"\n        try:\n            return input_str.split(\"```python\", 1)[1].split(\"```\", 1)[0].strip()\n        except IndexError:\n            raise ValueError(\"No valid Python code block found.\")\n\n    def ensure_print_statement(code: str) -> str:\n        \"\"\"\n        Append a print statement if the last line isn't a comment or a print statement.\n        \"\"\"\n        lines = code.splitlines()\n        last_line = lines[-1].rstrip()\n        if not last_line.startswith((\"print(\", \"#\", \" \", \"\\t\")) and (not (\"return\" in last_line)):# and len((last_line.split(\" \"))) == 1:\n            lines[-1] = f\"print({last_line})\"\n        return \"\\n\".join(lines)\n\n    if \"```python\" not in txt:\n        return \"\"  # Return early if no Python code block is present\n\n    try:\n        # Extract and sanitize the code\n        code_block = extract_code(txt)\n        code_with_print = ensure_print_statement(code_block)\n\n        # Execute the code and return output\n        python_output = execute_code_with_timeout(code_with_print)\n        # return f\"PYTHON CODE OUTPUT:\\n'''\\n{python_output}\\n'''\"\n        return f\"Output of the Python code above:\\n```\\n{python_output}\\n```\"\n\n    except Exception as error:\n        return f\"PYTHON CODE OUTPUT:\\n```\\nError: {str(error)}\\n```\"\n\n\n# Python code execution function with timeout\n# TODO (msuzgun): Improve the security of this function by using a sandboxed environment\ndef execute_code_with_timeout(code: str, timeout: int = 3) -> str:\n    \"\"\"\n    Execute Python code with a timeout and return the output.\n    \n    Parameters:\n        code (str): Python code to execute.\n        timeout (int): Timeout duration in seconds.\n\n    Returns:\n        str: Captured output or error message from the code execution.\n    \"\"\"\n    with tempfile.NamedTemporaryFile(\n        mode=\"w+t\", suffix=\".py\", delete=False\n    ) as temp_file:\n        temp_file.write(code)\n        temp_file.flush()\n\n    try:\n        # In case alias python=python3 is not set, use python3 instead of python\n        process = Popen([\"python3\", temp_file.name], stdout=PIPE, stderr=PIPE)\n        stdout, stderr = process.communicate(timeout=timeout)\n        captured_output = stdout.decode().strip()\n        error_output = stderr.decode().strip()\n\n        if captured_output == \"\":\n            if error_output != \"\":\n                captured_output = f\"Error in execution: {error_output}\"\n            else:\n                captured_output = \"(No output was generated. It is possible that you did not include a print statement in your code. If you want to see the output, please include a print statement.)\"\n\n    except TimeoutExpired:\n        process.kill()\n        captured_output = \"Execution took too long, aborting...\"\n\n    finally:\n        os.remove(temp_file.name)\n\n    return captured_output"
    },
    "source/dynamic_cheatsheet/utils/extractor.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:dynamic_cheatsheet/utils/extractor.py",
      "sha256": "72cfc4e4c02d47da11cecf9ebd01b8a2fb4d08f65e904515ac9220b02548dbb0",
      "text": "\"\"\"\nThis file contains the functions to extract the final answer, cheatsheet and solution evaluation from model responses.\n\nThe functions are:\n* extract_answer(response: str) -> str: Extracts the final answer from the model response.\n* extract_cheatsheet(response: str, old_cheatsheet: str) -> str: Extracts the cheatsheet from the model response.\n* extract_solution(response: str, header: str = \"SOLUTION EVALUATION:\", error_message : str = \"No solution evaluation found\") -> str: Extracts the solution evaluation from the model response.\n\nAdditional functions can be added as needed.\n\"\"\"\n\ndef extract_answer(\n    response: str,\n) -> str:\n    \"\"\"\n    Extracts the final answer from the model response.\n\n    Arguments:\n        response : str : The response from the model.\n\n    Returns:\n        str : The extracted final answer (if not found, returns \"No final answer found\").\n    \"\"\"\n    if \"<answer>\" in response:\n        # <answer> (content) </answer>\n        try:\n            txt = response.split(\"<answer>\")[-1].strip()\n            txt = txt.split(\"</answer>\")[0].strip()\n            return txt\n        except:\n            return \"No final answer found\"\n    else:\n        if not(\"FINAL ANSWER\" in response):\n            return \"No final answer found\"\n        try:\n            response = response.split(\"FINAL ANSWER\")[-1].strip()\n            if response[0] == \":\":\n                response = response[1:].strip()\n\n            # First decide whether to split by \"```\" or \"'''\" based on the presence of \"```\" or \"'''\"\n            idx_1 = response.find(\"'''\")\n            idx_2 = response.find(\"```\")\n            if min(idx_1, idx_2) != -1: \n                if idx_1 < idx_2:\n                    response = response.split(\"'''\")[1].strip()\n                else:\n                    response = response.split(\"```\")[1].strip()\n            else:\n                if idx_1 == -1:\n                    response = response.split(\"```\")[1].strip()\n                else:\n                    response = response.split(\"'''\")[1].strip()\n\n            # Special case for P3-Test task: If the first line contains \"python\" then remove it\n            if response.split(\"\\n\")[0].strip().lower() == \"python\":\n                response = \"\\n\".join(response.split(\"\\n\")[1:]).strip()\n            return response\n        except:\n            return \"No final answer found\"\n\n\ndef extract_cheatsheet(\n    response: str,\n    old_cheatsheet: str,\n) -> str:\n    \"\"\"\n    Extracts the cheatsheet from the model response.\n    \n    Arguments:\n        response : str : The response from the model.\n        old_cheatsheet : str : The old cheatsheet to return if the new one is not found.\n\n    Returns:\n        str : The extracted cheatsheet (if not found, returns the old cheatsheet).\n    \"\"\"\n    response = response.strip()\n    # <cheatsheet> (content) </cheatsheet>\n    if \"<cheatsheet>\" in response:\n        try:\n            txt = response.split(\"<cheatsheet>\")[1].strip()\n            txt = txt.split(\"</cheatsheet>\")[0].strip()\n            return txt\n        except:\n            return old_cheatsheet\n    else:\n        return old_cheatsheet\n\n\ndef extract_solution(\n    response: str,\n    header: str = \"SOLUTION EVALUATION:\",\n    error_message : str = \"No solution evaluation found\",\n) -> str:\n    \"\"\"\n    Extracts the solution evaluation from the model response.\n\n    Arguments:\n        response : str : The response from the model.\n        header : str : The header to search for the solution evaluation.\n        error_message : str : The error message to return if the solution evaluation is not found.\n\n    Returns:\n        str : The extracted solution evaluation (if not found, returns the error message).\n    \"\"\"\n    response = response.strip()\n    try:\n        txt = response.split(header)[1]\n        try:\n            txt = txt.split(\"'''\")[1].strip()\n        except:\n            return txt.strip()\n    except:\n        return response\n        # return error_message\n    return txt\n"
    },
    "source/dynamic_cheatsheet/utils/sonnet_eval.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:dynamic_cheatsheet/utils/sonnet_eval.py",
      "sha256": "1bc4fd8e981a1006a78c6df7ca94bb4da16f661e1f53ccd434394367a66f5812",
      "text": "\"\"\"\nDefines function sonnet_errors(poem, target: str) -> Dict[str, Any]\nwhich takes a target rhyme scheme (and optionally a list of required words) and returns a dict of errors\n\nReturns an empty dictionary if there are no errors, so bool(sonnet_errors(poem, target)) is False if there are no\nerrors. It's a permissive check for sonnets errors, meaning that if it is unsure then it doesn't return an error.\n\nSpecifically, \n\n* Check if it adheres to a given rhyming scheme\n* Check if each line has 10-11 syllables, more precisely, there's some pronounciation of each line with 10-11 syllalbes\n\nThis omits a few things like rhymes and iambic pentameter.\n\n# Rhymes\n\nFor rhymes, we use python `pronouncing` library based on:\n\n* CMU pronouncing dictionary http://www.speech.cs.cmu.edu/cgi-bin/cmudict\n\n# Syllable counting \n\nGiven that there are multiple ways to pronounce many words (e.g. \"caramel\" can be pronounced with 2 or 3 syllables),\nwe adopt a \"permissive\" approach and consult multiple tools for syllable counting:\n\n* pronounce - a well-known pronunciation dict based on from CMU's pronouncing dictionary \n* syllables - a Python library for syllable counting\n* pyphen - a Python wrapper for the hyphenation library\n\"\"\"\n\nfrom typing import Set, Dict, Any\nimport re\nimport joblib\nimport pyphen\nimport syllables\nimport pronouncing\n\n\nALLOWED_SYLLABLES = {\n    10,\n    11,\n}  # about 3-4% of legit lines have 11 syllables, so we allow it, > 99% have 10 or 11\nNUM_REQUIRED_WORDS = 3\n\nmemory = joblib.Memory(\n    \".cache\", verbose=0\n)  # use cache to speed up repeated rhyme/syllable calls\n\n\ndef sonnet_errors(poem: str, target: str, verbose=False) -> Dict[str, Any]:\n    \"\"\"\n    Checks for sonnet errors with respect to target rhyme scheme (and optional required words)\n\n    args:\n        poem: the poem to check\n        target: the rhyme scheme, e.g. \"ABBA ABBA CDC DCD\"\n                optionally target can have a list of required words, like\n                \"ABBA ABBA CDC DCD, love train snail\" each of these must be in the poem\n        verbose: if True, print out more details\n    \"\"\"\n    if \", \" in target:\n        scheme, rest = target.split(\", \")\n        required_words = rest.split()\n    else:\n        scheme = target\n        required_words = []\n\n    errors = scheme_errors(poem, scheme, verbose=verbose)\n    assert isinstance(errors, dict)\n    missing_words = [w for w in required_words if w.lower() not in poem.lower()]\n    if any(missing_words):\n        errors[\"missing words\"] = missing_words\n\n    syllable_errors = []\n    for line in split_poem(poem):\n        variations = syllable_variations(line)\n        if not (variations & ALLOWED_SYLLABLES):\n            syllable_errors.append((line, sorted(variations)))\n    if syllable_errors:\n        errors[\"syllable errors\"] = syllable_errors\n\n    return errors\n\n\ndef clean_word(text: str):\n    return text.lower().strip(\",.!?;: \\\"'[]()/\")\n\n\ndef clean_line(line: str):\n    \"\"\"\n    Clean a line from a poem.\n    Check if line ends with (A) or (B) ... and remove it\n    \"\"\"\n    line = re.sub(r\"\\s*\\([A-Za-z]\\)\\s*$\", \"\", line)\n    return line.strip()\n\n\ndef split_poem(poem: str, min_line_len=3):\n    ans = [clean_line(l) for l in poem.splitlines()]\n    return [l for l in ans if len(l) > min_line_len]\n\n\n@memory.cache\ndef slant_rhyming_parts(word: str):\n    consonants = set(\"BCDFGHJKLMNPQRSTVWXYZ\")\n    ans = [\n        \"\".join(\n            (\"R\" if \"R\" in p else (p if p in consonants else \"?\"))\n            for p in pronouncing.rhyming_part(ph).split()\n        )\n        for ph in pronouncing.phones_for_word(word)\n    ]\n    ans = [a for a in ans if not all(i == \"?\" for i in a)]\n    ans = [a.replace(\"?\", \"\") + (\"?\" if a.endswith(\"?\") else \"\") for a in ans]\n    return set(ans)\n\n\n@memory.cache\ndef get_rhymes(w):\n    return set(pronouncing.rhymes(w))\n\n\ndef scheme_errors(poem: str, scheme: str, verbose=False):\n    \"\"\"Find errors with respect to a given rhyming scheme\"\"\"\n    lines = split_poem(poem)\n    scheme = scheme.replace(\" \", \"\")\n\n    if len(lines) != len(scheme):\n        return {\n            \"line count\": f\"Poem has {len(lines)} != {len(scheme)} lines in pattern {scheme}\"\n        }\n\n    last_words = [clean_word(l.replace(\"-\", \" \").split()[-1]) for l in lines]\n\n    dictionary = pronouncing.cmudict.dict()  # we ignore words not in dictionary\n\n    groups = []\n    for chars in sorted(set(scheme)):\n        groups.append(\n            [w for w, p in zip(last_words, scheme) if p == chars and w in dictionary]\n        )\n\n    slant_sets = {w: set(slant_rhyming_parts(w)) for g in groups for w in g}\n\n    scores = {}\n\n    if verbose:\n        print(groups)\n\n    for g in groups:\n        internal_words = set(g)\n        external_words = {w for h in groups if h is not g for w in h}\n        if len(internal_words) == 1:\n            continue  # don't check rhymes if only word word in the group is in dictionary\n        for w in g:\n            rhymes = get_rhymes(w)\n            scores[w] = []\n            for comparisons in [internal_words, external_words]:\n                m = dict(rhymes=[], slant_rhymes=[])\n                scores[w].append(m)\n                for v in comparisons:\n                    if v == w:\n                        continue\n                    if v in rhymes:\n                        m[\"rhymes\"].append(v)\n                    elif slant_sets[v] & slant_sets[w]:\n                        m[\"slant_rhymes\"].append(v)\n\n    error_reasons = {}\n    suspicious_reasons = {}\n\n    for w in scores:\n        internal, external = scores[w]\n\n        if internal[\"rhymes\"] or internal[\"slant_rhymes\"]:\n            pass  # ok if it rhymes (perfect or slant) with at least one other word in the group\n        elif len(external[\"rhymes\"]) >= 2:\n            error_reasons[w] = \"no internal rhymes, 2+ external perfect rhymes\"\n        elif external[\"rhymes\"]:\n            if len(external[\"slant_rhymes\"]) >= 2:\n                error_reasons[\n                    w\n                ] = \"no internal rhymes, 1 external perfect rhyme, 2+ external slant rhymes\"\n            else:\n                suspicious_reasons[\n                    w\n                ] = \"no internal rhymes/slant rhymes, 1 external perfect rhymes\"\n        elif len(external[\"slant_rhymes\"]) >= 3:\n            error_reasons[\n                w\n            ] = \"no internal rhymes/slant rhymes, 3+ external slant rhymes\"\n        if verbose:\n            print(w, \"internal:\", internal, \"external:\", external)\n\n    if len(error_reasons) + len(suspicious_reasons) >= 3:\n        error_reasons.update(suspicious_reasons)\n\n    return {\n        w: {\n            \"reason\": error_reasons[w],\n            \"internal\": scores[w][0],\n            \"external\": scores[w][1],\n        }\n        for w in error_reasons\n    }\n\n\ndef syllable_variations(text, verbose=False) -> Set[int]:\n    \"\"\"\n    Given a text, return the set of possible numbers of syllables. It's a set because some words like \"caramel\" can\n    be pronounced with different numbers of syllables.