Dynamic Cheatsheet agentic-system analysis

Type: types/agentic-system-analysis-result.md

Run identity

Run state: kb/reports/state/agentic-system-analysis/AAS-2026-09-27-dynamic-cheatsheet-04/run-state.md

Generated review: kb/agentic-systems/reviews/dynamic-cheatsheet.md

Memory analysis report: kb/reports/state/agentic-system-analysis/AAS-2026-09-27-dynamic-cheatsheet-04/memory-report.md

Memory analysis report SHA-256: 39be455f397ecf0998172d975808de78a7815e12408c9117948ef64c77da55ae

Run AAS-2026-09-27-dynamic-cheatsheet-04 analyses Dynamic Cheatsheet at the frontmatter revision. The report and review destinations identify outputs; publication completion is established by run state.

Boundary and evidence

Evidence basis: commit-addressed Python implementation and shipped prompts/documentation inspected on 2026-09-27; no fresh model execution. The intended use is to distinguish accumulated inference-time guidance, its admission and later consumers, from demonstrated learning and warranted knowledge.

The target is the shipped Dynamic Cheatsheet library and benchmark workflow, including all six approach branches, local Python execution, provider-native execution dispatch, benchmark scoring, initialization and resume. It is a memory/context-engineering system at whole-system responsibility: the caller or benchmark supplies problems, templates, model choice and stored state; provider computation is an external dependency. The source allowlist is only https://github.com/suzgunmirac/dynamic-cheatsheet at 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9. The verified access root is /home/zby/llm/commonplace/related-systems/suzgunmirac--dynamic-cheatsheet; its worktree was not evidence.

External provider weights, hidden reasoning, server-side execution isolation, and live dataset service contents are excluded; therefore this analysis cannot establish their fixity, hidden criticism, deployment guarantees or current outcomes. The standalone unified client's unrelated chat/web-search APIs and inactive evaluation utilities are outside the assessed invocation families; the conclusion is not a security assessment of every reusable utility. External papers and figure interpretation are excluded; README performance statements remain attributed claims unless supported separately below. No Commonplace transfer analysis is included.

Source register

source ID	kind	identity/location	revision or capture	evidence layer	inspected scope	citation anchors	access gaps and conclusion prevented
SRC-1	Git	https://github.com/suzgunmirac/dynamic-cheatsheet	5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9	implementation	run_benchmark.py; dynamic_cheatsheet/language_model.py; dynamic_cheatsheet/utils/execute_code.py, evaluation.py and extractor.py; selected text_generation/simple_unified_client.py dispatch, retries and native execution	quoted record anchors below and commit-relative paths	no provider internals or executed probes; wiring cannot establish operation or causal benefit
SRC-2	Git	https://github.com/suzgunmirac/dynamic-cheatsheet	5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9	doctrine/design	README.md opening, quick start and approaches; prompts/generator_prompt.txt; both curator prompts	quoted record anchors below	linked paper excluded; reported effects not independently attributed
SRC-3	Git	https://github.com/suzgunmirac/dynamic-cheatsheet	5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9	observed run	first two rows of results/AIME_2024/gpt-4o_DynamicCheatsheet_Cumulative.jsonl	generated anchors on OBJ-1	historical producing revision/provider configuration unknown; cannot establish current-revision operation or causal effect
SRC-4	Git	https://github.com/suzgunmirac/dynamic-cheatsheet	5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9	reported operation	README.md Performance Improvements paragraph	generated CLM-2 quotation	report without reconstructed experiment design; cannot attribute individual component effect

Shared records

Components

CMP-1 — Generator and curator model endpoint

Source-native identity: LanguageModel wraps a caller-selected provider/model string and the same client serves generator and curator calls. Form: distributed-parametric model behind an API; substrate: external service or caller's compatible local endpoint. Parameter-change conclusion status: uninspected for provider internals; the inspected wrapper issues generation requests, not parameter updates. Exact-version-fixity conclusion status: uninspected because arbitrary model IDs, including aliases, are accepted; source pinning does not pin provider weights. Endpoint-selection conclusion status: wired. Evidence: SRC-1 dynamic_cheatsheet/language_model.py and text_generation/simple_unified_client.py. The host selects temperature, token ceiling and additional API parameters. This does not certify model compliance or opaque internal criticism.

client_kwargs = {"provider": provider, "model": model_id, "max_retries": 3, "retry_delay": 10} --- dynamic_cheatsheet/language_model.py:91-91 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

CMP-2 — Precomputed embedding producer

Source-native identity: question embedding vectors are loaded from CSV, converted to numeric arrays, ordered by dataset input, and consumed by cosine similarity. Form: distributed-parametric producer uninspected; symbolic numeric output OBJ-3. Substrate of producer: uninspected. Parameter-change conclusion status: uninspected; exact-version-fixity conclusion status: uninspected. The inspected runtime does not identify or call a specific embedding model. Evidence: SRC-1 run_benchmark.py and dynamic_cheatsheet/language_model.py. Provider/model identity and generation provenance are needed to infer embedding-space stability beyond the pinned vector bytes.

Operative objects

OBJ-1 — Cumulative cheatsheet

Identity remains cumulative and hybrid cheatsheet content. A process string becomes final_cheatsheet in JSONL, then generator and curator user-message content. Form: natural-language plus potentially formal math/code. Lineage: imported initialization or trace-extracted curator output. Consumer authority: advisory knowledge through the paths BAP-1 and BAP-2. Status: wired; historical mixed content and delivery: observed only for SRC-3. Reasons and examples may survive inside the text, but the curator's separate reasoning is not saved as a dedicated audit record. Whole-sheet replacement can drop content; prior checkpoints remain in rows but are not automatically searched by cumulative mode.

The historical first row records a cheatsheet that teaches a rectangle count of 15 while the row target is 315. The second row's current cheatsheet equals the first row's final cheatsheet and appears in its generator prompt. This establishes retained and delivered target-inconsistent guidance in that historical artifact, not a causal explanation of the next answer.

"final_cheatsheet": "Version: 1.0\n\nSOLUTIONS, IMPLEMENTATION PATTERNS, AND CODE SNIPPETS\n\n\n\nCounting Rectangles in a Regular Polygon:\nTo find the number of rectangles that can be formed inside a regular polygon (e.g., dodecagon), where each side of the rectangle lies on either a side or a diagonal of the polygon, follow these steps:\n1. A rectangle is defined by two pairs of opposite vertices.\n2. Opposite vertices in a regular polygon are evenly spaced around the circle.\n3. Use combinatorics to count the number of valid pairs of opposite vertices.\n\n\nExample: Counting rectangles in a regular dodecagon (12-gon):\n1. A dodecagon has 12 vertices.\n2. Opposite vertices are separated by 6 steps (half the total number of vertices).\n3. To form a rectangle, select 2 pairs of opposite vertices. The number of ways to do this is:\n \[\n \binom{6}{2} = 15\n \]\nThus, the total number of rectangles is 15.\n --- results/AIME_2024/gpt-4o_DynamicCheatsheet_Cumulative.jsonl:1-1 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

"target": "315", "raw_input": "Find the number of rectangles that can be formed inside a fixed regular dodecagon (12-gon) where each side of the rectangle lies on either a side or a diagonal of the dodecagon. The diagram below shows three of those rectangles.", "input_txt": --- results/AIME_2024/gpt-4o_DynamicCheatsheet_Cumulative.jsonl:1-1 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The following generated excerpts locate the carried sheet in the second row’s generator prompt and current-cheatsheet field. A direct comparison of the two commit-addressed JSON rows found exact equality between the first row’s final sheet and the second row’s current sheet, and exact containment of that sheet in the second generator prompt. The target disagreement is the bounded finding; no independent solution of the mathematics was performed.

CHEATSHEET:\n'''\nVersion: 1.0\n\nSOLUTIONS, IMPLEMENTATION PATTERNS, AND CODE SNIPPETS\n\n\n\nCounting Rectangles in a Regular Polygon: --- results/AIME_2024/gpt-4o_DynamicCheatsheet_Cumulative.jsonl:2-2 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

"current_cheatsheet": "Version: 1.0\n\nSOLUTIONS, IMPLEMENTATION PATTERNS, AND CODE SNIPPETS\n\n\n\nCounting Rectangles in a Regular Polygon: --- results/AIME_2024/gpt-4o_DynamicCheatsheet_Cumulative.jsonl:2-2 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Evidence: SRC-1 dynamic_cheatsheet/language_model.py and SRC-3 results/AIME_2024/gpt-4o_DynamicCheatsheet_Cumulative.jsonl. Storage: files and process memory. The observed first-two-row relation supports delivery and mixed content only; current-revision route execution and target truth were not independently established.

OBJ-2 — Prior input/output solutions

Identity: raw input corpus aligned by position with accumulated final_output strings. In-memory lists and JSONL preserve problem-solving trajectories, potentially including local execution output. Forms are prose and any embedded formal expressions/code; raw retention is not itself trace learning. The route RTE-3 delivers all or selected pairs as advisory examples; the route RTE-7 derives guidance from them. The benchmark preserves unsuccessful outputs too. Status: wired. Evidence: SRC-1 run_benchmark.py, dynamic_cheatsheet/language_model.py; see the checkpoint and retrieval quotes below.