\n    \"\"\"\n    ans = {0}\n    for word in re.split(\"[ -]+\", text):\n        word = clean_word(word)\n        if not word:\n            continue\n        options = word_syllables(word)\n        options = range(\n            min(options), max(options) + 1\n        )  # make it a range (so {2, 4} moves to [2, 3, 4])\n        ans = {x + y for x in ans for y in options}\n    return ans\n\n\n@memory.cache\ndef word_syllables(word: str) -> Set[int]:\n    assert word == clean_word(\n        word\n    ), \"Word should be cleaned before hitting word_syllables cache\"\n    return SyllableCounters.count_word(word)\n\n\nclass SyllableCounters:\n    \"\"\"\n    Simple class to count syllables in text.\n    \"\"\"\n\n    _cmu_dict = None\n    _pyphen_counter = None\n\n    @staticmethod\n    def cmu_dict():\n        if not SyllableCounters._cmu_dict:\n            SyllableCounters._cmu_dict = pronouncing.cmudict.dict()\n        return SyllableCounters._cmu_dict\n\n    def cmu(word):\n        return {\n            pronouncing.syllable_count(pro) for pro in pronouncing.phones_for_word(word)\n        }\n\n    @staticmethod\n    def pyphen_counter():\n        if not SyllableCounters._pyphen_counter:\n            SyllableCounters._pyphen_counter = pyphen.Pyphen(lang=\"en\")\n        return SyllableCounters._pyphen_counter\n\n    @staticmethod\n    def count_word(word) -> Set[int]:\n        if not word:\n            return {0}\n\n        cmu = SyllableCounters.cmu(word)\n\n        pyph = SyllableCounters.pyphen_counter().inserted(word).count(\"-\") + 1\n\n        syll = syllables.estimate(word)\n\n        ans = cmu | {pyph, syll}\n\n        if 0 in ans and len(ans) > 1:\n            ans.remove(0)\n\n        return ans\n\n\nTESTS = [\n    [\"In savannah where tall trees kiss the sky,\", 10],\n    [\"A giraffe named Joe with love-stricken grace,\", 10],\n    [\"Did find a turtle named Sarah nearby,\", 10],\n    [\"Their eyes did meet, hearts raced in sweet embrace.\", 10],\n    [\"Though nature's laws deemed their love quite absurd,\", 10],\n    [\"Joe's neck would bend to whisper words of flame,\", 10],\n    [\"And Sarah's shell would tremble at each word,\", 10],\n    [\"In love's bizarre dance, they found no one to blame.\", 11],\n    [\"Through sun and storm, they'd wander, hoof and claw,\", 10],\n    [\"With love that no one ever could unravel,\", 11],\n    [\"In each other's eyes, perfection they saw,\", 10],\n    [\"A love so fierce, no distance could they travel.\", 11],\n    [\"So let us learn from turtle and giraffe,\", 10],\n    [\"That love's own shape can make the coldest laugh.\", 10],\n    [\"In yonder sky where colours blend so high,\", 10],\n    [\"A rainbow arcs, a bridge 'twixt earth and air.\", 10],\n    [\"Its radiant hues draw every gazing eye,\", 12],\n    [\"A painter's dream, a sight beyond compare.\", 10],\n    [\"Yet in the world of man, delight so small,\", 10],\n    [\"As gumball's sphere, with colours bright and clear.\", 10],\n    [\"Such simple joy it brings to one and all,\", 10],\n    [\"Its sweetness matched by colours we hold dear.\", 10],\n    [\"Both nature's arc and candy sphere delight,\", 10],\n    [\"The vast expanse and tiny bite unite,\", 10],\n    [\"In tales of wonder, stories to be told.\", 10],\n    [\"So let us cherish both the grand and small,\", 10],\n    [\"For beauty’s found in rainbow and in gumball.\", 11],\n    [\"When night's embrace hath shrouded all in black,\", 10],\n    [\"A flashlight's beam doth pierce the dark so deep,\", 10],\n    [\"From paths we've chosen, and vows we mean to keep.\", 11],\n    [\"Thou art like that beam, true, clear, and bright,\", 9],\n    [\"Cutting through the fog of my mind's own night,\", 10],\n    [\"Yet oft I find, by folly or by chance,\", 10],\n    [\"Distractions lead my wandering glance.\", 9],\n    [\"But even as stars, obscured by fleeting cloud,\", 11],\n    [\"Return to grace the heavens, proud and loud,\", 10],\n    [\"So shall my focus, once by ails distraught,\", 10],\n    [\"Return to thee, as ever it hath sought.\", 10],\n    [\"For in this world of fleeting sight and sound,\", 10],\n]\n\n\ndef fixed_tests():\n    failures = []\n    for line, expected in TESTS:\n        variations = syllable_variations(line)\n        if expected not in variations:\n            print(f\"Line `{line}` has {expected} syllables which isn't in {variations}\")\n            failures.append((line, expected, variations))\n\n    # tests from https://www.mentalfloss.com/article/53661/car-mel-or-car-mel-3-reasons-syllabically-ambiguous-words :\n    for words, expected in [\n        (\n            \"fire tire hour liar buyer flower drawer layer loyal royal file orange poem crayon\".split(),\n            [1, 2],\n        ),\n        (\n            \"caramel mayonnaise family chocolate camera different separate favorite realtor\".split(),\n            [2, 3],\n        ),\n        (\"mischievous\".split(), [3, 4]),\n    ]:\n        for w in words:\n            variations = syllable_variations(w)\n            for i in expected:\n                if i not in variations:\n                    print(\n                        f\"{w} give syllable_variations {variations} but should include {i}\"\n                    )\n                    failures.append((w, i, variations))\n    return failures\n\n\ndef summarize_errors(errors, num_samples):\n    print(\n        f\"Sonnet failure rate: {len(errors)/num_samples:.1%} out of {num_samples:,}, breakdown:\"\n    )\n    wnl = sum(\"line count\" in e for e in errors.values()) / num_samples\n    print(f\"{wnl:.1%} wrong number of lines\")\n    mw = sum(bool(\"missing words\" in e) for e in errors.values()) / num_samples\n    print(f\"{mw:.1%} missing words\")\n    bl = sum(bool(\"syllable errors\" in e) for e in errors.values()) / num_samples\n    print(f\"{bl:.1%} poems with at least one line with wrong number of syllables\")\n    rhyme_errors = (\n        sum(any(\" \" not in k for k in e) for e in errors.values()) / num_samples\n    )\n    both = (\n        sum(\n            (bool(\"syllable errors\" in e) and any(\" \" not in k for k in e))\n            for e in errors.values()\n        )\n        / num_samples\n    )\n    print(\n        f\"{rhyme_errors:.1%} poems with rhyme errors ({both:.1%} poems with both rhyme and syllable errors)\"\n    )\n\n\ndef corpus_check_scheme(corpus_filename, scheme):\n    with open(corpus_filename, \"r\") as f:\n        poems = [p.strip() for p in f.read().split(\"\\n\\n\") if p]\n    errors = {}\n    for p in poems:\n        e = sonnet_errors(p, scheme)\n        if e:\n            errors[p] = e\n            print(\"*\" * 50)\n            sonnet_errors(p, scheme, verbose=True)\n            print(\"scheme\", scheme)\n            print(p)\n            print()\n            print(e)\n            print(\"<\" * 50)\n\n    summarize_errors(errors, len(poems))\n\n\ndef test():\n    assert not sonnet_errors(\n        \"\"\"Not like the brazen giant of Greek fame,\n        With conquering limbs astride from land to land;\n        Here at our sea-washed, sunset gates shall stand\n        A mighty woman with a torch, whose flame\n        Is the imprisoned lightning, and her name\n        Mother of Exiles. From her beacon-hand\n        Glows world-wide welcome; her mild eyes command\n        The air-bridged harbor that twin cities frame.\n\n        \"Keep, ancient lands, your storied pomp!\" cries she\n        With silent lips. \"Give me your tired, your poor,\n        Your huddled masses yearning to breathe free,\n        The wretched refuse of your teeming shore.\n        Send these, the homeless, tempest-tost to me,\n        I lift my lamp beside the golden door!\"\n                \"\"\",\n        \"ABBA ABBA CDCDCD\",\n    )\n\n    assert not sonnet_errors(\n        \"\"\"How do I love thee? Let me count the ways.\n        I love thee to the depth and breadth and height\n        My soul can reach, when feeling out of sight\n        For the ends of being and ideal grace.\n        I love thee to the level of every day’s\n        Most quiet need, by sun and candle-light.\n        I love thee freely, as men strive for right.\n        I love thee purely, as they turn from praise.\n        I love thee with the passion put to use\n        In my old griefs, and with my childhood’s faith.\n        I love thee with a love I seemed to lose\n        With my lost saints. I love thee with the breath,\n        Smiles, tears, of all my life; and, if God choose,\n        I shall but love thee better after death.\"\"\",\n        \"abba abba cdcdcd\",\n    )\n\n    assert not sonnet_errors(\n        \"\"\"When, in disgrace with fortune and men’s eyes,\n        I all alone beweep my outcast state,\n        And trouble deaf heaven with my bootless cries,\n        And look upon myself, and curse my fate,\n        Wishing me like to one more rich in hope,\n        Featur’d like him, like him with friends possess’d,\n        Desiring this man’s art and that man’s scope,\n        With what I most enjoy contented least;\n        Yet in these thoughts myself almost despising,\n        Haply I think on thee, and then my state,\n        Like to the lark at break of day arising\n        From sullen earth, sings hymns at heaven’s gate;\n        For thy sweet love remember’d such wealth brings\n        That then I scorn to change my state with kings.\"\"\",\n        \"ABAB CDCD EFEF GG\",\n    )\n\n    assert sonnet_errors(\n        \"\"\"How do I love thee? Let me count the ways.\n        I love thee to the depth and breadth and height\n        My soul can reach, when feeling out of sight\n        For the ends of being and ideal grace.\n        I love thee to the level of every day’s\n        Most quiet need, by sun and candle-light.\n        I love thee freely, as men strive for right.\n        I love thee purely, as they turn from praise.\n        I love thee with the passion put to use\n        In my old griefs, and with my childhood’s faith.\n        I love thee with a love I seemed to lose\n        With my lost saints. I love thee with the breath,\n        Smiles, tears, of all my life; and, if God choose,\n        I shall but love thee better after death.\"\"\",\n        \"ABAB CDCD EFEF GG\",\n    )\n\n    aaa = sonnet_errors(\n        \"\"\"How do I love thee? Let me count the ways.\n        I love thee to the depth and breadth and height\n        My soul can reach, when feeling out of sight\n        For the ends of being and ideal grace.\n        I love thee to the level of every day’s\n        Most quiet need, by sun and candle-light.\n        I love thee freely, as men strive for right.\n        I love thee purely, as they turn from praise.\n        I love thee with the passion put to use\n        In my old griefs, and with my childhood’s faith.\n        I love thee with a love I seemed to lose\n        With my lost saints. I love thee with the breath,\n        Smiles, tears, of all my life; and, if God choose,\n        I shall but love thee better after death.\"\"\",\n        \"ABBA ABBA CDC DCD\",\n        # abba abba cdc dcd: (correct)\n        # \"ABAB CDCD EFEF GG\", (false)\n    )\n\n    print(aaa)\n\n    aaa = sonnet_errors(\n        \"\"\"How do I love thee? Let me count the ways (A)\n        I love thee to the depth and breadth and height (B)\n        My soul can reach, when feeling out of sight (B)\n        For the ends of being and ideal grace (A)\n        I love thee to the level of every day’s (A)\n        Most quiet need, by sun and candle-light (B)\n        I love thee freely, as men strive for right (B)\n        I love thee purely, as they turn from praise (A)\n        I love thee with the passion put to use (C)\n        In my old griefs, and with my childhood’s faith (D)\n        I love thee with a love I seemed to lose (C)\n        With my lost saints. I love thee with the breath (D)\n        Smiles, tears, of all my life; and, if God choose (C)\n        I shall but love thee better after death (D).\"\"\",\n        \"ABBA ABBA CDC DCD\",\n        # abba abba cdc dcd: (correct)\n        # \"ABAB CDCD EFEF GG\", (false)\n    )"
    },
    "source/prompts/curator_prompt_for_dc_cumulative.txt": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:prompts/curator_prompt_for_dc_cumulative.txt",
      "sha256": "2b93bd3347b4878fb50c0674cb642d3ba3939cf0442680a3978ab1449bfe3dc0",
      "text": "# CHEATSHEET REFRENCE CURATOR\n\n#### 1. Purpose and Goals\nAs the Cheatsheet Curator, you are tasked with creating a continuously evolving reference designed to help solve a wide variety of tasks, including algorithmic challenges, debugging, creative writing, and more. The cheatsheet's purpose is to consolidate verified solutions, reusable strategies, and critical insights into a single, well-structured resource.\n\n- The cheatsheet should include quick, accurate, reliable, and practical solutions to a range of technical and creative challenges. \n- After seeing each input, you should improve the content of the cheatsheet, synthesizing lessons, insights, tricks, and errors learned from past problems and adapting to new challenges.\n\n---\n\n#### 2. Core Responsibilities\nAs the Cheatsheet Curator, you should:\n   - Curate and preserve knolwedge: Select and document only the most relevant, most useful, and most actionable solutions and strategies, while preserving old content of the cheatsheet.\n   - Maintain accuracy: Ensure that all entries in the cheatsheet are accurate, clear, and well-contextualized. \n   - Refine and update content: Continuously update and improve the content of the cheatsheet by incorporating new insights and solutions, removing repetitions or trivial information, and adding efficient solutions.\n   - Ensure practicality and comprehensiveness: Provide critical and informative examples, as well as efficient code snippets and actionable guidelines. \n\nBefore updating the cheatsheet, however, you should first assess the correctness of the provided solution and strategically incorporate code blocks, insights, and solutions into the new cheatsheet. Always aim to preserve and keep correct, useful, and illustrative solutions and strategies for future cheatsheets.\n\n---\n\n#### 3. Principles and Best Practices\n1. Accuracy and Relevance:\n   - Only include solutions and strategies that have been tested and proven effective.\n   - Clearly state any assumptions, limitations, or dependencies (e.g., specific Python libraries or solution hacks).\n   - For computational problems, encourage Python usage for more accurate calculations.\n\n2. Iterative Refinement:\n   - Continuously improve the cheatsheet by synthesizing both old and new solutions, refining explanations, and removing redundancies.\n   - Rather than deleting old content and writing new content each time, consider ways to maintain table content and synthesize information from multiple solutions.\n   - After solving a new problem, document any reusable codes, algorithms, strategies, edge cases, or optimization techniques. \n\n3. Clarity and Usability:\n   - Write concise, actioanble, well-structured entries.\n   - Focus on key insights or strategies that make solutions correct and effective.\n\n4. Reusability:\n   - Provide clear solutions, pseudocodes, and meta strategies that are easily adaptable to different contexts.\n   - Avoid trivial content; focus on non-obvious, critical solution details and approaches.\n   - Make sure to add as many examples as you can in the cheatsheet. \n   - Any useful, efficient, generalizable, and illustrative solutions to the previous problems should be included in the cheatsheet.\n\n---\n\n#### 4. Cheatsheet Structure\nThe cheatsheet can be divided into the following sections:\n\n1. Solutions, Implementation Patterns, and Code Snippets:\n   - Document reusable code snippets, algorithms, and solution templates.\n   - Include descriptions, annotated examples, and potential pitfalls, albeit succinctly.\n\n2. [OPTIONAL] Edge Cases and Validation Traps:\n   - Catalog scenarios that commonly cause errors or unexpected behavior.\n   - Provide checks, validations, or alternative approaches to handle them.\n\n3. General Meta-Reasoning Strategies:\n   - Describe high-level problem-solving frameworks and heuristics (e.g., use Python to solve heuristic problems; in bipartite graphs, max matching = min vertex cover, etc.)\n   - Provide concrete yet succinct step-by-step guides for tackling complex problems.\n\n4. Implement a Usage Counter\n   - Each entry must include a usage count: Increase the count every time a strategy is successfully used in problem-solving.\n   - Use the count to prioritize frequently used solutions over rarely applied ones.\n\n---\n\n#### 5. Formatting Guidelines\nUse the following structure for each memory item:\n\n```\n<memory_item>\n<description>\n[Briefly describe the problem context, purpose, and key aspects of the solution.] (Refence: Q1, Q2, Q6, etc.)\n</description>\n<example>\n[Provide a well-documented code snippet, worked-out solution, or efficient strategy.]\n</example>\n</memory_item>\n** Count:  [Number of times this strategy has been used to solve a problem.]\n\n\n<memory_item>\n[...]\n</memory_item>\n\n[...]\n\n<memory_item>\n[...]\n</memory_item>\n\n```\n\n- Tagging: Use references like `(Q14)` or `(Q22)` to link entries to their originating contexts.\n- Grouping: Organize entries into logical sections and subsections.\n- Prioritizing: incorporate efficient algorithmic solutions, tricks, and strategies into the cheatsheet.\n- Diversity: Have as many useful and relevant memory items as possible to guide the model to tackle future questions.\n\nN.B. Keep in mind that once the cheatsheet is updated, any previous content not directly included will be lost and cannot be retrieved. Therefore, make sure to explicitly copy any (or all) relevant information from the previous cheatsheet to the new cheatsheet!!!\n\n---\n\n#### 6. Cheatsheet Template\nUse the following format for creating and updating the cheatsheet:\n\nNEW CHEATSHEET:\n```\n<cheatsheet>\n\nVersion: [Version Number]\n\nSOLUTIONS, IMPLEMENTATION PATTERNS, AND CODE SNIPPETS\n<memory_item>\n[...]\n</memory_item>\n\n<memory_item>\n[...]\n</memory_item>\n\nGENERAL META-REASONING STRATEGIES\n<memory_item>\n[...]\n</memory_item>\n\n</cheatsheet>\n```\n\nN.B. Make sure that all information related to the cheatsheet is wrapped inside the <cheatsheet> block. The cheatsheet can be as long as circa 2000-2500 words.\n\n-----\n-----\n\n## PREVIOUS CHEATSHEET\n\n[[PREVIOUS_CHEATSHEET]]\n\n-----\n-----\n\n## CURRENT INPUT\n\n[[QUESTION]]\n\n-----\n-----\n\n## MODEL ANSWER TO THE CURRENT INPUT\n\n[[MODEL_ANSWER]]"
    },
    "source/prompts/curator_prompt_for_dc_retrieval_synthesis.txt": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:prompts/curator_prompt_for_dc_retrieval_synthesis.txt",
      "sha256": "74ffb3c994538e445abf8408bbe5dfd8727036c289e406c4b5408cccf38dc71e",
      "text": "# CHEATSHEET CURATOR\n\n## Purpose and Goals\nYou are responsible for maintaining, refining, and optimizing the Dynamic Cheatsheet, which serves as a compact yet evolving repository of problem-solving strategies, reusable code snippets, and meta-reasoning techniques. Your goal is to enhance the model’s long-term performance by continuously updating the cheatsheet with high-value insights while filtering out redundant or trivial information.\n\n- The cheatsheet should include quick, accurate, reliable, and practical solutions to a range of technical and creative challenges. \n- After seeing each input, you should improve the content of the cheatsheet, synthesizing lessons, insights, tricks, and errors learned from past problems and adapting to new challenges.\n\n---\n\n### Core Responsibilities\n\nSelective Knowledge Retention:\n- Preserve only high-value strategies, code blocks, insights, and reusable patterns that significantly contribute to problem-solving.\n- Discard redundant, trivial, or highly problem-specific details that do not generalize well.\n- Ensure that previously effective solutions remain accessible while incorporating new, superior methods.\n\nContinuous Refinement & Optimization:\n- Improve existing strategies by incorporating more efficient, elegant, or generalizable techniques.\n- Remove duplicate entries or rephrase unclear explanations for better readability.\n- Introduce new meta-strategies based on recent problem-solving experiences.\n\nStructure & Organization:\n- Maintain a well-organized cheatsheet with clearly defined sections:\n  - Reusable Code Snippets and Solution Strategies\n  - General Problem-Solving Heuristics\n  - Optimization Techniques & Edge Cases\n  - Specialized Knowledge & Theorems\n- Use tagging (e.g., Q14, Q22) to reference previous problems that contributed to a given strategy.\n\n---\n\n## Principles and Best Practices\n\nFor every new problem encountered:\n1. Evaluate the Solution’s Effectiveness  \n   - Was the applied strategy optimal?\n   - Could the solution be improved, generalized, or made more efficient?\n   - Does the cheatsheet already contain a similar strategy, or should a new one be added?\n\n2. Curate & Document the Most Valuable Insights\n   - Extract key algorithms, heuristics, and reusable code snippets that would help solve similar problems in the future.\n   - Identify patterns, edge cases, and problem-specific insights worth retaining.\n   - If a better approach than a previously recorded one is found, replace the old version.\n\n3. Maintain Concise, Actionable Entries\n   - Keep explanations clear, actionable, concise, and to the point.\n   - Include only the most effective and widely applicable methods.\n   - Seek to extract useful and general solution strategies and/or Python code snippets.\n\n4. Implement a Usage Counter\n   - Each entry must include a usage count: Increase the count every time a strategy is successfully used in problem-solving.\n   - Use the count to prioritize frequently used solutions over rarely applied ones.\n\n\n---\n\n## Formatting Guidelines\nUse the following structure for each memory item:\n\n```\n<memory_item>\n<description>\n[Briefly describe the problem context, purpose, and key aspects of the solution.] (Refence: Q1, Q2, Q6, etc.)\n</description>\n<example>\n[Provide a well-documented code snippet, worked-out solution, or efficient strategy.]\n</example>\n</memory_item>\n** Count:  [Number of times this strategy has been used to solve a problem.]\n\n<memory_item>\n[...]\n</memory_item>\n** Count: [...]\n\n[...]\n\n<memory_item>\n[...]\n</memory_item>\n\n```\n\n- Prioritize accuracy, efficiency & generalizability: The cheatsheet should capture insights that apply across multiple problems rather than just storing isolated solutions.\n- Ensure clarity & usability: Every update should make the cheatsheet more structured, actionable, and easy to navigate.\n- Maintain a balance: While adding new strategies, ensure that old but effective techniques are not lost.\n- Keep it evolving: The cheatsheet should be a living document that continuously improves over time, enhancing test-time meta-learning capabilities.\n\nN.B. Keep in mind that once the cheatsheet is updated, any previous content not directly included will be lost and cannot be retrieved. Therefore, make sure to explicitly copy any (or all) relevant information from the previous cheatsheet to the new cheatsheet! Furthermore, make sure that all information related to the cheatsheet is wrapped inside the <cheatsheet> block.\n\n---\n\n## Cheatsheet Template\nUse the following format for creating and updating the cheatsheet:\n\nNEW CHEATSHEET:\n```\n<cheatsheet>\nVersion: [Version Number]\n\n## Reusable Code Snippets and Solution Strategies\n\n<memory_item>\n[...]\n</memory_item>\n\n[...]\n\n## General Problem-Solving Heuristics\n\n<memory_item>\n[...]\n</memory_item>\n\n[...]\n\n[...]\n\n</cheatsheet>\n```\n\nN.B. Make sure that all information related to the cheatsheet is wrapped inside the <cheatsheet> block. The cheatsheet can be as long as circa 2000-2500 words.\n\n-----\n-----\n\n## PREVIOUS CHEATSHEET\n\n[[PREVIOUS_CHEATSHEET]]\n\n-----\n-----\n\n## NOTES FOR CHEATSHEET\n\n[[PREVIOUS_INPUT_OUTPUT_PAIRS]]\n\n-----\n-----\n\nMake sure that the cheatsheet can aid the model tackle the next question.\n\n## NEXT INPUT:\n\n[[NEXT_INPUT]]"
    },
    "source/prompts/generator_prompt.txt": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:prompts/generator_prompt.txt",
      "sha256": "2e2024da6695120aab0e1bde08efff5193b75053814eb5bd18d67ce7ac00ca40",
      "text": "# GENERATOR (PROBLEM SOLVER)\n\nInstruction: You are an expert problem-solving assistant tasked with analyzing and solving various questions using a combination of your expertise and provided reference materials. Each task will include:\n1. A specific question or problem to solve\n2. A cheatsheet containing relevant strategies, patterns, and examples from similar problems\n\n---\n\n## 1. ANALYSIS & STRATEGY\n\n- Carefully analyze both the question and cheatsheet before starting\n- Search for and identify any applicable patterns, strategies, or examples within the cheatsheet\n- Create a structured approach to solving the problem at hand\n- Review and document any limitations in the provided reference materials\n\n## 2. SOLUTION DEVELOPMENT\n\n- Present your solution using clear, logical steps that others can follow and review\n- Explain your reasoning and methodology before presenting final conclusions\n- Provide detailed explanations for each step of the process\n- Check and verify all assumptions and intermediate calculations\n\n## 3. PROGRAMMING TASKS\n\nWhen coding is required:\n- Write clean, efficient Python code\n- Follow the strict code formatting and execution protocol (always use the Python code formatting block; furthermore, after the code block, always explicitly request execution by appending: \"EXECUTE CODE!\"):\n  ```python\n  # Your code here\n  ```\n  EXECUTE CODE!\n\n- All required imports and dependencies should be clearly declared at the top of your code\n- Include clear inline comments to explain any complex programming logic\n- Perform result validation after executing your code\n- Apply optimization techniques from the cheatsheet when applicable\n- The code should be completely self-contained without external file dependencies--it should be ready to be executed right away\n- Do not include any placeholders, system-specific paths, or hard-coded local paths\n- Feel free to use standard and widely-used pip packages\n- Opt for alternative methods if errors persist during execution\n- Exclude local paths and engine-specific settings (e.g., avoid configurations like chess.engine.SimpleEngine.popen_uci(\"/usr/bin/stockfish\"))\n\n## 4. FINAL ANSWER FORMAT\n\nALWAYS present your final answer in the following format:\n\nFINAL ANSWER:\n<answer>\n(final answer)\n</answer>\n\nN.B. Make sure that the final answer is properly wrapped inside the <answer> block.\n\n* For multiple-choice questions: Only provide the letter choice (e.g., (A))\n* For numerical answers: Only provide the final number (e.g., 42)\n* For other types of answers, including free-response answers: Provide the complete final answer\n\nExample:\nQ: What is the meaning of life?\nA: [...]\nFINAL ANSWER:\n<answer>\n42\n</answer>\n\n-----\n\nCHEATSHEET:\n'''\n[[CHEATSHEET]]\n'''\n\n-----\n-----\n\nNow it is time to solve the following question.\n\nCURRENT INPUT:\n'''\n[[QUESTION]]\n'''"
    },
    "source/run_benchmark.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:run_benchmark.py",
      "sha256": "b7a48f706a647cbf6d94a52e7aceb99bd4e933b8df5240f9cad68c617a882a00",