OBJ-3 — Precomputed question embeddings

Identity remains the precomputed question vectors. CSV values are parsed into numeric NumPy arrays and reordered by matching question strings to the current dataset order. These imported symbolic access data rank prior solutions; they are not readable memory content or learned parameters updated by this runtime. Storage is files and process memory, not an evidenced vector database. Status: wired. Upstream embedding model, production process, and duplicate-question identity handling are unestablished.

df = pd.read_csv(embeddings_path) questions = df["input"].tolist() embeddings = df["embedding"].apply(ast.literal_eval) embeddings = np.array(embeddings.tolist()) # (N, embedding_dim)

    # Re-order the embeddings based on the order of the dataset inputs
    dataset_inputs = [example["input"] for example in dataset]
    indices = [questions.index(inp) for inp in dataset_inputs]
    embeddings = embeddings[indices]
    questions = dataset_inputs

--- run_benchmark.py:211-220 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Evidence: SRC-1 run_benchmark.py, dynamic_cheatsheet/language_model.py.

OBJ-6 — Retrieval-synthesis cheatsheet

Registered object, distinct from the cumulative sheet's update mechanism. Retained previous guidance and retrieved solution pairs feed a curator before the current answer. Its string is delivered to the generator, returned as final_cheatsheet, persisted by the route RTE-1, and supplied as the next invocation's previous cheatsheet. Natural-language and possible symbolic content; files and process memory; trace-extracted lineage; advisory knowledge authority. Status: wired. Missing-tag fallback adopts the current retrieved-pair text rather than the previous synthesized sheet. Evidence: SRC-1 dynamic_cheatsheet/language_model.py; the synthesis quote below supports this identity and its consumer.

OBJ-4 — Generator solution and final answer

The generator returns a natural-language solution, possible Python snippets and an extracted final answer. The mixed natural-language/symbolic payload lives in strings, step dictionaries and benchmark JSONL. Producer is CMP-1 through RTE-4 or RTE-6; consumers are curator RTE-2, future pair selection RTE-3, and answer evaluation RTE-5. Lineage is model-generated from question, prompt, selected memory and optional code output. Conclusion status: wired. Evidence: SRC-1 dynamic_cheatsheet/language_model.py, run_benchmark.py, dynamic_cheatsheet/utils/extractor.py. An extracted answer is a formatting result, not correctness certification.

OBJ-5 — Per-example evaluation result and counters

A Python boolean from a task-specific evaluator updates correct/total counters and printed scores. Form: symbolic; substrate: process memory/stdout. The JSONL record stores the target and generated output but the shown loop does not attach this boolean to output_dict. Producer RTE-5; immediate consumer is score accounting, not the curator. Conclusion status: wired. Evidence: SRC-1 run_benchmark.py. Evaluation can be recomputed from saved records; that is distinct from a persisted acceptance decision about cheatsheet claims.

Routes

RTE-1 — Sequential benchmark, persistence and resume

Endpoints and owner: operator CLI to dataset loader to LanguageModel. The Python loop owns next-example scheduling; arguments select task, approach, prompts, model and limits. It initializes an empty or imported sheet, restores saved final_cheatsheet and final_output history when requested, optionally shuffles at seed 10, and passes the prefix of questions/embeddings and prior outputs to advanced_generate. State effects: final_output joins history, final_cheatsheet replaces the next iteration's sheet, records are rewritten to JSONL after each example and finally. Immediate return: JSONL plus printed score. Later read-back: next example and explicit continuation consume those retained fields. Delegated visibility: generator/curator see assembled user-message content, not arbitrary saved fields or the dataset target. Selection predicate: approach and sequential index, with resume skipping earlier indices. Invalidation/expiry: a new run resets state unless initialized or resumed; no time expiry in this loop. Activation/effect conclusion status: wired for supplying contexts, uninspected for behavior change caused by them. Implementation conclusion status: wired. Evidence: SRC-1 run_benchmark.py.

generator_outputs_so_far.append(output_dict["final_output"])

    outputs.append({
        "input": current_input,
        "target": original_target,
        "raw_input": original_input,
        **output_dict,
    })
    cheatsheet = output_dict["final_cheatsheet"]
    final_answer = output_dict["final_answer"]

--- run_benchmark.py:272-281 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Load the previous cheatsheet from the last output (if available)

    last_cheatsheet = outputs[-1].get("final_cheatsheet")
    if last_cheatsheet is not None:
        cheatsheet = last_cheatsheet

    generator_outputs_so_far = [output["final_output"] for output in outputs]

--- run_benchmark.py:184-189 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Admission/recovery: an operator supplies an initialization file or prior JSONL path; the program admits loaded text without semantic validation, and resume checks only a selected argument list. There is no human intervention in ordinary iterations. Invalid paths and inconsistent checked arguments raise errors. A crash before an example save loses that example's progress; write_jsonl rewrites the file directly and is not an atomic transaction. The imported sheet and restored records retain whatever guidance and rationale their payloads contain; reconstructing correctness is not part of this path. Open library calls delegate persistence and repeated invocation to the caller. Guidance on imported content is uninspected; theory-builder conditions 1–4 and learning for import alone are inapplicable because this route restores material rather than itself criticizing it. Memory overlays below specify the richer consumers.

Memory-specific overlay from the specialist:

Owner: benchmark driver. Trigger: invocation, each dataset example, or explicit resume path. It optionally imports a text file, builds or restores state, passes current cheatsheet and prior outputs into advanced_generate, appends results and replaces current cheatsheet. It rewrites the accumulated JSONL after evaluation and each completed example. Later consumers are the next approach invocation and resume loader. Status: wired; recovery is last persisted row, with unsaved work lost on failure. No automatic expiry. Immediate return is the approach result; visibility is local process plus caller-selected output files, with no separate delegated reader.

Initialize the cheatsheet

cheatsheet = "(empty)"
if args.initialize_cheatsheet_path is not None:
    with open(args.initialize_cheatsheet_path, "r") as file:
        cheatsheet = file.read()

--- run_benchmark.py:170-174 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Resume checks selected argument values, then skips len(outputs) dataset items. It does not compare no_shuffle, retrieval k, initialization path, curator prompt contents or dataset digest. Restored outputs are joined to newly loaded dataset questions by position. Changed dataset ordering can therefore misassociate prior solutions; reliable resume requires compatible external inputs. Empty checkpoint handling is not guarded before outputs[-1]. The strongest supported claim is a wired recovery mechanism, not invariant replay consistency.

RTE-2 — Cumulative and hybrid curator replacement

This route owns both cumulative write branches. Trigger: model answer in each cumulative refinement round, or one hybrid answer. Producer: model call guided by the cumulative curator template, receiving the prior sheet, current question and generator output. Admission: extract text after an opening cheatsheet tag; missing opening tag falls back to prior sheet. Rejection is format-based only. Retention: same-question next round and/or benchmark checkpoint for next question. Later consumers: generator and next curator. Rollback is manual reuse of previous retained text; no automatic quality rollback or invalidation. Implementation status: wired; guarantee: best effort for semantic quality. Effects beyond delivery are unestablished.

STEP 2: Run the cheatsheet extraction model with the generator output and the current cheatsheet

            cheatsheet_prompt = cheatsheet_template.replace("[[QUESTION]]", input_txt).replace("[[MODEL_ANSWER]]", generator_output).replace("[[PREVIOUS_CHEATSHEET]]", current_cheatsheet)

            cheatsheet_history = [{"role": "user", "content": cheatsheet_prompt}]
            cheatsheet_output = self.generate(
                history=cheatsheet_history,
                temperature=temperature,
                max_tokens=2 * max_tokens,
                allow_code_execution=False,
            )

            # Extract the new cheatsheet from the output (if present); otherwise, return the old cheatsheet
            new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=current_cheatsheet)
            cheatsheet = new_cheatsheet

--- dynamic_cheatsheet/language_model.py:422-435 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The cumulative loop uses max(1, max_num_rounds) and appends prior extracted answers to later-round generator context by default. Hybrid performs a single generator/curator pair even when max_num_rounds is larger; it concatenates cumulative guidance with retrieved examples before generation.

--- Build combined cheatsheet for the generator ---

        combined_cheatsheet = cheatsheet
        if retrieved_section:
            combined_cheatsheet = f"{cheatsheet}\n{retrieved_section}"

        # --- Generator step ---
        generator_prompt = generator_template.replace("[[QUESTION]]", input_txt).replace("[[CHEATSHEET]]", combined_cheatsheet)
        current_cheatsheet = cheatsheet

--- dynamic_cheatsheet/language_model.py:610-617 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Read-back audit: immediate return is answer plus replacement sheet; later generator/curator invocations receive it automatically. Delegated visibility is the user-message payload, not score targets or all checkpoint history. Selection predicate is cumulative/hybrid approach plus available current sheet; invalidation/expiry is whole replacement or caller reset, with no automatic quality withdrawal. Activation/effect conclusion status: uninspected for reliance on particular entries; context delivery conclusion status: wired. Evidence: SRC-1 dynamic_cheatsheet/language_model.py and SRC-2 prompts/curator_prompt_for_dc_cumulative.txt.