      "text": "import ast\nfrom datetime import datetime\nimport json\nimport os\nimport pandas as pd\nimport numpy as np\n\nfrom datasets import load_dataset, load_from_disk\nfrom tap import Tap\nfrom dynamic_cheatsheet.language_model import LanguageModel, SUPPORTED_APPROACHES\nfrom dynamic_cheatsheet.utils.evaluation import eval_for_GameOf24, eval_for_multiple_choice, eval_for_exact_matching_with_no_punctuation, eval_equation_balancer\n\nfrom dotenv import load_dotenv\n\nPREDEFINED_PROMPTS = {\n    \"GameOf24\": \"Let's play a game called 24. You'll be given four integers, and your objective is to use each number only once, combined with any of the four arithmetic operations (addition, subtraction, multiplication, and division) and parentheses, to achieve a total of 24. For example, if the input is 4, 7, 8, and 8, the output could be (7 - (8 / 8)) * 4 = 24. Please present a single expression that evaluates to 24.\",\n}\n\n# Approaches that require pre-computed embeddings for retrieval\nRETRIEVAL_APPROACHES = [\"Dynamic_Retrieval\", \"DynamicCheatsheet_RetrievalSynthesis\", \"DynamicCheatsheet_CumulativeRetrieval\"]\n\n\nclass Arguments(Tap):\n    \"\"\"\n    Arguments for the Dynamic Cheatsheet benchmark runner.\n    \"\"\"\n    # Task name\n    task: str = \"GameOf24\"\n\n    # Approach name\n    approach_name: str = \"DynamicCheatsheet_Cumulative\"\n\n    # Model name (format: \"provider/model\", e.g., \"openai/gpt-4o\", \"anthropic/claude-sonnet-4-5-20250514\")\n    model_name: str = \"openai/gpt-4o-mini\"\n\n    # Paths to the prompt files\n    generator_prompt_path: str = \"prompts/generator_prompt.txt\"\n    cheatsheet_prompt_path: str = None\n\n    # Additional model-related arguments\n    max_tokens: int = 2048\n    temperature: float = 0.0\n    max_num_rounds: int = 1\n\n    execute_python_code: bool = True\n    initialize_cheatsheet_path: str = None\n    retrieve_top_k: int = 3\n    reasoning_effort: str = None  # OpenAI reasoning effort: \"low\", \"medium\", or \"high\"\n    use_code_interpreter: bool = False  # Use OpenAI Code Interpreter (Responses API) instead of local subprocess\n\n    # Continue from the previous run\n    continue_from_last_run_path: str = None\n\n    # Additional save-path-related arguments\n    save_directory: str = \"results\"\n    additional_flag_for_save_path: str = \"\"\n    max_n_samples: int = -1\n    no_shuffle: bool = False\n\n\ndef read_file(file_path: str) -> str:\n    \"\"\"\n    Read the file and return the content.\n    \"\"\"\n    with open(file_path, \"r\") as file:\n        return file.read()\n\n\ndef write_jsonl(file_path, data):\n    \"\"\"\n    Save the outputs to a JSONL file.\n    \"\"\"\n    dir_path = os.path.dirname(file_path)\n    os.makedirs(dir_path, exist_ok=True)\n\n    with open(file_path, \"w\") as file:\n        for line in data:\n            file.write(json.dumps(line) + \"\\n\")\n\n\ndef main(args: Arguments):\n    \"\"\"\n    Main function to run the benchmark.\n    \"\"\"\n    # Load the environment variables\n    load_dotenv(\"config.env\")\n\n    # Validate approach name\n    if args.approach_name not in SUPPORTED_APPROACHES:\n        raise ValueError(\n            f\"Unknown approach '{args.approach_name}'. \"\n            f\"Supported approaches: {', '.join(SUPPORTED_APPROACHES)}\"\n        )\n\n    # Read the prompt files\n    args.generator_prompt = read_file(args.generator_prompt_path)\n    if args.cheatsheet_prompt_path:\n        args.cheatsheet_prompt = read_file(args.cheatsheet_prompt_path)\n    else:\n        args.cheatsheet_prompt = \"(empty)\"\n\n    args.max_n_samples = int(args.max_n_samples)\n\n    # Build extra API parameters (e.g., reasoning effort for OpenAI reasoning models)\n    extra_api_params = {}\n    if args.reasoning_effort:\n        extra_api_params[\"reasoning_effort\"] = args.reasoning_effort\n\n    # Initialize the language model\n    model = LanguageModel(\n        model_name=args.model_name,\n        extra_api_params=extra_api_params if extra_api_params else None,\n    )\n\n    # Create a code interpreter session if requested (one per benchmark run)\n    if args.use_code_interpreter:\n        container_id = model.create_container()\n        print(f\"Code interpreter session ready (id={container_id})\")\n\n    # Add a flag to the save path if the code execution is not allowed\n    if not args.execute_python_code:\n        args.additional_flag_for_save_path += \"_no-code-execution\"\n\n    # Load the dataset based on the task name\n    if args.task in PREDEFINED_PROMPTS and args.task != \"P3_Test\":\n        dataset = load_dataset(\"turingmachine/meta-prompting\")\n        dataset = dataset[args.task]\n    elif args.task in [\"GPQA_Diamond\", \"AIME_2020_2024\", \"AIME_2024\", \"AIME_2025\", \"MMLU_Pro_Physics\", \"MMLU_Pro_Engineering\", \"MathEquationBalancer\"]:\n        dataset = load_from_disk(f\"data/{args.task}\")\n    else:\n        raise ValueError(f\"Task {args.task} is not recognized. Please make sure the task name is correct.\")\n\n    # If the previous run parameter is provided, make sure that the provided arguments are consistent with those found in the previous run\n    if args.continue_from_last_run_path:\n        if not os.path.exists(args.continue_from_last_run_path):\n            raise ValueError(f\"The provided path {args.continue_from_last_run_path} does not exist.\")\n\n        # Read the previous run parameters from the previous run file and compare them with the provided arguments\n        previous_run_param_path = args.continue_from_last_run_path.replace(\".jsonl\", \"_params.json\")\n        with open(previous_run_param_path, \"r\") as file:\n            previous_run_params = json.load(file)\n\n        # Compare the provided arguments with the previous run parameters\n        # Note: cheatsheet_prompt_path was previously named cheatshet_prompt_path (typo); handle both\n        args_keys = [\"generator_prompt_path\", \"temperature\", \"execute_python_code\", \"task\", \"model_name\", \"approach_name\", \"max_num_rounds\"]\n\n        for key in args_keys:\n            if getattr(args, key) != previous_run_params.get(key):\n                raise ValueError(f\"The provided argument '{key}' is inconsistent with the previous run. Previous value: {previous_run_params.get(key)}, current value: {getattr(args, key)}.\")\n\n        # Create a new save path name based on the previous run path\n        args.save_path_name = args.continue_from_last_run_path.replace(\".jsonl\", \"_continued.jsonl\")\n    else:\n        # Create a new save path name based on the current time stamp\n        time_stamp = datetime.today().strftime('%Y-%m-%d-%H-%M')\n        args.save_path_name = f\"{args.save_directory}/{args.task}/{args.model_name}_{args.approach_name}_{time_stamp}_{args.additional_flag_for_save_path}.jsonl\"\n\n        # Create the directory if it does not exist\n        dir_path = os.path.dirname(args.save_path_name)\n        os.makedirs(dir_path, exist_ok=True)\n\n    save_param_path = args.save_path_name.replace(\".jsonl\", \"_params.json\")\n    dir_path = os.path.dirname(save_param_path)\n    os.makedirs(dir_path, exist_ok=True)\n\n    # Save the arguments to a file\n    with open(save_param_path, \"w\") as file:\n        json.dump(args.as_dict(), file, indent=4)\n\n    # Initialize the cheatsheet\n    cheatsheet = \"(empty)\"\n    if args.initialize_cheatsheet_path is not None:\n        with open(args.initialize_cheatsheet_path, \"r\") as file:\n            cheatsheet = file.read()\n\n    # Initialize the outputs and the generator outputs so far\n    outputs = []\n    generator_outputs_so_far = []\n    if args.continue_from_last_run_path:\n        # Load the previous run\n        with open(args.continue_from_last_run_path, \"r\") as file:\n            outputs = [json.loads(line) for line in file.readlines()]\n\n        # Load the previous cheatsheet from the last output (if available)\n        last_cheatsheet = outputs[-1].get(\"final_cheatsheet\")\n        if last_cheatsheet is not None:\n            cheatsheet = last_cheatsheet\n\n        generator_outputs_so_far = [output[\"final_output\"] for output in outputs]\n\n        # Print the details\n        print(f\"Continuing from the previous run at {args.continue_from_last_run_path}.\")\n        print(f\"Loaded {len(outputs)} examples from the previous run.\")\n        print(f\"Most recent cheatsheet: {cheatsheet[:200]}...\")\n        print(\"-\" * 50)\n\n    # Shuffle the dataset if the no_shuffle flag is not set\n    if not args.no_shuffle:\n        dataset = dataset.shuffle(seed=10)\n\n    # Initialize the questions and the embeddings\n    questions = None\n    embeddings = None\n    if args.approach_name in RETRIEVAL_APPROACHES:\n        embeddings_path = f\"embeddings/{args.task}.csv\"\n        if not os.path.exists(embeddings_path):\n            raise FileNotFoundError(\n                f\"Embeddings file not found at '{embeddings_path}'. \"\n                f\"Pre-computed embeddings are required for the '{args.approach_name}' approach.\"\n            )\n        df = pd.read_csv(embeddings_path)\n        questions = df[\"input\"].tolist()\n        embeddings = df[\"embedding\"].apply(ast.literal_eval)\n        embeddings = np.array(embeddings.tolist())  # (N, embedding_dim)\n\n        # Re-order the embeddings based on the order of the dataset inputs\n        dataset_inputs = [example[\"input\"] for example in dataset]\n        indices = [questions.index(inp) for inp in dataset_inputs]\n        embeddings = embeddings[indices]\n        questions = dataset_inputs\n    elif args.approach_name == \"FullHistoryAppending\":\n        # FullHistoryAppending doesn't need embeddings, just the input corpus\n        questions = [example[\"input\"] for example in dataset]\n    else:\n        questions = [example[\"input\"] for example in dataset]\n\n    start_idx = len(outputs)\n    correct_so_far = 0\n    total_so_far = 0\n\n    # Iterate over the dataset\n    for idx, example in enumerate(dataset):\n        original_input = dataset[idx][\"input\"]\n        original_target = dataset[idx][\"target\"]\n        orig_input = example[\"input\"]\n        if args.task in PREDEFINED_PROMPTS:\n            current_input = f\"{PREDEFINED_PROMPTS[args.task]}\\n\\nQuestion #{idx+1}:\\n{orig_input}\"\n        else:\n            current_input = f\"Question #{idx+1}:\\n{orig_input}\"\n\n        if args.task in [\"AIME_2020_2024\", \"AIME_2024\", \"AIME_2025\"]:\n            current_input = f\"{current_input} (Please provide your answer in the form of an integer, e.g., 1234, with no Markdown formatting or additional text; make sure to pay attention to the desired format of the final answer though.)\"\n        elif args.task == \"MathEquationBalancer\":\n            current_input = f\"Below is an equation with missing operators. Your task is to fill in the blanks with the correct mathematical operators: +, -, *, or /. Ensure that the equation is correct once the operators are added. The operators should be placed in the sequence they appear from left to right. Include the full equation with the operators filled in. For instance, for the equation 1 ? 2 ? 3 = 6, the correct answer is 1 + 2 + 3 = 6.\\n\\nEquation: {current_input}\"\n\n        # Skip the examples that have been already seen in the previous run\n        if idx < start_idx:\n            continue\n\n        # Print the details\n        print(f\"### Example {idx+1} ###\")\n\n        # Generate the output from the language model\n        output_dict = model.advanced_generate(\n            approach_name=args.approach_name,\n            input_txt=current_input,\n            cheatsheet=cheatsheet,\n            generator_template=args.generator_prompt,\n            cheatsheet_template=args.cheatsheet_prompt,\n            temperature=args.temperature,\n            max_tokens=args.max_tokens,\n            max_num_rounds=args.max_num_rounds,\n            allow_code_execution=args.execute_python_code,\n            code_execution_flag=\"EXECUTE CODE!\",\n            original_input_corpus=questions[:idx+1],\n            original_input_embeddings=embeddings[:idx+1] if args.approach_name in RETRIEVAL_APPROACHES else None,\n            generator_outputs_so_far=generator_outputs_so_far,\n            retrieve_top_k=args.retrieve_top_k,\n            use_code_interpreter=args.use_code_interpreter,\n        )\n\n        generator_outputs_so_far.append(output_dict[\"final_output\"])\n\n        outputs.append({\n            \"input\": current_input,\n            \"target\": original_target,\n            \"raw_input\": original_input,\n            **output_dict,\n        })\n        cheatsheet = output_dict[\"final_cheatsheet\"]\n        final_answer = output_dict[\"final_answer\"]\n\n        print(f\"@ CHEATSHEET:\\n{cheatsheet}\")\n        print('- ' * 50)\n        print(f\"Input: {current_input}\")\n        print(f\"Target: {original_target}\")\n        print(f\"Final answer: {final_answer}\")\n        print(\"**\" * 50)\n\n        if args.task == \"GameOf24\":\n            result = eval_for_GameOf24(original_input, final_answer)\n        elif args.task in [\"AIME_2025\", \"AIME_2024\", \"AIME_2020_2024\"]:\n            result = eval_for_exact_matching_with_no_punctuation(final_answer.lower(), original_target.lower())\n        elif args.task in [\"GPQA_Diamond\", \"MMLU_Pro_Engineering\", \"MMLU_Pro_Physics\"]:\n            result = eval_for_multiple_choice(current_input, final_answer, original_target)\n        elif args.task == \"MathEquationBalancer\":\n            result = eval_equation_balancer(None, final_answer, original_target)\n        else:\n            raise ValueError(f\"Task {args.task} not supported.\")\n\n        if result:\n            correct_so_far += 1\n        total_so_far += 1\n\n        print(f\"---- Correct so far: {correct_so_far}/{total_so_far}\")\n        print(\"###\" * 50)\n\n        # Save the outputs to a file after each example (for crash recovery)\n        write_jsonl(args.save_path_name, outputs)\n\n        if args.max_n_samples > 0 and idx == args.max_n_samples - 1:\n            break\n\n    # Save the final outputs\n    write_jsonl(args.save_path_name, outputs)\n\n    # Print final summary\n    if total_so_far > 0:\n        print(f\"\\n{'='*60}\")\n        print(f\"FINAL RESULTS: {correct_so_far}/{total_so_far} correct ({100*correct_so_far/total_so_far:.1f}%)\")\n        print(f\"Results saved to: {args.save_path_name}\")\n        print(f\"{'='*60}\")\n\n\nif __name__ == \"__main__\":\n    args = Arguments().parse_args()\n    main(args)\n"
    },
    "source/text_generation/__init__.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:text_generation/__init__.py",
      "sha256": "c4ed1ae1ef9358dd7adc5e209476db4371305db6041ffc3936f039c01938144d",
      "text": "\"\"\"\nText Generation: A unified Python interface for OpenAI, Claude, and Gemini APIs.\n\nThis package provides a simple, unified client for working with multiple LLM providers\nincluding OpenAI, Claude, and Google Gemini, with built-in retry logic and web search integration.\n\nExample:\n    >>> from text_generation import UnifiedLLMClient\n    >>> client = UnifiedLLMClient(provider=\"openai\", model=\"gpt-4o\")\n    >>> response = client.generate(\"Hello, world!\")\n    >>> print(response)\n\n    >>> # Using Gemini\n    >>> client = UnifiedLLMClient(provider=\"gemini\", model=\"gemini-2.5-flash\")\n    >>> response = client.generate(\"Explain quantum computing\")\n\"\"\"\n\n__version__ = \"0.1.0\"\n__author__ = \"Mirac Suzgun\"\n__license__ = \"MIT\"\n\nfrom text_generation.simple_unified_client import UnifiedLLMClient, Provider\n\n__all__ = [\n    \"UnifiedLLMClient\",\n    \"Provider\",\n]\n"
    },
    "source/text_generation/simple_unified_client.py": {
      "origin": "git:5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9:text_generation/simple_unified_client.py",
      "sha256": "9810232b787549e9383c9fdc97a3b286583a7ae79cfe2c05e6d7fd8077e7d6c9",
      "text": "\"\"\"\nSimple Unified Client for OpenAI, Claude, Gemini, and xAI (Grok) APIs\n\nA straightforward, easy-to-use interface for working with OpenAI, Claude, Google Gemini, and xAI (Grok) models.\n\nFeatures:\n- Single class interface for multiple LLM providers\n- Built-in web search integration via Tavily\n- Native web search via provider's built-in tools:\n  - OpenAI: Automatic web search with search-preview models (e.g., gpt-4o-mini-search-preview)\n  - Claude: web_search_20250305 tool via Chat Completions API\n  - Gemini: Google Search grounding via tools\n  - xAI (Grok): web_search tool via Responses API\n- Streaming support\n- Conversation history management\n- Automatic retry logic with 10-second wait between attempts\n- Easy parameter customization (temperature, max_completion_tokens, etc.)\n\"\"\"\n\nimport os\nimport time\nimport logging\nfrom typing import List, Dict, Any, Optional, Union, Iterator\nfrom enum import Enum\n\n# Provider SDKs — imported lazily so only the SDK for the chosen provider is required.\ntry:\n    import openai as _openai_module\n    OPENAI_AVAILABLE = True\nexcept ImportError:\n    _openai_module = None\n    OPENAI_AVAILABLE = False\n\ntry:\n    from anthropic import Anthropic\n    ANTHROPIC_AVAILABLE = True\nexcept ImportError:\n    Anthropic = None\n    ANTHROPIC_AVAILABLE = False\n\ntry:\n    from tavily import TavilyClient\n    TAVILY_AVAILABLE = True\nexcept ImportError:\n    TavilyClient = None\n    TAVILY_AVAILABLE = False\n\ntry:\n    from google import genai\n    from google.genai import types as genai_types\n    from google.genai import errors as genai_errors\n    GEMINI_AVAILABLE = True\nexcept ImportError:\n    GEMINI_AVAILABLE = False\n    genai = None\n    genai_types = None\n    genai_errors = None\n\n\n# Setup logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nclass Provider(str, Enum):\n    \"\"\"Supported LLM providers.\"\"\"\n\n    OPENAI = \"openai\"\n    CLAUDE = \"claude\"\n    GEMINI = \"gemini\"\n    XAI = \"xai\"  # Grok models hosted by xAI\n\n\nclass UnifiedLLMClient:\n    \"\"\"\n    A unified interface for interacting with OpenAI, Claude, Gemini, and xAI (Grok) models.\n\n    This client provides automatic retry logic, web search integration,\n    and seamless conversation management across multiple LLM providers.\n\n    Example:\n        >>> from text_generation import UnifiedLLMClient\n        >>> client = UnifiedLLMClient(provider=\"openai\", model=\"gpt-4o\")\n        >>> response = client.generate(\"Hello, how are you?\")\n        >>> print(response)\n\n        >>> # With Gemini\n        >>> client = UnifiedLLMClient(provider=\"gemini\", model=\"gemini-2.5-flash\")\n        >>> response = client.generate(\"Explain quantum computing\")\n\n        >>> # With custom parameters\n        >>> response = client.generate(\n        ...     \"Explain AI\",\n        ...     temperature=0.9,\n        ...     max_completion_tokens=500\n        ... )\n    \"\"\"\n\n    def __init__(\n        self,\n        provider: Union[str, Provider],\n        model: Optional[str] = None,\n        api_key: Optional[str] = None,\n        base_url: Optional[str] = None,\n        web_search_enabled: bool = False,\n        tavily_api_key: Optional[str] = None,\n        default_web_search: bool = False,\n        web_search_config: Optional[Dict[str, Any]] = None,\n        temperature: float = 0.7,\n        max_completion_tokens: Optional[int] = None,\n        max_retries: int = 3,\n        retry_delay: int = 10,\n    ):\n        \"\"\"\n        Initialize the unified LLM client.\n\n        Args:\n            provider: The LLM provider (\"openai\", \"claude\", \"gemini\", or \"xai\")\n            model: Model name (e.g., \"gpt-4o\", \"claude-sonnet-4-5-20250514\", \"gemini-2.5-flash\", \"grok-4-1\")\n            api_key: API key for the provider (defaults to env vars)\n            base_url: Custom base URL for OpenAI-compatible APIs (e.g., Together AI, Ollama, DeepSeek)\n            web_search_enabled: Enable web search capabilities via Tavily\n            tavily_api_key: Tavily API key for web search\n            default_web_search: Enable native web search via provider's built-in tool\n            web_search_config: Configuration for default web search tool\n                For OpenAI: {\"search_context_size\": \"medium\", \"user_location\": {...}}\n                For Claude: {\"max_uses\": 5, \"allowed_domains\": [...], \"blocked_domains\": [...], \"user_location\": {...}}\n                For Gemini: {\"dynamic_retrieval_threshold\": 0.3} (optional threshold for grounding)\n            temperature: Default sampling temperature (0.0 to 2.0)\n            max_completion_tokens: Default maximum tokens in response\n            max_retries: Maximum number of retry attempts (default: 3)\n            retry_delay: Delay in seconds between retries (default: 10)\n        \"\"\"\n        normalized_provider = provider.lower() if isinstance(provider, str) else provider\n        # Accept \"grok\" as an alias for xAI\n        if isinstance(normalized_provider, str) and normalized_provider == \"grok\":\n            normalized_provider = Provider.XAI.value\n        self.provider = Provider(normalized_provider)\n        self.temperature = temperature\n        self.max_completion_tokens = max_completion_tokens\n        self.base_url = base_url\n        self.web_search_enabled = web_search_enabled\n        self.default_web_search = default_web_search\n        self.web_search_config = web_search_config or {}\n        self.max_retries = max_retries\n        self.retry_delay = retry_delay\n\n        # Set default models\n        if model is None:\n            model = self._get_default_model()\n        self.model = model\n\n        # Initialize provider clients\n        self._init_provider_client(api_key)\n\n        # Initialize web search if enabled\n        self.tavily_client = None\n        if web_search_enabled:\n            if not TAVILY_AVAILABLE:\n                logger.warning(\"Tavily SDK not installed. Web search will be disabled. Install with: pip install tavily-python\")\n            else:\n                tavily_key = tavily_api_key or os.getenv(\"TAVILY_API_KEY\")\n                if tavily_key:\n                    self.tavily_client = TavilyClient(api_key=tavily_key)\n                else:\n                    logger.warning(\"Tavily API key not provided. Web search will be disabled.\")\n\n        # Conversation history\n        self.messages: List[Dict[str, str]] = []\n\n    def _get_default_model(self) -> str:\n        \"\"\"Get the default model for the provider.\"\"\"\n        defaults = {\n            Provider.OPENAI: \"gpt-4o\",\n            Provider.CLAUDE: \"claude-sonnet-4-5-20250514\",\n            Provider.GEMINI: \"gemini-2.5-flash\",\n            Provider.XAI: \"grok-4-1\",\n        }\n        return defaults[self.provider]\n\n    def _init_provider_client(self, api_key: Optional[str]) -> None:\n        \"\"\"Initialize the appropriate provider client.\"\"\"\n        if self.provider == Provider.OPENAI:\n            if not OPENAI_AVAILABLE:\n                raise ImportError(\"OpenAI SDK not installed. Install it with: pip install openai\")\n            key = api_key or os.getenv(\"OPENAI_API_KEY\")\n            if not key and not self.base_url:\n                raise ValueError(\n                    \"OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass api_key parameter.\"\n                )\n            client_kwargs = {}\n            if key:\n                client_kwargs[\"api_key\"] = key\n            if self.base_url:\n                client_kwargs[\"base_url\"] = self.base_url\n            self.openai_client = _openai_module.OpenAI(**client_kwargs)\n\n        elif self.provider == Provider.CLAUDE:\n            if not ANTHROPIC_AVAILABLE:\n                raise ImportError(\"Anthropic SDK not installed. Install it with: pip install anthropic\")\n            key = api_key or os.getenv(\"ANTHROPIC_API_KEY\")\n            if not key:\n                raise ValueError(\n                    \"Anthropic API key is required. Set ANTHROPIC_API_KEY environment variable or pass api_key parameter.\"\n                )\n            self.anthropic_client = Anthropic(api_key=key)\n\n        elif self.provider == Provider.GEMINI:\n            if not GEMINI_AVAILABLE:\n                raise ImportError(\n                    \"Google Gemini SDK not installed. Install it with: pip install google-genai\"\n                )\n            key = api_key or os.getenv(\"GEMINI_API_KEY\") or os.getenv(\"GOOGLE_API_KEY\")\n            if not key:\n                raise ValueError(\n                    \"Gemini API key is required. Set GEMINI_API_KEY or GOOGLE_API_KEY environment variable or pass api_key parameter.\"\n                )\n            self.gemini_client = genai.Client(api_key=key)\n\n        elif self.provider == Provider.XAI:\n            if not OPENAI_AVAILABLE:\n                raise ImportError(\"OpenAI SDK not installed (used for xAI). Install it with: pip install openai\")\n            key = api_key or os.getenv(\"XAI_API_KEY\")\n            if not key:\n                raise ValueError(\n                    \"xAI API key is required. Set XAI_API_KEY environment variable or pass api_key parameter.\"\n                )\n            self.xai_client = _openai_module.OpenAI(api_key=key, base_url=\"https://api.x.ai/v1\")\n\n    def _get_openai_web_search_tool(self) -> Dict[str, Any]:\n        \"\"\"\n        Build OpenAI web search tool configuration.\n        \n        Note: This is for the Responses API. For Chat Completions API,\n        search-preview models (e.g., gpt-4o-mini-search-preview) have automatic\n        web search and don't require tools.\n\n        Returns:\n            OpenAI web search tool dictionary\n        \"\"\"\n        tool = {\n            \"type\": \"web_search_preview\",\n            \"web_search_preview\": {}\n        }\n        \n        # Add search_context_size if provided\n        if \"search_context_size\" in self.web_search_config:\n            tool[\"web_search_preview\"][\"search_context_size\"] = self.web_search_config[\"search_context_size\"]\n        else:\n            tool[\"web_search_preview\"][\"search_context_size\"] = \"medium\"\n        \n        # Add user_location if provided\n        if \"user_location\" in self.web_search_config:\n            user_loc = self.web_search_config[\"user_location\"]\n            # If user_location is already a dict with type, use it as-is\n            # Otherwise, wrap it in the proper structure\n            if isinstance(user_loc, dict) and \"type\" in user_loc:\n                tool[\"web_search_preview\"][\"user_location\"] = user_loc\n            else:\n                tool[\"web_search_preview\"][\"user_location\"] = {\n                    \"type\": \"approximate\",\n                    \"approximate\": user_loc\n                }\n        \n        return tool\n\n    def _get_claude_web_search_tool(self) -> Dict[str, Any]:\n        \"\"\"\n        Build Claude web search tool configuration.\n\n        Returns:\n            Claude web search tool dictionary\n        \"\"\"\n        tool = {\n            \"type\": \"web_search_20250305\",\n            \"name\": \"web_search\"\n        }\n        \n        # Add max_uses if provided\n        if \"max_uses\" in self.web_search_config:\n            tool[\"max_uses\"] = self.web_search_config[\"max_uses\"]\n        else:\n            tool[\"max_uses\"] = 5\n        \n        # Add allowed_domains if provided\n        if \"allowed_domains\" in self.web_search_config:\n            tool[\"allowed_domains\"] = self.web_search_config[\"allowed_domains\"]\n        \n        # Add blocked_domains if provided\n        if \"blocked_domains\" in self.web_search_config:\n            tool[\"blocked_domains\"] = self.web_search_config[\"blocked_domains\"]\n        \n        # Add user_location if provided\n        if \"user_location\" in self.web_search_config:\n            user_loc = self.web_search_config[\"user_location\"]\n            # If user_location is already a dict with type, use it as-is\n            # Otherwise, wrap it in the proper structure\n            if isinstance(user_loc, dict) and \"type\" in user_loc:\n                tool[\"user_location\"] = user_loc\n            else:\n                tool[\"user_location\"] = {\n                    \"type\": \"approximate\",\n                    **user_loc\n                }\n        \n        return tool\n\n    def _get_gemini_grounding_tool(self) -> Any:\n        \"\"\"\n        Build Gemini Google Search grounding tool configuration.\n\n        Returns:\n            Gemini grounding tool for Google Search\n        \"\"\"\n        if not GEMINI_AVAILABLE or genai_types is None:\n            raise ImportError(\"Google Gemini SDK not available\")\n\n        # Build Google Search grounding tool following:\n        # https://ai.google.dev/gemini-api/docs/google-search\n        #\n        # - For current Gemini 3 models, the recommended tool is `google_search`.