Guidance and revision: the cumulative template asks the curator to assess the solution, preserve useful examples, revise old material and identify lessons. The model proposes and judges replacement; extraction decides admission and can reject only by falling back on missing opening tags. Humans choose inputs/template but do not approve each change. Answer oracle access is inapplicable to this curator call: dataset targets and answer-score results are not supplied. Operating modes are open library calls and bounded benchmark sequences. The operative retained material is the sheet itself, including any reasons/examples the model keeps; omitted rationale is not reconstructed from old criticisms.

Theory-builder conditions 1–4: condition 1 (localized content) observed in SRC-3 and wired as explicit whole-sheet text in SRC-1; condition 2 (consumption of what the theory says) uninspected, since delivery alone is insufficient; condition 3 (content-directed criticism) claimed in SRC-2, without a retained named proposition, result and blame assignment; condition 4 (criticism shaping the next round) uninspected, because replacement/delivery does not establish that the replacement resulted from such criticism. Addressability: whole sheet and text entries/formulas are individually readable/editable, but stable entry identities and structured scope conditions are not enforced. Persistence: cross-problem sheet delivery observed historically, within-problem refinement and cross-run restoration wired. Learning conclusion status: claimed for README improvement, uninspected for attribution to criticism. Reflection conclusion status: uninspected for a two-way causal self-representation and criticism of method texts. Autonomy conclusion status: wired for proposal call, extraction/admission and persistence once configured; theory-builder autonomy uninspected because its criticism and iteration conditions remain unresolved. These statuses do not deny inaccessible model processing.

RTE-3 — Prior-pair selection and generator context assembly

This route remains the family owning selection and delivery; the route RTE-7 specifies its distinct synthesis branch. Full history zips corpus inputs with every previous output and supplies all pairs. Retrieval branches rank prior input vectors against the current input vector, take configured top k (benchmark default 3), and reverse display order so the most similar pair is nearest the question. Raw retrieval delivers these pairs directly; hybrid adds them to the whole cumulative sheet; retrieval synthesis first passes them to a curator. Trigger: new question. Persistence: source pairs and returned context strings in benchmark JSONL; immediate return: generator result. No model memory request, TTL, or automatic withdrawal. Status: wired. No token-aware trimming or relevance threshold is applied; k is a count budget, not a token budget. Evidence: SRC-1 dynamic_cheatsheet/language_model.py.

curated_cheatsheet = "### PREVIOUS SOLUTIONS (START)\n\n" for i, (previous_input_txt, previous_output_txt) in enumerate(zip(original_input_corpus, generator_outputs_so_far)): 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" curated_cheatsheet += "#### PREVIOUS SOLUTIONS (END)" --- dynamic_cheatsheet/language_model.py:463-466 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Retrieve the most similar k input-output pairs from the previous inputs and outputs

        if len(prev_original_input_embeddings) > 0:
            similarities = cosine_similarity([current_original_input_embedding], prev_original_input_embeddings)
            top_k_indices = np.argsort(similarities[0])[::-1][:retrieve_top_k]
            top_k_original_inputs = [original_input_corpus[i] for i in top_k_indices]
            top_k_original_outputs = [generator_outputs_so_far[i] for i in top_k_indices]
            top_k_similar_values = similarities[0][top_k_indices]

--- dynamic_cheatsheet/language_model.py:508-514 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Read-back audit: later consumers are generator directly, or curator then generator through RTE-7. Delegated visibility is selected pairs/assembled context, not the whole backing JSONL. Selection predicate and budget are prior-pair availability, all-history or cosine top-k; expiry is caller reset or sheet replacement. Immediate return is generated output plus assembled context fields. Activation/effect conclusion status: uninspected beyond wired delivery. Selection changes context but admits no new validated proposition. Guidance, theory-builder conditions 1–4, learning, reflection and autonomy for pure transport/selection are inapplicable; the distinct proposal route is RTE-7. Retrieval's relevance ranking has no correctness veto. Evidence: SRC-1 dynamic_cheatsheet/language_model.py.

RTE-7 — Retrieval-synthesis write and delivery branch

Registered route subordinate to the selection family RTE-3, without reassigning its identity. Trigger: each retrieval-synthesis question. Producer/selector: curator LLM, guided by the retrieval-synthesis template, consuming selected old solutions, previous synthesized sheet and next question. It creates the object OBJ-6 before answering. Curator generation is capped at twice the generator token budget and uses no local execution. Admission uses the same extractor; fallback is retrieved examples. The generator immediately receives the derived sheet, and the driver retains it for the next curator. Status: wired. This is an online trace-fed write with a later model consumer even though the new question has not yet been solved. No separate approval, automatic semantic rejection or quality rollback. Evidence: SRC-1 dynamic_cheatsheet/language_model.py; guidance: SRC-2 prompts/curator_prompt_for_dc_retrieval_synthesis.txt.

For RetrievalSynthesis, run the curator to synthesize a better cheatsheet

        previous_cheatsheet = cheatsheet
        if approach_name == "DynamicCheatsheet_RetrievalSynthesis":
            cheatsheet_prompt = cheatsheet_template.replace("[[PREVIOUS_INPUT_OUTPUT_PAIRS]]", curated_cheatsheet)
            cheatsheet_prompt = cheatsheet_prompt.replace("[[NEXT_INPUT]]", input_txt)
            cheatsheet_prompt = cheatsheet_prompt.replace("[[PREVIOUS_CHEATSHEET]]", previous_cheatsheet)
            cheatsheet_history = [{"role": "user", "content": cheatsheet_prompt}]
            cheatsheet_output = self.generate(
                history=cheatsheet_history,
                temperature=temperature,
                max_tokens=2 * max_tokens,
                allow_code_execution=False,
            )
            new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=curated_cheatsheet)
            curated_cheatsheet = new_cheatsheet

--- dynamic_cheatsheet/language_model.py:530-544 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Read-back audit: immediate return is the generator result and derived sheet; the generator receives the just-created sheet and the next curator receives the retained sheet. Delegated visibility is selected prior pairs, previous sheet and upcoming question. Selection predicate is retrieval-synthesis mode; a model judgment selects/transforms content after cosine selection. Expiry is whole replacement or caller reset, with no TTL. Activation/effect conclusion status: uninspected for actual behavioral dependence; delivery conclusion status: wired. Recovery from missing tags uses current retrieved pairs, not the old synthesized sheet.

Guidance and admission: the retrieval template asks for improved strategies, new meta-strategies, correction and replacement of worse approaches. Model proposes and semantically selects text; extractor admits it. Humans select the template and questions, with no per-update veto. Ground-truth oracle access is inapplicable to the model call; only previous generated solutions and current question are supplied. Operating modes are bounded sequences or externally supplied library calls. Retention keeps the derived guidance and whatever reasons survive inside it, not a separate criticism ledger.

Theory-builder conditions 1–4: condition 1 (localized content) wired as an inspectable whole text; condition 2 (consumption) uninspected beyond delivery; condition 3 (content-directed criticism) claimed by the template, with named challenged claim, result and blame uninspected; condition 4 (iteration of criticism) uninspected despite wired next-curator reuse. Addressability is whole-sheet/entry text, not validated persistent claim identities. Persistence reaches cross-problem and explicit checkpoint restoration in code. Learning conclusion status: uninspected for attributable improved capacity; source improvement claim remains CLM-1. Reflection conclusion status: uninspected; a sheet about problem-solving is not sufficient self-representation evidence. Autonomy conclusion status: wired for configured propose/extract/retain operations; autonomous theory-builder qualifier uninspected. Evidence: SRC-1 dynamic_cheatsheet/language_model.py, SRC-2 prompts/curator_prompt_for_dc_retrieval_synthesis.txt.

RTE-4 — Generation with optional local Python feedback

Owner and endpoints: LanguageModel.generate sends supplied history to the provider, extracts output, and when enabled recognizes a code block followed by EXECUTE CODE!. The helper runs the first Python block in a temporary file using host python3. Output or errors are wrapped as model-visible text; recursion appends assistant output and a user continuation request. Context effects are cumulative within this call; durable retention occurs only when the enclosing approach/benchmark stores final_output. Immediate return: accumulated text. Later read-back: via OBJ-2 and curator routes, not a separate executor memory store. Delegated visibility: the provider sees assembled history and local output; subprocess receives code and inherits host environment. Selection: flag present, pre-flag text ending in a closing fence, and allow_code_execution. Expiry: temporary file is removed; subprocess timeout requests kill. Activation: code execution and returned feedback are wired, model use of feedback uninspected. Implementation conclusion status: wired. Evidence: SRC-1 dynamic_cheatsheet/language_model.py, dynamic_cheatsheet/utils/execute_code.py.

process = Popen(["python3", temp_file.name], stdout=PIPE, stderr=PIPE) stdout, stderr = process.communicate(timeout=timeout) --- dynamic_cheatsheet/utils/execute_code.py:83-84 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

If we haven't exceeded the max depth, prompt the model to continue

            if current_depth <= max_depth_num_rounds:

--- dynamic_cheatsheet/language_model.py:263-264 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

except TimeoutExpired: process.kill() captured_output = "Execution took too long, aborting..." --- dynamic_cheatsheet/utils/execute_code.py:94-96 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Capability surface is unrestricted Python under the caller's OS grant; runtime Boolean allow_code_execution gates this local generator route. There is no inspected sandbox enforcement point in the helper: the temporary file and three-second wait limit are not an isolation envelope. The program executes before checking whether to recurse. The depth comparison controls continuation after execution, so it does not warrant the prompt's stronger claim that no further execution will occur on the last round. The benchmark's max_num_rounds controls cumulative answer/curator rounds; it is not passed as generate's max_depth_num_rounds. Local and provider paths have different owners and limits.