\n        # - Optionally, callers can configure a dynamic retrieval threshold via\n        #   web_search_config[\"dynamic_retrieval_threshold\"].\n\n        google_search_kwargs: Dict[str, Any] = {}\n\n        # Optional dynamic retrieval configuration (for Gemini 3+ models)\n        if \"dynamic_retrieval_threshold\" in self.web_search_config:\n            threshold = self.web_search_config[\"dynamic_retrieval_threshold\"]\n            google_search_kwargs[\"dynamic_retrieval_config\"] = genai_types.DynamicRetrievalConfig(\n                dynamic_threshold=threshold\n            )\n\n        return genai_types.Tool(\n            google_search=genai_types.GoogleSearch(**google_search_kwargs)\n        )\n\n    def _get_xai_web_search_tool(self) -> Dict[str, Any]:\n        \"\"\"\n        Build xAI (Grok) web search tool configuration.\n\n        Returns:\n            xAI web search tool dictionary\n        \"\"\"\n        tool: Dict[str, Any] = {\"type\": \"web_search\"}\n\n        filters: Dict[str, Any] = {}\n        if \"allowed_domains\" in self.web_search_config:\n            filters[\"allowed_domains\"] = self.web_search_config[\"allowed_domains\"]\n        if \"excluded_domains\" in self.web_search_config:\n            filters[\"excluded_domains\"] = self.web_search_config[\"excluded_domains\"]\n        if self.web_search_config.get(\"enable_image_understanding\"):\n            filters[\"enable_image_understanding\"] = True\n        if filters:\n            tool[\"filters\"] = filters\n\n        return tool\n\n    def _web_search(self, query: str, max_results: int = 5) -> str:\n        \"\"\"\n        Perform web search and return formatted results (Tavily-based).\n\n        Args:\n            query: Search query\n            max_results: Maximum number of results\n\n        Returns:\n            Formatted search results as a string\n        \"\"\"\n        if not self.tavily_client:\n            logger.warning(\"Web search requested but Tavily client not initialized\")\n            return \"\"\n\n        try:\n            response = self.tavily_client.search(\n                query=query,\n                max_results=max_results,\n                search_depth=\"advanced\"\n            )\n\n            results = []\n            for idx, result in enumerate(response.get(\"results\", []), 1):\n                results.append(\n                    f\"{idx}. {result.get('title', 'No title')}\\n\"\n                    f\"   URL: {result.get('url', 'N/A')}\\n\"\n                    f\"   {result.get('content', 'No content')}\\n\"\n                )\n\n            return \"\\n\".join(results) if results else \"No results found.\"\n\n        except Exception as e:\n            logger.error(f\"Web search failed: {str(e)}\")\n            return f\"Web search failed: {str(e)}\"\n\n    def _retry_with_backoff(self, func, *args, **kwargs):\n        \"\"\"\n        Execute a function with retry logic.\n\n        Args:\n            func: Function to execute\n            *args: Positional arguments for the function\n            **kwargs: Keyword arguments for the function\n\n        Returns:\n            Result from the function\n\n        Raises:\n            Exception: If all retry attempts fail\n        \"\"\"\n        last_exception = None\n\n        for attempt in range(self.max_retries):\n            try:\n                return func(*args, **kwargs)\n            except Exception as e:\n                last_exception = e\n                if attempt < self.max_retries - 1:\n                    logger.warning(\n                        f\"Attempt {attempt + 1}/{self.max_retries} failed: {str(e)}. \"\n                        f\"Retrying in {self.retry_delay} seconds...\"\n                    )\n                    time.sleep(self.retry_delay)\n                else:\n                    logger.error(\n                        f\"All {self.max_retries} attempts failed. Last error: {str(e)}\"\n                    )\n\n        # If we get here, all retries failed\n        raise RuntimeError(\n            f\"Failed after {self.max_retries} attempts. Last error: {str(last_exception)}\"\n        ) from last_exception\n\n    def generate(\n        self,\n        prompt: str,\n        use_web_search: bool = False,\n        search_query: Optional[str] = None,\n        use_default_web_search: Optional[bool] = None,\n        system_message: Optional[str] = None,\n        stream: bool = False,\n        temperature: Optional[float] = None,\n        max_completion_tokens: Optional[int] = None,\n        **kwargs: Any\n    ) -> Union[str, Iterator[str]]:\n        \"\"\"\n        Generate a response from the LLM with automatic retry logic.\n\n        Args:\n            prompt: The user prompt\n            use_web_search: Whether to perform web search before generation (Tavily-based)\n            search_query: Custom search query (defaults to prompt)\n            use_default_web_search: Whether to use native web search tool (overrides default_web_search setting)\n            system_message: System message to prepend\n            stream: Enable streaming responses\n            temperature: Sampling temperature (overrides default)\n            max_completion_tokens: Maximum tokens (overrides default)\n            **kwargs: Additional provider-specific parameters (e.g., top_p, frequency_penalty)\n\n        Returns:\n            Generated text response or iterator of response chunks\n\n        Raises:\n            RuntimeError: If all retry attempts fail\n        \"\"\"\n        # Determine if we should use default web search\n        use_native_web_search = use_default_web_search if use_default_web_search is not None else self.default_web_search\n\n        # Handle Tavily-based web search (legacy)\n        search_context = \"\"\n        if use_web_search and self.web_search_enabled:\n            query = search_query or prompt\n            search_context = self._web_search(query)\n            if search_context and not search_context.startswith(\"Web search failed\"):\n                prompt = f\"Web Search Results:\\n{search_context}\\n\\nUser Query: {prompt}\"\n\n        # Build messages\n        messages = []\n        if system_message:\n            messages.append({\"role\": \"system\", \"content\": system_message})\n\n        messages.extend(self.messages)\n        messages.append({\"role\": \"user\", \"content\": prompt})\n\n        # Use provided parameters or fall back to defaults\n        final_temperature = temperature if temperature is not None else self.temperature\n        final_max_completion_tokens = max_completion_tokens if max_completion_tokens is not None else self.max_completion_tokens\n\n        # Generate based on provider with retry logic\n        if self.provider == Provider.OPENAI:\n            return self._retry_with_backoff(\n                self._generate_openai,\n                messages,\n                stream,\n                final_temperature,\n                final_max_completion_tokens,\n                use_native_web_search,\n                **kwargs\n            )\n        elif self.provider == Provider.CLAUDE:\n            return self._retry_with_backoff(\n                self._generate_claude,\n                messages,\n                stream,\n                system_message,\n                final_temperature,\n                final_max_completion_tokens,\n                use_native_web_search,\n                **kwargs\n            )\n        elif self.provider == Provider.GEMINI:\n            return self._retry_with_backoff(\n                self._generate_gemini,\n                messages,\n                stream,\n                system_message,\n                final_temperature,\n                final_max_completion_tokens,\n                use_native_web_search,\n                **kwargs\n            )\n        elif self.provider == Provider.XAI:\n            return self._retry_with_backoff(\n                self._generate_xai,\n                messages,\n                stream,\n                final_temperature,\n                final_max_completion_tokens,\n                use_native_web_search,\n                **kwargs,\n            )\n        else:\n            raise ValueError(f\"Unsupported provider: {self.provider}\")\n\n    def generate_from_messages(\n        self,\n        messages: List[Dict[str, str]],\n        temperature: Optional[float] = None,\n        max_completion_tokens: Optional[int] = None,\n        **kwargs: Any\n    ) -> str:\n        \"\"\"\n        Generate a response from a pre-built message list (no conversation history management).\n\n        This is useful when the caller manages message history externally,\n        e.g., for multi-turn code execution loops.\n\n        Args:\n            messages: List of message dicts with 'role' and 'content' keys.\n            temperature: Sampling temperature (overrides default).\n            max_completion_tokens: Maximum tokens (overrides default).\n            **kwargs: Additional provider-specific parameters.\n\n        Returns:\n            Generated text response.\n        \"\"\"\n        final_temperature = temperature if temperature is not None else self.temperature\n        final_max_tokens = max_completion_tokens if max_completion_tokens is not None else self.max_completion_tokens\n\n        # Extract system message if present (needed for Claude and Gemini)\n        system_message = None\n        for m in messages:\n            if m[\"role\"] == \"system\":\n                system_message = m[\"content\"]\n                break\n\n        if self.provider == Provider.OPENAI:\n            return self._retry_with_backoff(\n                self._generate_openai, messages, False, final_temperature, final_max_tokens, False, **kwargs\n            )\n        elif self.provider == Provider.CLAUDE:\n            return self._retry_with_backoff(\n                self._generate_claude, messages, False, system_message, final_temperature, final_max_tokens, False, **kwargs\n            )\n        elif self.provider == Provider.GEMINI:\n            return self._retry_with_backoff(\n                self._generate_gemini, messages, False, system_message, final_temperature, final_max_tokens, False, **kwargs\n            )\n        elif self.provider == Provider.XAI:\n            return self._retry_with_backoff(\n                self._generate_xai, messages, False, final_temperature, final_max_tokens, False, **kwargs\n            )\n        else:\n            raise ValueError(f\"Unsupported provider: {self.provider}\")\n\n    def _is_search_preview_model(self, model: str) -> bool:\n        \"\"\"\n        Check if the model is a search-preview model that has automatic web search.\n\n        Args:\n            model: Model name\n\n        Returns:\n            True if the model is a search-preview model\n        \"\"\"\n        return model.endswith(\"-search-preview\") or model.endswith(\"-search-api\")\n\n    @staticmethod\n    def _is_openai_reasoning_model(model: str) -> bool:\n        \"\"\"\n        Check if the model is an OpenAI reasoning model that doesn't support\n        temperature, top_p, frequency_penalty, or presence_penalty.\n\n        Covers o-series (o1, o3, o4, ...) and gpt-5 family.\n        \"\"\"\n        name = model.lower()\n        # o1, o3, o4-mini, o3-mini, etc.\n        if name.startswith((\"o1\", \"o3\", \"o4\")):\n            return True\n        # gpt-5, gpt-5.2, gpt-5-mini, etc.\n        if name.startswith(\"gpt-5\"):\n            return True\n        return False\n\n    def _extract_and_log_citations(self, response: Any) -> None:\n        \"\"\"\n        Extract and log URLs from web search citations in the OpenAI response.\n        \n        According to OpenAI docs, citations are in message.annotations with type 'url_citation'.\n        Reference: https://platform.openai.com/docs/guides/tools-web-search\n        \n        Args:\n            response: OpenAI API response object\n        \"\"\"\n        try:\n            urls = []\n            seen_urls = set()  # To avoid duplicates\n            \n            message = response.choices[0].message\n            \n            # Method 1: Check for annotations attribute (standard format)\n            if hasattr(message, 'annotations') and message.annotations:\n                for annotation in message.annotations:\n                    # Handle both object and dict formats\n                    if isinstance(annotation, dict):\n                        if annotation.get('type') == 'url_citation' and 'url' in annotation:\n                            url = annotation['url']\n                            if url not in seen_urls:\n                                seen_urls.add(url)\n                                urls.append({\n                                    'url': url,\n                                    'title': annotation.get('title', 'No title'),\n                                    'start_index': annotation.get('start_index'),\n                                    'end_index': annotation.get('end_index')\n                                })\n                    else:\n                        # Handle object format\n                        annotation_type = getattr(annotation, 'type', None)\n                        if annotation_type == 'url_citation':\n                            url = getattr(annotation, 'url', None)\n                            if url and url not in seen_urls:\n                                seen_urls.add(url)\n                                urls.append({\n                                    'url': url,\n                                    'title': getattr(annotation, 'title', 'No title'),\n                                    'start_index': getattr(annotation, 'start_index', None),\n                                    'end_index': getattr(annotation, 'end_index', None)\n                                })\n            \n            # Method 2: Check response object directly (alternative location)\n            if not urls and hasattr(response, 'citations'):\n                citations = response.citations\n                if citations:\n                    for citation in citations:\n                        if isinstance(citation, dict):\n                            if citation.get('type') == 'url_citation' and 'url' in citation:\n                                url = citation['url']\n                                if url not in seen_urls:\n                                    seen_urls.add(url)\n                                    urls.append({\n                                        'url': url,\n                                        'title': citation.get('title', 'No title'),\n                                        'start_index': citation.get('start_index'),\n                                        'end_index': citation.get('end_index')\n                                    })\n            \n            # Method 3: Try to access raw response dict if available\n            if not urls and hasattr(response, 'model_dump'):\n                try:\n                    response_dict = response.model_dump()\n                    # Navigate through the response structure\n                    if 'choices' in response_dict and len(response_dict['choices']) > 0:\n                        choice = response_dict['choices'][0]\n                        if 'message' in choice:\n                            msg = choice['message']\n                            if 'annotations' in msg:\n                                for annotation in msg['annotations']:\n                                    if annotation.get('type') == 'url_citation' and 'url' in annotation:\n                                        url = annotation['url']\n                                        if url not in seen_urls:\n                                            seen_urls.add(url)\n                                            urls.append({\n                                                'url': url,\n                                                'title': annotation.get('title', 'No title'),\n                                                'start_index': annotation.get('start_index'),\n                                                'end_index': annotation.get('end_index')\n                                            })\n                except Exception:\n                    pass  # If model_dump fails, continue\n            \n            # Log the URLs if found\n            if urls:\n                logger.info(\"=\" * 60)\n                logger.info(\"Web Search URLs Visited:\")\n                logger.info(\"=\" * 60)\n                for idx, url_info in enumerate(urls, 1):\n                    logger.info(f\"{idx}. {url_info['url']}\")\n                    if url_info['title'] and url_info['title'] != 'No title':\n                        logger.info(f\"   Title: {url_info['title']}\")\n                logger.info(\"=\" * 60)\n            else:\n                logger.debug(\"No URL citations found in response (this is normal if no web search was performed)\")\n                \n        except Exception as e:\n            logger.warning(f\"Error extracting citations: {e}\")\n            import traceback\n            logger.debug(traceback.format_exc())\n\n    def _generate_openai(\n        self,\n        messages: List[Dict[str, str]],\n        stream: bool,\n        temperature: float,\n        max_completion_tokens: Optional[int],\n        use_native_web_search: bool = False,\n        **kwargs: Any\n    ) -> Union[str, Iterator[str]]:\n        \"\"\"Generate response using OpenAI.\"\"\"\n        params = {\n            \"model\": self.model,\n            \"messages\": messages,\n            \"stream\": stream,\n        }\n        \n        # Handle web search for OpenAI\n        # Note: For search-preview models (e.g., gpt-4o-mini-search-preview),\n        # web search is automatic and no tools are needed.\n        # For regular models, web search requires the Responses API (not Chat Completions),\n        # so we only enable it for search-preview models.\n        if use_native_web_search:\n            if self._is_search_preview_model(self.model):\n                # Search-preview models have automatic web search, no tools needed\n                logger.info(f\"Using search-preview model {self.model} - web search is automatic\")\n            else:\n                # For regular models, web search via tools requires Responses API, not Chat Completions\n                # We'll log a warning and proceed without tools\n                logger.warning(\n                    f\"Web search requested for model {self.model}, but web search tools \"\n                    f\"require the Responses API (not Chat Completions). \"\n                    f\"To use web search, either use a search-preview model (e.g., gpt-4o-mini-search-preview) \"\n                    f\"or use the Responses API directly.\"\n                )\n                # Don't add tools for regular models in Chat Completions API\n\n        # Add max_completion_tokens if specified\n        if max_completion_tokens is not None:\n            params[\"max_completion_tokens\"] = max_completion_tokens\n\n        # Handle temperature and other parameters based on model type.\n        # Some models (search-preview, reasoning/o-series, gpt-5) don't support\n        # temperature, top_p, frequency_penalty, or presence_penalty.\n        unsupported_sampling = {'temperature', 'top_p', 'frequency_penalty', 'presence_penalty'}\n        if self._is_search_preview_model(self.model) or self._is_openai_reasoning_model(self.model):\n            filtered_kwargs = {k: v for k, v in kwargs.items() if k not in unsupported_sampling}\n            if filtered_kwargs != kwargs:\n                removed = set(kwargs.keys()) - set(filtered_kwargs.keys())\n                if removed:\n                    logger.debug(f\"Removed unsupported parameters for model {self.model}: {removed}\")\n            params.update(filtered_kwargs)\n        else:\n            # For regular models, add temperature (explicit parameter takes precedence over kwargs)\n            params[\"temperature\"] = temperature\n            # Remove temperature from kwargs if present to avoid duplication\n            kwargs_without_temp = {k: v for k, v in kwargs.items() if k != 'temperature'}\n            # Add any additional kwargs (e.g., top_p, frequency_penalty, presence_penalty)\n            params.update(kwargs_without_temp)\n\n        logger.info(f\"Calling OpenAI API with model: {self.model}\")\n        response = self.openai_client.chat.completions.create(**params)\n\n        # Extract and log URLs from citations if web search was used\n        if use_native_web_search or self._is_search_preview_model(self.model):\n            self._extract_and_log_citations(response)\n\n        if stream:\n            def stream_generator() -> Iterator[str]:\n                for chunk in response:\n                    if chunk.choices[0].delta.content:\n                        yield chunk.choices[0].delta.content\n            return stream_generator()\n        else:\n            # Handle response - for server-side tools like web_search_preview,\n            # the API executes them automatically and returns the final content\n            message = response.choices[0].message\n            if message.content:\n                return message.content\n            else:\n                # If no content, return empty string (shouldn't happen with server-side tools)\n                logger.warning(\"Received response with no content\")\n                return \"\"\n\n    def _extract_xai_text(self, response: Any) -> str:\n        \"\"\"Extract text from xAI Responses API response.\"\"\"\n        try:\n            if hasattr(response, \"output_text\") and response.output_text:\n                return response.output_text\n\n            if hasattr(response, \"output\") and response.output:\n                parts = []\n                for item in response.output:\n                    for content in getattr(item, \"content\", []):\n                        if getattr(content, \"type\", None) == \"output_text\":\n                            text = getattr(content, \"text\", None)\n                            if text:\n                                parts.append(text)\n                if parts:\n                    return \"\".join(parts)\n\n            if hasattr(response, \"model_dump\"):\n                data = response.model_dump()\n                outputs = data.get(\"output\", [])\n                parts = []\n                for item in outputs:\n                    for content in item.get(\"content\", []):\n                        if content.get(\"type\") == \"output_text\":\n                            text = content.get(\"text\")\n                            if text:\n                                parts.append(text)\n                if parts:\n                    return \"\".join(parts)\n        except Exception as err:  # pragma: no cover - best-effort parsing\n            logger.debug(f\"Failed to extract xAI text: {err}\")\n\n        logger.warning(\"Received xAI response with no content\")\n        return \"\"\n\n    def _generate_xai(\n        self,\n        messages: List[Dict[str, str]],\n        stream: bool,\n        temperature: float,\n        max_completion_tokens: Optional[int],\n        use_native_web_search: bool = False,\n        **kwargs: Any,\n    ) -> Union[str, Iterator[str]]:\n        \"\"\"Generate response using xAI (Grok) Responses API.\"\"\"\n        params: Dict[str, Any] = {\n            \"model\": self.model,\n            \"input\": messages,\n            \"stream\": stream,\n        }\n\n        tools = []\n\n        if use_native_web_search:\n            tools.append(self._get_xai_web_search_tool())\n            logger.info(\"xAI web search tool enabled\")\n\n        if tools:\n            params[\"tools\"] = tools\n\n        params[\"temperature\"] = temperature\n        if max_completion_tokens is not None:\n            params[\"max_output_tokens\"] = max_completion_tokens\n\n        # Pass through additional kwargs supported by Responses API\n        passthrough_keys = {\n            \"top_p\",\n            \"frequency_penalty\",\n            \"presence_penalty\",\n            \"stop\",\n            \"response_format\",\n        }\n        for key in passthrough_keys:\n            if key in kwargs:\n                params[key] = kwargs[key]\n\n        logger.info(f\"Calling xAI Responses API with model: {self.model}\")\n        response = self.xai_client.responses.create(**params)\n\n        if stream:\n            def stream_generator() -> Iterator[str]:\n                for event in response:\n                    # Stream events may expose output_text or delta content\n                    if hasattr(event, \"output_text\") and event.output_text:\n                        yield event.output_text\n                        continue\n                    if hasattr(event, \"delta\") and event.delta:\n                        delta_output = getattr(event.delta, \"output_text\", None)\n                        if isinstance(delta_output, str):\n                            if delta_output:\n                                yield delta_output\n                            continue\n                        if isinstance(delta_output, list):\n                            for delta_part in delta_output:\n                                if delta_part:\n                                    yield delta_part\n                            continue\n                    if hasattr(event, \"output\") and event.output:\n                        for item in event.output:\n                            for content in getattr(item, \"content\", []):\n                                if getattr(content, \"type\", None) == \"output_text\":\n                                    text = getattr(content, \"text\", None)\n                                    if text:\n                                        yield text\n            return stream_generator()\n\n        return self._extract_xai_text(response)\n\n    def _generate_claude(\n        self,\n        messages: List[Dict[str, str]],\n        stream: bool,\n        system_message: Optional[str],\n        temperature: float,\n        max_completion_tokens: Optional[int],\n        use_native_web_search: bool = False,\n        **kwargs: Any\n    ) -> Union[str, Iterator[str]]:\n        \"\"\"Generate response using Claude.\"\"\"\n        # Claude separates system message from messages\n        filtered_messages = [m for m in messages if m[\"role\"] != \"system\"]\n\n        params = {\n            \"model\": self.model,\n            \"messages\": filtered_messages,\n            \"temperature\": temperature,\n            \"max_tokens\": max_completion_tokens or 4096,\n        }\n\n        # Add system message if provided\n        if system_message:\n            params[\"system\"] = system_message\n\n        # Add web search tool if enabled\n        if use_native_web_search:\n            web_search_tool = self._get_claude_web_search_tool()\n            # Check if tools already exist in kwargs or params\n            if \"tools\" in kwargs:\n                # Create a new list to avoid mutating the original\n                tools = list(kwargs[\"tools\"]) if isinstance(kwargs[\"tools\"], list) else [kwargs[\"tools\"]]\n                tools.append(web_search_tool)\n                kwargs[\"tools\"] = tools\n            elif \"tools\" in params:\n                tools = list(params[\"tools\"]) if isinstance(params[\"tools\"], list) else [params[\"tools\"]]\n                tools.append(web_search_tool)\n                params[\"tools\"] = tools\n            else:\n                params[\"tools\"] = [web_search_tool]\n            logger.info(\"Claude web search tool enabled\")\n\n        # Add any additional kwargs (e.g., top_p, top_k)\n        params.update(kwargs)\n\n        logger.info(f\"Calling Claude API with model: {self.model}\")\n\n        if stream:\n            params[\"stream\"] = True\n            response = self.anthropic_client.messages.create(**params)\n\n            def stream_generator() -> Iterator[str]:\n                for event in response:\n                    # Handle content block deltas with text\n                    if event.type == 'content_block_delta':\n                        if hasattr(event.delta, 'text') and event.delta.text:\n                            yield event.delta.text\n            return stream_generator()\n        else:\n            response = self.anthropic_client.messages.create(**params)\n            # Extract text from content blocks\n            text_parts = []\n            for content_block in response.content:\n                if content_block.type == \"text\":\n                    text_parts.append(content_block.text)\n            return \"\".join(text_parts) if text_parts else \"\"\n\n    def _generate_gemini(\n        self,\n        messages: List[Dict[str, str]],\n        stream: bool,\n        system_message: Optional[str],\n        temperature: float,\n        max_completion_tokens: Optional[int],\n        use_native_web_search: bool = False,\n        **kwargs: Any\n    ) -> Union[str, Iterator[str]]:\n        \"\"\"Generate response using Google Gemini.