Revision admission: model proposes code; string checks admit it and host execution determines its effects. No human approval step is wired here. Recovery reports errors/timeouts but cannot undo arbitrary side effects. Decision roles: the model chooses code and reasoning; deterministic code selects execution and recursion. Answer oracle is inapplicable to executing arbitrary code: output checks computations the code actually expresses, not necessarily the original problem. Operating modes: open library generation and bounded benchmark examples. Guidance: generator prompt requests checking assumptions, calculations and alternative methods after errors; retained solution text may state hypotheses and criticism, but hidden model reasoning is uninspected. Theory-builder conditions 1–4: condition 1 afforded by explicit solution text; condition 2 uninspected for dependence on that text; condition 3 claimed by verification instructions and afforded by feedback, not established for a named criticized proposition; condition 4 uninspected for retained criticism shaping a next round. Learning: uninspected. Addressability: text/code blocks are inspectable as units, with no mandatory proposition-level revision protocol. Persistence: within invocation and via parent records across problems. Reflection: uninspected; code feedback about a problem is not by itself a self-representation of the runtime. Autonomy: computation performs generation/execution/continuation once configured, but this does not establish autonomous theory-building.

Memory-specific overlay from the specialist:

Keep the seed's generation/execution identity. For local execution the returned generator text explicitly concatenates code and execution output. The retained solutions and curator input can therefore include actual tool traces, not just model descriptions of tool use. Status: wired. The recursive continuation history stays within this question; durable distilled read-back occurs only if its returned output then enters the routes RTE-2 or RTE-7. Provider-native alternatives return textual output; OpenAI exposes response.output_text, and Claude delegates to its text generation path. Do not infer that all provider tool messages or persistent container contents reach the curator. Provider internals remain excluded. Evidence: SRC-1 dynamic_cheatsheet/language_model.py, text_generation/simple_unified_client.py.

current_output = f"{output_prefix}\n{code_execution_flag}\n\n{executed_code}" final_output = f"{final_output}\n\n{current_output}".strip() --- dynamic_cheatsheet/language_model.py:260-261 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

RTE-5 — Post-generation answer evaluation

Endpoints: extracted answer OBJ-4 and dataset target/input to task-specific Python evaluators, then OBJ-5 counters. Owner: benchmark loop. Game of 24 evaluates an expression and checks number usage; AIME normalizes a small punctuation set then exact-matches target; multiple-choice uses letter/text matching; equation balancing compares number strings and evaluates arithmetic against the target. Immediate return: boolean, counter update and printed accuracy. Later read-back: no scoring result is passed to subsequent generation/curation in this loop. Delegated visibility: target remains in benchmark storage/printing, not the advanced_generate argument list. Selection: task name. Expiry: counters reset each process; resumed scores count new examples. Activation/effect: wired to score counters only. Implementation conclusion status: wired. Evidence: SRC-1 run_benchmark.py, dynamic_cheatsheet/utils/evaluation.py.

if result: correct_so_far += 1 total_so_far += 1 --- run_benchmark.py:301-303 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Get the value of the expression using eval

    value = eval(clean_output)
    if not (abs(value - 24) < 1e-3):
        return False

--- dynamic_cheatsheet/utils/evaluation.py:59-62 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

output = remove_punctuation(output) output = convert_newline_to_space(output) if target == output: return True return False --- dynamic_cheatsheet/utils/evaluation.py:108-112 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Answer oracle access: dataset target supplied by dataset authors, used for AIME, multiple-choice and equation matching; Game of 24 uses an executable task predicate plus source numbers. The oracle is evaluator-side, after cheatsheet update. Diagnosis and pass/fail are computational; operator selects dataset/evaluator and can stop a run. Rejection ability: false means not counted correct, not rejection of cheatsheet text or future solution-pair reuse. No successor candidate selection follows the score. Guidance admission and theory-builder conditions are inapplicable to score accounting alone. Guarantee strength: protocol for scoring branches, not a proof of explanation truth or transfer. Python eval in arithmetic evaluators is an alternate execution surface and is not covered by allow_code_execution on generation. The actual deployment isolation is inherited and uninspected.

Memory-specific overlay from the specialist:

Keep the evaluation identity. The benchmark evaluates the extracted answer against its target after retaining the new cheatsheet in process state. It updates printed aggregate scores and then checkpoints outputs. Targets are written to JSONL but not passed as ground truth to the curator. Failure does not roll back the cheatsheet or remove its solution from retrieval. Status: wired. Evaluation measures answer correctness, not dependence on recalled memory. Evidence: SRC-1 run_benchmark.py; retention ordering is shown under the route RTE-1.

RTE-6 — Provider-native code execution

Endpoints: use_code_interpreter plus stored container/sentinel routes generation to provider APIs. OpenAI creates one named container per benchmark and sends its ID in Responses code_interpreter tools; Claude uses a native execution tool per request and a sentinel locally. Owner of internal tool scheduling, execution and limits: provider. Local wrapper returns provider text immediately and bypasses the local recursion. Immediate return: output text. Later read-back: text can enter ordinary saved solution memory; OpenAI container is reused within the benchmark, but its opaque internal state and expiry are outside this analysis. Delegated visibility: submitted messages and provider container, not separately inspected tool traces. Selection predicate: both use_code_interpreter and self.container_id; advanced_generate disables local execution when the flag is set, even if no container exists. Invalidation/expiry: provider-owned and uninspected. Activation/effect: dispatch wired, remote execution uninspected. Evidence: SRC-1 dynamic_cheatsheet/language_model.py, text_generation/simple_unified_client.py.

Code interpreter path: handled server-side by the provider

    if use_code_interpreter and self.container_id:

--- dynamic_cheatsheet/language_model.py:212-213 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

params: Dict[str, Any] = { "model": self.model, "input": messages, "tools": [{"type": "code_interpreter", "container": container_id}], } --- text_generation/simple_unified_client.py:1128-1132 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Capabilities/grants: provider tool request is authorized by caller configuration; remote isolation is an external contract, not a local guarantee. Unsupported provider container creation raises. Remote retries can repeat a failed API attempt; exactly-once remote effects are not established. Theory-route fields are inapplicable to dispatch alone; opaque provider reasoning and revision mechanisms remain uninspected. OpenAI and Claude are alternate paths, so local timeout/depth guarantees cannot be generalized to them.

Claims

CLM-1 — Inference-time improvement without labels

Claimed operation: the README says black-box models accumulate strategies without modifying underlying parameters, and improve without ground-truth labels or human feedback. Conclusion status: claimed. Evidence: SRC-2 README.md. RTE-2 and RTE-3 wire label-free memory construction; RTE-5 separately scores with targets. Thus label-free memory does not mean evaluation has no oracle. Parameter invariance inside provider internals is uninspected (CMP-1).

• Zero-Shot Learning: Improves performance without ground-truth labels or human feedback --- README.md:20-20 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

CLM-2 — Large benchmark performance gains

Claimed operation: README reports AIME, Game of 24, arithmetic and knowledge-task gains. Conclusion status: claimed. Evidence: SRC-4 README.md. This analysis does not reconstruct experiment assignment, exact model state, sample ordering or controlled ablations, so it does not attribute a component effect to the cheatsheet or to criticism of its content.

• Puzzles: GPT-4o's success rate on Game of 24 increased from approximately 10% to 99% after discovering and reusing Python-based solutions --- README.md:26-26 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

CLM-3 — Correctness-aware curation

Claimed operation: curator should assess correctness, preserve useful strategies and retain tested/proven solutions. Conclusion status: claimed. Evidence: SRC-2 prompts/curator_prompt_for_dc_cumulative.txt; update implementation is RTE-2. The instruction establishes a model judgment request; it does not establish executed proof, independent validation, or an observed claim-level criticism.

The broader generated quotation on CLM-4 supplies the shared prompt evidence.

CLM-4 — Curator quality and maintenance promises

Registered claim. Source claim status: claimed. The cumulative template requests correctness assessment, removal of repetitions, retained examples, counts and prioritization. The retrieval template similarly requests duplicate removal, new meta-strategies and replacement of worse approaches. The update route is wired, while successful semantic deduplication and count-based promotion remain unverified prompt intentions. Counts and Q references are text; no program parses them to select entries. The default benchmark curator template is literally (empty) unless the operator supplies a path, so supported curated behavior is conditional on selecting a real template. Non-None checking does not reject this default.

• 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.
• Ensure practicality and comprehensiveness: Provide critical and informative examples, as well as efficient code snippets and actionable guidelines.