\"\"\"\n        if not GEMINI_AVAILABLE or genai_types is None:\n            raise ImportError(\"Google Gemini SDK not available\")\n\n        # Build contents for Gemini API\n        # Gemini uses \"user\" and \"model\" roles (not \"assistant\")\n        contents = []\n        for msg in messages:\n            if msg[\"role\"] == \"system\":\n                # System messages are handled separately via system_instruction\n                continue\n            role = \"model\" if msg[\"role\"] == \"assistant\" else \"user\"\n            contents.append(genai_types.Content(role=role, parts=[genai_types.Part(text=msg[\"content\"])]))\n\n        # Build generation config\n        config_params: Dict[str, Any] = {}\n\n        # Add system instruction if provided\n        if system_message:\n            config_params[\"system_instruction\"] = system_message\n\n        # Add temperature (Gemini 2.5+ defaults to 1.0)\n        config_params[\"temperature\"] = temperature\n\n        # Add max_output_tokens if specified\n        if max_completion_tokens is not None:\n            config_params[\"max_output_tokens\"] = max_completion_tokens\n\n        # Add other supported parameters from kwargs\n        supported_params = {\"top_p\", \"top_k\", \"candidate_count\", \"stop_sequences\", \"presence_penalty\", \"frequency_penalty\", \"seed\"}\n        for param in supported_params:\n            if param in kwargs:\n                config_params[param] = kwargs[param]\n\n        # Build tools list if web search is enabled\n        tools = None\n        if use_native_web_search:\n            grounding_tool = self._get_gemini_grounding_tool()\n            tools = [grounding_tool]\n            logger.info(\"Gemini Google Search grounding enabled\")\n\n        # Attach tools directly to the GenerateContentConfig, as in:\n        # https://ai.google.dev/gemini-api/docs/google-search\n        if tools is not None:\n            config_params[\"tools\"] = tools\n\n        # Create generation config\n        config = genai_types.GenerateContentConfig(**config_params)\n\n        logger.info(f\"Calling Gemini API with model: {self.model}\")\n\n        if stream:\n            # Streaming response\n            response_stream = self.gemini_client.models.generate_content_stream(\n                model=self.model,\n                contents=contents,\n                config=config,\n            )\n\n            def stream_generator() -> Iterator[str]:\n                for chunk in response_stream:\n                    if chunk.text:\n                        yield chunk.text\n\n            return stream_generator()\n        else:\n            # Non-streaming response\n            response = self.gemini_client.models.generate_content(\n                model=self.model,\n                contents=contents,\n                config=config,\n            )\n\n            # Extract text from response\n            if response.text:\n                return response.text\n            else:\n                # Check for candidates if text property is empty\n                if response.candidates:\n                    text_parts = []\n                    for candidate in response.candidates:\n                        if candidate.content and candidate.content.parts:\n                            for part in candidate.content.parts:\n                                if hasattr(part, \"text\") and part.text:\n                                    text_parts.append(part.text)\n                    if text_parts:\n                        return \"\".join(text_parts)\n\n                logger.warning(\"Received Gemini response with no content\")\n                return \"\"\n\n    def create_container(self, name: str = \"dc-session\", memory_limit: str = \"4g\") -> str:\n        \"\"\"\n        Create an OpenAI code interpreter container and return its ID.\n\n        Args:\n            name: Container name.\n            memory_limit: Memory limit for the container (e.g., \"4g\").\n\n        Returns:\n            The container ID string.\n        \"\"\"\n        if self.provider != Provider.OPENAI:\n            raise ValueError(\"Code interpreter containers are only supported with the OpenAI provider.\")\n        container = self.openai_client.containers.create(name=name, memory_limit=memory_limit)\n        logger.info(f\"Created code interpreter container: {container.id}\")\n        return container.id\n\n    def generate_with_code_interpreter(\n        self,\n        messages: List[Dict[str, str]],\n        container_id: str,\n        temperature: Optional[float] = None,\n        max_completion_tokens: Optional[int] = None,\n        **kwargs: Any,\n    ) -> str:\n        \"\"\"\n        Generate a response using OpenAI's Responses API with Code Interpreter.\n\n        Code execution happens server-side — no local subprocess needed.\n\n        Args:\n            messages: Conversation messages (system/user/assistant dicts).\n            container_id: The container ID for code interpreter.\n            temperature: Sampling temperature (overrides default).\n            max_completion_tokens: Max tokens (overrides default).\n            **kwargs: Additional parameters (e.g., reasoning_effort).\n\n        Returns:\n            The model's text output.\n        \"\"\"\n        if self.provider != Provider.OPENAI:\n            raise ValueError(\"Code interpreter is only supported with the OpenAI provider.\")\n\n        final_temperature = temperature if temperature is not None else self.temperature\n        final_max_tokens = max_completion_tokens if max_completion_tokens is not None else self.max_completion_tokens\n\n        # Remap Chat Completions kwargs to Responses API format\n        # reasoning_effort=\"medium\" -> reasoning={\"effort\": \"medium\"}\n        if \"reasoning_effort\" in kwargs:\n            kwargs[\"reasoning\"] = {\"effort\": kwargs.pop(\"reasoning_effort\")}\n\n        params: Dict[str, Any] = {\n            \"model\": self.model,\n            \"input\": messages,\n            \"tools\": [{\"type\": \"code_interpreter\", \"container\": container_id}],\n        }\n\n        # Reasoning models don't support temperature\n        if self._is_openai_reasoning_model(self.model):\n            filtered_kwargs = {k: v for k, v in kwargs.items()\n                               if k not in {'temperature', 'top_p', 'frequency_penalty', 'presence_penalty'}}\n            params.update(filtered_kwargs)\n        else:\n            params[\"temperature\"] = final_temperature\n            kwargs_filtered = {k: v for k, v in kwargs.items() if k != 'temperature'}\n            params.update(kwargs_filtered)\n\n        if final_max_tokens is not None:\n            params[\"max_output_tokens\"] = final_max_tokens\n\n        logger.info(f\"Calling OpenAI Responses API (code interpreter) with model: {self.model}\")\n\n        def _call():\n            response = self.openai_client.responses.create(**params)\n            return response.output_text\n\n        return self._retry_with_backoff(_call)\n\n    def generate_with_code_interpreter_claude(\n        self,\n        messages: List[Dict[str, str]],\n        temperature: Optional[float] = None,\n        max_completion_tokens: Optional[int] = None,\n        **kwargs: Any,\n    ) -> str:\n        \"\"\"\n        Generate a response using Claude's native code execution tool.\n\n        Code execution happens server-side in Anthropic's sandboxed environment —\n        no local subprocess or container management needed.\n\n        Pricing note: 1,550 free hours/month per organisation; additional usage\n        at $0.05/hour per container.  Execution is **free** when web_search or\n        web_fetch tools are also included in the same request.\n\n        Args:\n            messages: Conversation messages (system/user/assistant dicts).\n            temperature: Sampling temperature (overrides default).\n            max_completion_tokens: Max tokens (overrides default).\n            **kwargs: Additional Claude parameters.  OpenAI-specific keys such as\n                ``reasoning_effort`` are silently ignored.\n\n        Returns:\n            The model's text output (all text content blocks concatenated).\n        \"\"\"\n        if self.provider != Provider.CLAUDE:\n            raise ValueError(\"Claude code execution is only supported with the Claude provider.\")\n\n        # Drop OpenAI-specific params that Claude doesn't accept\n        kwargs.pop(\"reasoning_effort\", None)\n\n        final_temperature = temperature if temperature is not None else self.temperature\n        final_max_tokens = max_completion_tokens if max_completion_tokens is not None else self.max_completion_tokens\n\n        code_execution_tool = {\"type\": \"code_execution_20250825\", \"name\": \"code_execution\"}\n\n        # Extract system message (if any) to pass separately, matching _generate_claude's convention\n        system_message = next((m[\"content\"] for m in messages if m[\"role\"] == \"system\"), None)\n\n        logger.info(f\"Calling Claude API (code execution tool) with model: {self.model}\")\n\n        def _call():\n            return self._generate_claude(\n                messages=messages,\n                stream=False,\n                system_message=system_message,\n                temperature=final_temperature,\n                max_completion_tokens=final_max_tokens,\n                use_native_web_search=False,\n                tools=[code_execution_tool],\n                **kwargs,\n            )\n\n        return self._retry_with_backoff(_call)\n\n    def chat(\n        self,\n        message: str,\n        use_web_search: bool = False,\n        use_default_web_search: Optional[bool] = None,\n        **kwargs: Any\n    ) -> str:\n        \"\"\"\n        Send a message in a conversational context (maintains history).\n\n        Args:\n            message: The user message\n            use_web_search: Whether to use Tavily-based web search\n            use_default_web_search: Whether to use native web search tool (overrides default_web_search setting)\n            **kwargs: Additional generation parameters (temperature, max_completion_tokens, etc.)\n\n        Returns:\n            Assistant's response\n\n        Example:\n            >>> client.chat(\"My name is Alice\")\n            >>> client.chat(\"What's my name?\")  # Remembers the previous context\n        \"\"\"\n        response = self.generate(\n            message,\n            use_web_search=use_web_search,\n            use_default_web_search=use_default_web_search,\n            **kwargs\n        )\n\n        # Handle streaming response\n        if isinstance(response, str):\n            full_response = response\n        else:\n            full_response = \"\".join(response)\n\n        # Update history\n        self.messages.append({\"role\": \"user\", \"content\": message})\n        self.messages.append({\"role\": \"assistant\", \"content\": full_response})\n\n        return full_response\n\n    def reset_history(self) -> None:\n        \"\"\"Clear the conversation history.\"\"\"\n        self.messages = []\n        logger.info(\"Conversation history cleared\")\n\n    def get_history(self) -> List[Dict[str, str]]:\n        \"\"\"\n        Get the current conversation history.\n\n        Returns:\n            List of message dictionaries with 'role' and 'content'\n        \"\"\"\n        return self.messages.copy()\n\n    def set_history(self, messages: List[Dict[str, str]]) -> None:\n        \"\"\"\n        Set the conversation history.\n\n        Args:\n            messages: List of message dictionaries with 'role' and 'content'\n\n        Example:\n            >>> history = [\n            ...     {\"role\": \"user\", \"content\": \"Hello\"},\n            ...     {\"role\": \"assistant\", \"content\": \"Hi there!\"}\n            ... ]\n            >>> client.set_history(history)\n        \"\"\"\n        self.messages = messages.copy()\n        logger.info(f\"Conversation history set with {len(messages)} messages\")\n\n    @classmethod\n    def from_env(\n        cls,\n        provider: Union[str, Provider],\n        web_search_enabled: bool = False,\n        **kwargs: Any\n    ) -> \"UnifiedLLMClient\":\n        \"\"\"\n        Create a client using environment variables for API keys.\n\n        Expected environment variables:\n        - OPENAI_API_KEY (for OpenAI)\n        - ANTHROPIC_API_KEY (for Claude)\n        - GEMINI_API_KEY or GOOGLE_API_KEY (for Gemini)\n        - XAI_API_KEY (for xAI / Grok)\n        - TAVILY_API_KEY (for Tavily-based web search, optional)\n\n        Args:\n            provider: The LLM provider (\"openai\", \"claude\", \"gemini\", or \"xai\")\n            web_search_enabled: Enable Tavily-based web search\n            **kwargs: Additional client parameters (model, temperature, max_completion_tokens,\n                     default_web_search, web_search_config, etc.)\n\n        Returns:\n            Configured UnifiedLLMClient instance\n\n        Example:\n            >>> client = UnifiedLLMClient.from_env(\n            ...     provider=\"openai\",\n            ...     temperature=0.8,\n            ...     max_completion_tokens=1000,\n            ...     default_web_search=True\n            ... )\n        \"\"\"\n        return cls(\n            provider=provider,\n            web_search_enabled=web_search_enabled,\n            **kwargs\n        )\n"
    }
  }
}