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. Always aim to preserve and keep correct, useful, and illustrative solutions and strategies for future cheatsheets. --- prompts/curator_prompt_for_dc_cumulative.txt:15-18 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

1. Implement a Usage Counter
2. Each entry must include a usage count: Increase the count every time a strategy is successfully used in problem-solving.
3. Use the count to prioritize frequently used solutions over rarely applied ones. --- prompts/curator_prompt_for_dc_cumulative.txt:60-62 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

N.B. Make sure that all information related to the cheatsheet is wrapped inside the block. The cheatsheet can be as long as circa 2000-2500 words. --- prompts/curator_prompt_for_dc_cumulative.txt:128-128 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

• Introduce new meta-strategies based on recent problem-solving experiences. --- prompts/curator_prompt_for_dc_retrieval_synthesis.txt:21-21 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The request for new meta-strategies supports claimed synthesize under the controlled definition of creating a claim absent from inputs. The call wiring proves transformation and retention, but not this semantic property. The selected historical rows were inspected for stored content and delivery; no input-to-output proposition comparison established that the curator introduced a claim absent from all supplied material. Rephrasing, combining or generalizing labels alone cannot discharge that requirement. This limits the synthesis classification without weakening the independently wired trace-learning finding.

CLM-5 — Evidence limit on memory-dependent benefit

Registered claim. Evidence conclusion status: uninspected for causal dependence on memory. The two selected native result rows show text retention/delivery and target disagreement; they do not intervene on recalled content. README performance statements remain reported claims; external paper and full experiment designs are excluded. Thus faithfulness_tested is not-determinable, not a fabricated negative over all repository artifacts. Evidence: SRC-2 README.md; SRC-3 selected rows cited under the object OBJ-1.

Evidenced absences

ABS-1 — No semantic admission gate on cheatsheet extraction

Registered bounded absence. Conclusion status: absent within extractor and callers. Opening-tag extraction accepts any following string, including empty content or content without a closing tag. There is no memory schema, correctness test, human review or entry-level admission check here. Missing opening tag retains the caller's fallback. This does not deny that a curator may perform useful self-criticism; it denies an independently enforced admission guarantee.

Bounded absence search: at the frozen revision, executed git --no-replace-objects -C related-systems/suzgunmirac--dynamic-cheatsheet grep -n -E 'extract_cheatsheet|new_cheatsheet|final_cheatsheet|eval_|valid|reject|approv|correct' 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9 -- dynamic_cheatsheet/utils/extractor.py dynamic_cheatsheet/language_model.py run_benchmark.py. The search returned the extractor, all three curator admission sites, checkpoint assignment and answer-evaluation calls. The complete extractor body and the surrounding caller/benchmark control flow were also read from commit-addressed blobs, so the finding is not based solely on missing keyword hits. Those caller paths unconditionally use extracted or fallback text; benchmark correctness evaluation follows replacement and does not branch into rollback. The absence is limited to these inspected admission paths, not provider internals or all possible custom caller code.

response = response.strip() # (content) if "" in response: try: txt = response.split("")[1].strip() txt = txt.split("")[0].strip() return txt except: return old_cheatsheet else: return old_cheatsheet --- dynamic_cheatsheet/utils/extractor.py:76-86 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Evidence: SRC-1 dynamic_cheatsheet/utils/extractor.py, dynamic_cheatsheet/language_model.py, run_benchmark.py.

Behavioral-authority paths

BAP-1 — Advisory context at generator

Owner: generator prompt assembly. Question plus whole cheatsheet, full history or selected solutions are inserted into one user message. Authority: knowledge; implementation status: wired. The static template instructs use of applicable strategies, but retained content is presented as reference material rather than a new enforced policy. No claim of actual activation follows from its presence. Evidence: SRC-1 dynamic_cheatsheet/language_model.py; SRC-2 prompts/generator_prompt.txt.

• Carefully analyze both the question and cheatsheet before starting
• Search for and identify any applicable patterns, strategies, or examples within the cheatsheet
• Create a structured approach to solving the problem at hand
• Review and document any limitations in the provided reference materials --- prompts/generator_prompt.txt:11-14 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Consumer: generator CMP-1. Channel: user-message context. Force: advisory. Horizon: current answer, with later answers reached through repeated assembly. Evidence limitation: presence does not prove activation.

BAP-2 — Advisory retained context at curator

Owner: curator prompt assembly. Prior cheatsheet and solution traces are supplied through a user message to propose replacement guidance. Authority: knowledge; implementation status: wired. The source templates request criticism of correctness and preservation of useful reasons/examples, but no separately stored rejection rationale or validated per-entry identity is required. Evidence: SRC-1 dynamic_cheatsheet/language_model.py; SRC-2 curator templates; see the routes RTE-2 and RTE-7.

Consumer: curator CMP-1. Channel: user-message context. Force: advisory. Horizon: one replacement proposal, with retained output supplied in later updates. Evidence limitation: prompt instruction is not an independently checked criticism outcome.

BAP-3 — Execution admission

Consumer: host Python process or selected provider execution service. Channel: model text parsed for local execution, or provider tools request. Force: permissive dispatch followed by enforcing program execution conditions. Horizon: one call, with possible external side effects. Conclusion status: wired for the local wrapper; provider operation uninspected. Evidence: SRC-1 dynamic_cheatsheet/language_model.py, dynamic_cheatsheet/utils/execute_code.py, text_generation/simple_unified_client.py; routes RTE-4 and RTE-6. This is operational authority, not epistemic endorsement of generated code.

BAP-4 — Benchmark score accounting

Consumer: correct/total counters and operator reading stdout. Channel: task evaluator boolean. Force: ranking/accounting, not a veto on memory. Horizon: the processed dataset prefix/new resumed segment. Conclusion status: wired. Evidence: SRC-1 run_benchmark.py; route RTE-5. Score validity is bounded by the selected evaluator and data; it does not license every explanation or reusable strategy.

Runtime account

Ordinary invocation: operator starts run_benchmark.py with model/task/approach and templates. RTE-1 creates the client, loads task inputs and targets, initializes/reloads memory, optionally aligns question embeddings and iterates. A cumulative call supplies OBJ-1 to a generator, obtains OBJ-4 through RTE-4 or RTE-6, then supplies question/solution/current sheet to the curator through RTE-2. The new sheet and answer history are admitted before RTE-5 evaluates the answer. JSONL is saved and the loop advances. The Python loop owns ordering; CMP-1 chooses solution and replacement content. Human contribution is configuration, source prompts and problems, with no per-update veto. Terminal product is answer records, final retained sheet and printed benchmark summary.

Material alternates: default has no accumulated cheatsheet; FullHistoryAppending formats all past pairs; Dynamic_Retrieval selects top-k pairs; RetrievalSynthesis adds a curator before generation and uses previous sheet; CumulativeRetrieval combines cumulative guidance and selected examples then updates the cumulative sheet. Direct advanced_generate callers own persistence, inputs, embeddings and repeated calls; generate can be called without the memory workflow. Provider-native execution bypasses the local helper and has separate limits. The generic client's standalone chat/web-search APIs are unassessed and supply no all-client guarantee here.

Forcing cases statically traced:

1. Empty first history: full-history/retrieval construct empty context; cumulative still runs its curator after answering. Empty provider output becomes placeholder text; no correctness gate follows from that fallback.
2. Missing opening curator delimiter: extractor returns the old cumulative sheet or retrieved-pair fallback in retrieval synthesis; an opening delimiter without a closing delimiter can admit the remaining text. This is formatting recovery, not rejection of incorrect content. Validly delimited but incorrect content remains a model judgment issue.
3. Execution limits: local code runs before continuation-depth comparison; disabling generator execution does not disable evaluator eval. Native-tool dispatch has a different enforcement owner.
4. Resume: path and selected argument comparisons gate loading; restored last sheet and all final_output fields become future context. Crash recovery is per saved example, not atomic, and resumed score counters start anew.

Guarantees are scoped: branch selection and argument errors are code protocols owned by Python; model adherence to verification, concise memory and correct answers is best effort. Local timeout covers waiting on one subprocess; it does not establish an OS isolation boundary or all-descendant termination. API retry counts cover wrapper attempts, not exactly-once server work. No deployed grant set was inspected, so capabilities are stated separately from actual credentials/permissions.

Operating modes: bounded benchmark curricula plus open caller-supplied library requests. Benchmark scoring has answer-oracle access; curator proposal/admission does not receive that oracle or its score. The curator both proposes and semantically judges text under supplied instructions; extraction/admission is deterministic and cannot semantically veto it. Improvement trigger is each answer or next retrieval-synthesis question, not a failed benchmark score. No production-machinery revision route is included in the ordinary workflow; arbitrary local code can have external effects, whose concrete use is uninspected.

No dynamic check planned. Considered fresh benchmark execution, malformed-curator probe, local execution-depth probe and resume fixture: static branch inspection suffices to establish the implementation claims, while model runs would require providers/credentials and would not by themselves isolate memory benefit. No test execution outcome or absence claim is inferred from this choice.

Lens scoping

Memory/context scope

Full depth. Trigger evidence: SRC-1 and SRC-2, central retained objects OBJ-1, OBJ-2 and OBJ-3 and routes RTE-1, RTE-2 and RTE-3. Frozen input fixes all six approaches, initialization/resume and their actual consumers; excludes provider internals and static prompts from accumulated-memory classification. The specialist checks seeds, proposes any needed splits and owns the profile.

Epistemic scope

Full depth. Trigger evidence: CLM-1, CLM-2 and CLM-3; candidate solutions OBJ-4 and derived strategies OBJ-1, selection through OBJ-3, model correctness assessment, code feedback and score authority in RTE-2, RTE-3, RTE-4 and RTE-5. The question is which checks admit reliance on what content, and whether checking an answer controls future memory. Excluded provider internals prevent conclusions about hidden criticism; performance-design evidence is not reconstructed.

Lens outputs

Memory/context lens

The reusable state is plain cheatsheet text and prior input/output solutions, not a trained memory model. Cumulative mode rewrites the complete cheatsheet after every round; the benchmark carries it into the next question. Retrieval synthesis instead rewrites guidance before answering, using selected earlier solutions, the previous cheatsheet and the upcoming question. Raw retrieval and full history deliver earlier solutions without a learned transformation. These mechanisms are wired through the routes below.

Selection has two stages: code may pick previous examples by embedding similarity, then a curator may select and rewrite their useful content. The generator receives the resulting material in a user message as reference guidance. Context delivery is wired; reliable activation and benefit are not established. Full-history delivery has no input-token budget. Cumulative length is a prompt request, while output generation has a token cap. Curator output admission is delimiter extraction, not a correctness gate.

Write side

Automatic acquisition retains final model outputs from every approach. The default branch supplies (empty) and returns no cheatsheet; its raw saved outputs do not by themselves qualify as trace learning. Full history and raw retrieval automatically assemble prior pairs, but add no derived behavioral guidance beyond formatting. Cumulative, hybrid and retrieval synthesis transform trace content through curator generation and retain the result for later calls. In each qualifying route, raw problem/solution data becomes a derived sheet and reaches a generator or next curator. Local execution results can be part of that raw solution; provider-native text is not presumed to contain the same detail.

Cumulative has both within-question and cross-question horizons; hybrid is cross-question with one update per example. Retrieval synthesis derives guidance from previous questions for the next question, then carries that sheet forward. These are online learning writes under the comparison contract, without asserting new truths or demonstrated gains. File initialization and resume can transfer state between invocations, but are retention/import routes rather than additional offline learning.

Maintenance replaces a whole string. The source asks for useful reasoning, examples and source-question references to remain in it, and later consumers read them when present. It does not preserve a distinct criticism record, enforce stable entry IDs, compare versions, guarantee reason retention, or consult old rows to reconstruct omitted cumulative entries. Historical rows retain prior snapshots for operator recovery. Formatting extraction, fallback and post-answer scoring are three separate mechanisms; none makes correctness a prerequisite for admission.

Read-back

Every wired memory consumer here receives automatically prepared material. At a new question, the cumulative selector supplies the whole current sheet to generator and curator; at subsequent cumulative rounds it supplies the replaced sheet and optional prior answers. This is coarse push. Full history supplies all available prior pairs, also coarse push. Retrieval uses the current question vector and prior-vector prefix to pick top-k pairs for generator or curator: inferred-embedding push. Retrieval synthesis then uses the current question, prior guidance and selected pairs to produce question-conditioned guidance for the generator: inferred-judgment push. The prior sheet supplied to that curator remains coarse push. Hybrid combines coarse whole-sheet delivery with embedding-selected examples.

The template's approximate 2000–2500-word request is not an input cap; the curator output token budget is twice configured max_tokens, generator output uses max_tokens, and full history can grow without a memory-specific truncation check. Question-string identity matching reorders embeddings; it does not select an additional subset of memory for a consumer. Restoring an operator-specified file is preparation for these pushes, not an independent pull initiated by generator or curator.

Availability is established by state storage and restoration. Delivery is wired in the current code and observed in the two-row historical artifact. Neither proves the generator relied on a particular item, obeyed its usage guidance, or improved because of it. The historical target-inconsistent sheet is evidence against treating the source's reliability wording as a guarantee; it is not a general estimate of error rate.

Comparison rationale

The profile covers operative memory plus access structures. Numeric embedding arrays are symbolic ranking data stored in files and process memory, so their presence does not require vector as a storage substrate. Advisory strategies do not become instruction-tier memory merely because they contain imperatives. Likewise, default static prompts are not authored accumulated memory. Imported file content supports imported lineage without establishing who authored it.

The curation set distinguishes hard replacement from semantic aspirations. Evolve is wired whole-sheet replacement. Synthesize is claimed: the template requests new meta-strategies, but creating a claim absent from inputs is not established by an LLM transformation or the native RetrievalSynthesis name. Dedup and promotion likewise remain claimed because no inspected evidence establishes their successful semantics or an independent count-driven selector. The word “consolidate” in a prompt does not establish reduction without new claims, and deleting omitted content is not an invalidation protocol retaining a current-reliance marker. Trace-learning yes is independently wired even with all these quality limits. Formal mathematical expressions in the selected historical sheet establish observed symbolic content for both representational_form and distilled_form. That is the strongest witness for the existence of the value within the included historical evidence. The wired numeric embeddings remain a separate witness; the observed basis does not assert that the current revision produced the historical sheet or that model parameters changed.

Known sets apply only to the stated boundary. The unestablished historical producer prevents upgrading current route implementation to observed deployment at this revision. The inspected rows do not test dependence on recalled content, so the faithfulness assessment deliberately retains uncertainty.

Epistemic lens

1. Source-and-claim boundary

The analysis question is whether a generated solution or reusable strategy is checked and accepted for a named later use, and which result controls that use. Source scope is SRC-1, SRC-2, the selected historical artifact SRC-3 and the reported performance SRC-4. The two-row artifact records retained text and delivery but does not identify its producing revision. Consequential claims are CLM-1, CLM-2, CLM-3 and CLM-4; CLM-5 records the analytical evidence limit. Assessed families are proposal/curation, retrieval, direct context supply, code feedback and benchmark scoring. Provider hidden reasoning and remote tool internals are unassessed; these exclusions prevent classifying hidden criticism or establishing provider-side checks. No experiment design is reconstructed, preventing component-effect attribution from CLM-2. The ledger concerns the implementation, not an observed traversal of every route.

2. Epistemic-object inventory

object ID	candidate truth-apt content or none	claimed role	evidence source ID and local anchor	gap/limit
OBJ-1	reusable strategies, claims about methods and possible code	future-problem guidance; see OBJ-1	SRC-1 dynamic_cheatsheet/language_model.py; SRC-2 prompts/curator_prompt_for_dc_cumulative.txt	guidance can mix claims and prescriptions; successful delimiter extraction does not establish truth
OBJ-2	prior questions and model solutions	examples for future generation; see OBJ-2	SRC-1 run_benchmark.py	a saved answer may be incorrect; its retention is not an endorsement
OBJ-3	none individuated in numeric vectors	similarity selection; see OBJ-3	SRC-1 run_benchmark.py	vector proximity licenses ranking, not correctness or applicability
OBJ-4	proposed answer and explanatory claims	solution to current problem; see OBJ-4	SRC-1 dynamic_cheatsheet/language_model.py	entailment/preservation cannot be inferred from model generation alone
OBJ-5	Boolean that the selected evaluator accepts the answer	score accounting; see OBJ-5	SRC-1 dynamic_cheatsheet/utils/evaluation.py	narrowly tests formatting/task predicate against target, not all reasoning

| OBJ-6 | proposed strategies and possible formal content | upcoming-problem reference; see OBJ-6 | SRC-1 dynamic_cheatsheet/language_model.py | prior sheet is retained for the next curator; semantic novelty unestablished |

3. Authority-route ledger

Each row separates functions even when they share a canonical route. Generic identity and endpoints remain on the named canonical record. “No instance established here” below is an observation limit, not a negative architectural conclusion.

route ID	route function	architectural status	object/candidate ID	content/update relation	transition or check target	evaluator/condition and domain	activation and timing	possible or observed result	implemented force	epistemic authority and scope	operational authority	behavioral-authority path	evidence source ID and local anchor	claim IDs	mismatch marker	gap/limit
RTE-2	content transformation	implemented	OBJ-1	truth-apt transformation: indeterminate	proposed cheatsheet content	CMP-1 under curator prompt, problem strategies	after a cumulative answer	revised/copied/generalized text possible	proposes replacement	candidate only; preservation, derivation or ampliation depends on content	prepares subsequent context	see BAP-2	SRC-1 dynamic_cheatsheet/language_model.py	CLM-1, CLM-3	none	no semantic-preservation invariant
RTE-2	check/evidence production	implemented	OBJ-4, OBJ-1	no content change	correctness/usefulness of solution and guidance	CMP-1 prompted assessment; no independent reference answer supplied	curator call before extraction	model may criticize, confirm or omit	influences generated replacement through opaque judgment	requested semantic judgment only	may change proposed memory	see BAP-2	SRC-2 prompts/curator_prompt_for_dc_cumulative.txt; SRC-1 dynamic_cheatsheet/language_model.py	CLM-3	verified wording exceeds independent check	a named claim-level judgment is not guaranteed to be retained
RTE-2	disposition/acceptance	implemented	OBJ-1	no content change	extraction/admission of candidate string	opening delimiter and string splitting, syntax domain	curator return	new string or fallback	replacement/fallback	no truth warrant from delimiter condition	admits future guidance without a separate semantic veto	see BAP-1	SRC-1 dynamic_cheatsheet/utils/extractor.py	CLM-3	operational admission differs from epistemic acceptance	does not establish an evidence-consuming acceptance of every claim
RTE-1	retention	implemented	OBJ-1, OBJ-2, OBJ-4	no content change	completed example records	unconditional append and JSONL write after example	sequential loop	stored payloads	persistence	preserves available lineage fields, not validity	makes text available to later calls/resume	see BAP-1	SRC-1 run_benchmark.py	CLM-1	none	retention precedes any memory-specific epistemic acceptance
RTE-3	operational admission/selection/consumption	implemented	OBJ-2, OBJ-3	no content change	prior pair eligibility and ranking	prior index and cosine top-k, or all-history branch	before generator/curator	selected previous pairs	contextual selection	similarity does not warrant answers or transfer	supplies candidate examples	see BAP-1, BAP-2	SRC-1 dynamic_cheatsheet/language_model.py	CLM-1	none	delivery is not causal activation
RTE-7	content transformation	implemented	OBJ-6, OBJ-2	truth-apt transformation: indeterminate	retrieval-synthesized reference	CMP-1 under retrieval curator prompt	before current answer	rewritten reference possible	prepares memory context	candidate synthesis; no automatic entailment guarantee	generator receives result	see BAP-1, BAP-2	SRC-1 dynamic_cheatsheet/language_model.py	CLM-1	none	see RTE-7; new-claim synthesis remains claimed under CLM-4
RTE-7	check/evidence production	implemented	OBJ-6, OBJ-2	no content change	usefulness and possible improvements to prior strategies	CMP-1 under retrieval curator instructions	before current generator	model judgment expressed through proposed guidance	shapes replacement proposal	requested critique, not independent verification	may change future reference content	see BAP-2	SRC-2 prompts/curator_prompt_for_dc_retrieval_synthesis.txt; SRC-1 dynamic_cheatsheet/language_model.py	CLM-4	none	no separately retained criticism result required
RTE-7	disposition/acceptance	implemented	OBJ-6	no content change	proposed guidance string	extractor opening-tag condition	curator return	extracted text or retrieved-pair fallback	admits generator context	no semantic warrant from extraction	supplies new guidance immediately	see BAP-1	SRC-1 dynamic_cheatsheet/utils/extractor.py	CLM-4	admission differs from acceptance	see ABS-1; fallback also lacks truth certification
RTE-1	retention	implemented	OBJ-6	no content change	returned retrieval-synthesis sheet	ordinary checkpoint and next-loop assignment	after each example	sheet preserved	persistence for next curator	no claim-level endorsement	supplies prior guidance to RTE-7	see BAP-2	SRC-1 run_benchmark.py	CLM-1	none	retention is not post-acceptance integration
RTE-4	content transformation	implemented	OBJ-4	truth-apt transformation: indeterminate	solution and explanation	CMP-1 from problem/guidance/feedback	each generation call	answer, code, explanatory claims	produces candidate solution	no general warrant from model generation	may request code and supply answer	see BAP-3	SRC-1 dynamic_cheatsheet/language_model.py	CLM-1	none	derivation and conjecture must be assessed from particular content
RTE-4	check/evidence production	implemented	OBJ-4	no content change	generated computation, not necessarily original proposition	host Python semantics and process outcome	execution flag branch	output/error/timeout	feedback appended	witnesses the computation under actual environment only	permits further model reasoning	see BAP-3	SRC-1 dynamic_cheatsheet/utils/execute_code.py	none	none	no guarantee correct encoding or explanation
RTE-6	check/evidence production	implemented	OBJ-4	no content change	provider-managed code request	external provider, internal evaluator uninspected	native execution configured	provider text returned	feedback through external route	internal execution warrant uninspected	returns text to approach	see BAP-3	SRC-1 text_generation/simple_unified_client.py	none	none	dispatch implementation does not establish observed tool execution
RTE-5	check/evidence production	implemented	OBJ-4, OBJ-5	truth-apt transformation: entailed derivation	answer matches selected task predicate/target	deterministic evaluator and dataset target	after memory update	Boolean predicate result	produces score input	output only follows implemented predicate and supplied target	enables score increment	see BAP-4	SRC-1 dynamic_cheatsheet/utils/evaluation.py	CLM-2	none	target truth/encoding and explanation not certified
RTE-5	disposition/acceptance	implemented	OBJ-5	no content change	whether answer counts correct in current benchmark	result truth value	immediately after evaluation	count or no count	score accounting	accepted only under selected benchmark criterion	changes printed aggregate, no memory veto	see BAP-4	SRC-1 run_benchmark.py	CLM-2	answer success is not strategy acceptance	no observed candidate-linked run reconstructed here
RTE-1	lineage/freshness/recovery	implemented	OBJ-1, OBJ-2	truth-apt transformation: acquisition/import	imported or restored payload	operator path plus selected parameter checks	initialization/resume	old text reused	runtime state restoration	warrant of imported content unknown	restores next-iteration contexts	see BAP-1	SRC-1 run_benchmark.py	CLM-1	none	parameter consistency is not endorsement or freshness certification

No lifecycle-integration row is inferred from ordinary retention or operational reuse. A post-acceptance integration of strategy claims is not established by these routes; model-internal assessment remains opaque.

4. Per-object lifecycle disposition

The generic implementation permits both copied/reshaped content and newly generalized claims. It does not make every generated proposition ampliative or entailed. Specific observed artifacts introduced by the memory specialist retain their separately bounded evidence status.

candidate object ID	relevant route IDs	transformation	classifications still possible / lifecycle disposition	preserved lineage	implemented checks, retention, or use	current warrant limit	evidence needed
OBJ-1	RTE-2, RTE-1	indeterminate	non-ampliative reshaping, entailed derivation and ampliative conjecture remain possible for distinct entries	current question/solution and previous sheet available during generation; item references requested	prompted assessment, string admission, future context	no uniform claim-level acceptance or semantic preservation established	candidate text with source premises, stated criticism and evidence-consuming disposition
OBJ-2	RTE-1, RTE-3	truth-apt transformation: acquisition/import	discovery lifecycle: not applicable to recording/retrieving the existing payload	input/output association retained	selected by availability or cosine	import preserves the text, not source answer truth	input-level provenance and independently checked answer scope
OBJ-4	RTE-4, RTE-6, RTE-5	indeterminate	derivation, copying and ampliative conjecture depend on individual answer/explanation	prompt and output fields retained	optional code feedback and task answer evaluator	a passing answer cannot license every explanatory or reusable claim	candidate-linked derivation and check evidence
OBJ-5	RTE-5	truth-apt transformation: entailed derivation	discovery lifecycle: not applicable	evaluator input/target relation	Boolean controls score only	derived predicate result warranted only relative to implementation and encoded target, not target truth	executed result plus evaluator/target validity evidence

No lifecycle record for OBJ-3: no candidate truth-apt output for this object; relevant direct-adaptation or update routes: RTE-3. Its numeric similarity affects selection but does not assert a separately inspected proposition.

For OBJ-6, relevant routes are RTE-7 and RTE-1; transformation is indeterminate. Copying/reshaping, entailed derivation and ampliative conjecture remain possible for separate propositions. Lineage preserves selected prior pairs and previous sheet as model inputs; checks are prompted judgment and format extraction, with retention for generator/next-curator use. Current warrant is candidate guidance, not established claim novelty or epistemic acceptance. Resolving it requires an input-to-output proposition comparison plus recorded criticism and acceptance evidence. The semantic synthesize claim is CLM-4, not an implemented novelty guarantee.

For the observed OBJ-1 instance in SRC-3, the first-row target conflict and second-row delivery are evidenced. The artifact does not retain a distinct acceptance decision, named content criticism or producing revision. Its transformation remains indeterminate: it could preserve an already-generated unsupported solution or introduce generalized claims. These observations do not license acceptance, post-acceptance integration or causal learning. For OBJ-4 in that artifact, the answer/target mismatch is evidenced; the actual evaluator disposition and broader explanatory warrant are not separately established.

5. System-claim versus route comparison

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
CLM-1	improve from inference-time memory without labels	SRC-2 README.md, doctrine/design	generator/curator guidance and saved memory	RTE-1, RTE-2, RTE-3	bounded specialist artifacts can show payload retention, not universal improvement	no controlled comparison reconstructed	automatic memory construction and later delivery wired without curator target labels	label-free curation is compatible with label-based evaluation; improvement attribution unresolved
CLM-2	large performance gains	SRC-4 README.md, reported operation	named benchmark gains	RTE-5 supplies evaluators	repository artifacts are not reconstructed as full performance experiments here	model/version/order/design confounding unassessed	attributed report of improved outcomes	no independent causal claim about memory or criticism
CLM-3	retain verified correct useful strategies	SRC-2 prompts/curator_prompt_for_dc_cumulative.txt, doctrine/design	explicit model assessment instruction	RTE-2, RTE-3	named acceptance decisions not established here	no independent correctness/counterfactual test	model-mediated proposed curation with format-based admission	verification intent is stronger than enforced semantic check

| CLM-4 | new useful strategies, deduplication, counts and prioritization | SRC-2 curator prompts, doctrine/design | explicit requests | RTE-2, RTE-7 | content and delivery only via SRC-3 | none at semantic-operation grain | evolve wired; synthesize/dedup/promote claimed | native labels and whole-string rewriting do not prove semantic novelty or maintenance quality |

The analytical limit CLM-5 is not a new public product claim; it bounds observed-run and causal columns above.

6. Bounded conclusion

The workflow acquires problem inputs and earlier outputs, selects or reshapes them into context, and can generate new problem strategies. It grants that text operational access to future generation before a separate claim-level acceptance is established. Code execution produces feedback about submitted computations; task evaluators decide whether final answers count under a benchmark predicate. Neither check automatically endorses the explanations or the next cheatsheet. The system's strongest wired contribution is maintaining and supplying inference-time guidance across problems. The unresolved question is which retained claims are warranted, which are content-criticized and revised, and which changes cause better later performance. No system-wide epistemic grade follows from these different routes.

Reconciliation

The fresh specialist's final report is bound by the Run identity hash. Its input hash, source identity, full revision, complete status and method hash were checked; the parent inspected the cited control paths and the first two historical JSON rows. The specialist owns the memory comparison, adopted without strengthening any value.

Proposal mapping: local MEM-OBJ-1 became OBJ-6; local MEM-RTE-1 became RTE-7; local MEM-CLM-1 became CLM-4; local MEM-CLM-2 became CLM-5; local MEM-ABS-1 became ABS-1. Exact-token mapping preserves OBJ-1 as cumulative/hybrid guidance and RTE-2 as its update mechanism; RTE-3 remains the selection family, with RTE-7 as its specific synthesis branch. Each mapped target is declared once and all profile references resolve.

Amendments: the preliminary synthesis evidence basis on RTE-7 was wired; the specialist revised it to claimed for the controlled semantic operation because transformation alone does not establish a new claim absent from inputs. CLM-4 supplies the prompt evidence. The symbolic representational-form basis changed from wired OBJ-3 to observed OBJ-1 as the strongest existence witness, retaining current-revision provenance limits. These amendments affect the comparison fields and epistemic novelty assessment; they do not weaken independently wired trace-fed memory writes. The corrected report also added explicit bounded search evidence to ABS-1 and generated row-two delivery quotes on OBJ-1. No coordinator mechanical edits to the specialist report were made.

All integration issues were disposed: retrieval-synthesis guidance is retained and reused, with retrieved-pair fallback; hybrid has one update pair; resume depends on compatible ordering not checked by its selected parameter list; provider container contents are excluded; faithfulness remains not-determinable. Specialist quotations are retained on their supporting canonical records, with identical passages occurring only once. Shared-source overlap between lenses is not claimed as independent convergence. Generic IDs and runtime identity remain coordinator-owned; memory overlays do not redefine their referents. No unresolved anchored conflict remains.

Bounded synthesis

Evidence basis: fixed-commit source wiring, shipped prompts and two retained historical rows, with no fresh benchmark execution. Dynamic Cheatsheet's strongest supported contribution is an automatic inference-time memory path: generated solutions become retained guidance, and later generator/curator invocations receive it. The memory comparison's trace-learning classification describes that write-and-read-back mechanism. It does not establish improved capacity caused by criticism.

The operator first chooses a model, approach, task and prompts. The benchmark owns sequencing and persistence; the model chooses answer content and curator replacement. Cumulative mode can revisit one problem across rounds before carrying guidance to another problem. Retrieval modes select earlier examples, and retrieval synthesis additionally writes and retains guidance before the current answer. Hybrid combines two context sources but performs one answer/update pair. Initializing or resuming can carry state across invocations, conditional on compatible dataset ordering and input parameters.

The distinguishing admission decision is simple string extraction. Curator templates request correctness assessment and useful transferable strategies; delimiter presence controls replacement, while semantic quality remains model-mediated. Missing opening tags fall back to the old cumulative sheet or retrieved-pair text in retrieval synthesis. The historical artifact contains a sheet answer that conflicts with its recorded target and appears in the next problem's prompt. This is a concrete counterexample to treating retention/delivery as certification, not evidence that the next answer was caused by that sheet.

The implementation makes local code output available for refinement and curation, and can delegate execution to provider-native tools. These paths have different control owners. Local generation permission does not cover arithmetic evaluator eval, and subprocess timeout is not sandboxing. The post-generation evaluator has benchmark targets or task predicates, but its pass/fail does not decide memory admission. The practical consequence is that a correctly scored answer and a trustworthy reusable strategy are different findings.

Under the theory-builder definition, localized explicit content is observed in retained guidance; source-native structures offer inspectable whole entries, formulas and examples. Actual consumption of what a particular theory says remains uninspected beyond prompt delivery. Content-directed criticism is requested, while a retained, candidate-linked criticism and its later uptake are unestablished in this boundary. Thus conditions 1–4 do not share one strength: condition 1 observed, condition 2 uninspected, condition 3 claimed, condition 4 uninspected. A complete theory-builder classification is not established. Addressability is stronger than a single opaque value but weaker than stable validated claim units; persistence reaches cross-problem delivery in the historical artifact and cross-run restoration in code.

Learning and self-improvement: automatic revision and persistence are wired, and the README claims improved benchmark capacity. Causal improvement attributable to criticism, or to an individual memory component, is uninspected. Reflection is uninspected: some sheets describe problem-solving methods, but the inspected artifacts do not establish the two-way self-representation and criticism of method texts required by reflective-system. Autonomous operation is wired for configured solving, curator calls, extraction, storage and score accounting; humans supply inputs and configuration. Autonomous theory-building remains unestablished because its substantive criticism/iteration conditions remain unresolved. These are separate properties, not a ranking.

For sequential problems sharing reusable techniques, the architecture supplies a concrete path for guidance accumulation and reuse. For a use requiring independent acceptance of retained claims, the visible path supplies no such guarantee. Candidate-linked criticism and explicit decisions would change the theory-route assessment; controlled comparisons with pinned models and intervention on recalled material would change the learning/activation assessment. Provider execution traces and pinned embedding provenance would resolve different remaining boundaries.

Limitations

limitation	affected source, record, or route IDs	inspected boundary	conclusion prevented	evidence that would resolve it
Provider computation is opaque	CMP-1, RTE-6	wrapper dispatch	exact weights, hidden criticism, remote isolation or actual tool operation	pinned provider/runtime evidence and tool traces
Embedding provenance is not established	CMP-2, OBJ-3	CSV loading and cosine ranking	model fixity and semantic quality of retrieval	creation recipe, model revision and retrieval evaluation
No fresh runtime probe	SRC-1, RTE-1, RTE-4, RTE-5	static implementation	deployed operation and reliability	controlled executions at this revision
Performance reports not reconstructed as causal comparisons	CLM-2	README assertions	causal benefit from memory, its individual component or content criticism	inspectable experiment design and appropriately varied comparisons
Candidate judgment can be opaque	RTE-2, RTE-3, OBJ-1, OBJ-4	prompts, text replacement and deterministic dispatch	universal semantic preservation, warranted strategy transfer or all theory-builder conditions	candidate-linked criticism, admission decisions and later behavior evidence
Boundary excludes independent generic-client APIs	SRC-1	assessed Dynamic Cheatsheet entry families	all-client memory or security completeness	separately scoped inspection of those consumers

Verification and blockers

Semantic verification

Checked the integrated profile against its exact memory scope and canonical records: OBJ-1, OBJ-2, OBJ-3 and OBJ-6; RTE-1, RTE-2, RTE-3, RTE-4 and RTE-7. All six approaches are accounted for. Cumulative/hybrid and retrieval synthesis supply the qualifying trace-fed writes; raw history/retrieval/default do not acquire a learned transformation merely by saving text. No compaction branch was identified among these explicit approach implementations; whole-sheet replacement and retention are covered.

Checked every push signal's consumer and selector: whole-sheet/all-pair availability is coarse; question-vector cosine top-k is inferred-embedding; RTE-7's question-conditioned curator output supplied to generator is inferred-judgment. String matching that aligns vectors is not an added identifier signal. No pull route is inferred from internal file reads. Reviewed per-task versus cross-task horizons, online timing and both retained forms, including historical provenance limits. Checked automatic acquisition versus external authorship; selected initialization does not create an internal manual write route.

Checked curation semantics and amendments: evolve wired; synthesize/dedup/promote claimed; faithfulness not-determinable. Strongest form witnesses remain observed historical objects while current execution stays wired. Quote occurrences are generated; semantic support was checked against selected commit-addressed source spans, including target-conflict and exact row-to-row delivery. No passage depends on truncated output; initial oversized method read was reread in bounded spans, and specialist source rereads are documented in its report.

Checked all material route fields, admitting roles, answer-oracle boundaries, provider/local/evaluator alternatives, retention and read-back. Epistemic rows keep transformation, checking, disposition and retention distinct; accepted answer score does not license strategy correctness. Candidate transformation uncertainty blocks unsupported novelty/lifecycle claims. Theory-builder conditions, learning, reflection and autonomous operation retain separate statuses. Source identity/pin and publication destination guard are verified; no prior-analysis prose or audits were read.

Deterministic validation

commonplace-validate --full kb/reports/state/agentic-system-analysis/AAS-2026-09-27-dynamic-cheatsheet-04/result.md passed cleanly with exit status 0: schema, canonical records, 31 quote anchors, relative links and memory-comparison references. Final editorial completion is revalidated with the same command before publication.

Blockers

None.