Dynamic Cheatsheet memory report

Type: agentic-systems/types/agent-memory-analysis-report.md

Boundary and evidence

The target is the shipped Dynamic Cheatsheet repository at 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9 (SRC-1 implementation; SRC-2 design; SRC-3 reported artifacts). The memory boundary includes (1) cumulative cheatsheet text and its update route, (2) prior task input/output examples retained in benchmark rows and in-run history, and (3) precomputed dense vectors only where they select those examples. It covers explicit initialization, JSONL persistence and continuation, and the later prompt consumers. It excludes static prompts, benchmark task/target material except as inputs to the memory routes, evaluator outputs because they do not gate or inform memory admission, provider/model internals, and transient request context not retained in rows.

The source register declares three evidence layers for one frozen Git commit. This report directly read run_benchmark.py, dynamic_cheatsheet/language_model.py, dynamic_cheatsheet/utils/extractor.py, dynamic_cheatsheet/utils/evaluation.py, the shipped generator and curator prompts, and the shipped notebooks and results where relevant. Additional inspected coverage at the registered commit is recorded here; source paths below are commit-relative. Direct source inspection establishes wiring, not a run. Runtime findings are supplied provisional evidence and are cited as such where used. No provider execution trace or reproducible benchmark run is supplied.

Entry point or operation Source path Covering records Boundary and limit
CLI argument dispatch, variant selection, initialization, resume, loop and JSONL writes run_benchmark.py MEM-OBJ-1, MEM-OBJ-2, MEM-RTE-1, MEM-RTE-2 Inspected implementation. Answer evaluation occurs after memory update and is excluded from memory because its result is not passed to a curator.
Cumulative update and approach-specific prompt construction dynamic_cheatsheet/language_model.py MEM-RTE-1, MEM-RTE-2, MEM-RTE-3 Inspected implementation; provider responses and consumer activation are unobserved.
Cheatsheet parse/fallback dynamic_cheatsheet/utils/extractor.py MEM-RTE-1, MEM-RTE-2 Inspected implementation; parse success says nothing about truth or completeness.
Evaluator behavior dynamic_cheatsheet/utils/evaluation.py Excluded Evaluates the answer after memory update; the result is not an admission signal.
Hooks, registered tools and separate memory lifecycle operations Python source tree; run_benchmark.py; dynamic_cheatsheet/; text_generation/ No additional memory route declared No separate hook registration, memory cleanup API, or content-level rejection/withdrawal operation was found in the inspected target code; this does not exclude host- or provider-side behavior.
Prompt claims and memory instructions prompts/curator_prompt_for_dc_cumulative.txt; prompts/curator_prompt_for_dc_retrieval_synthesis.txt; prompts/generator_prompt.txt MEM-RTE-1, MEM-RTE-2 Design evidence only; prompt requests do not verify model compliance.
Retrieval access structure embeddings/; run_benchmark.py MEM-OBJ-3, MEM-RTE-3 Checked-in dense vectors are loaded and consumed; their generation method and provenance are unavailable.
Notebook and artifact consumers ExampleUsage.ipynb; EvaluatingResults.ipynb; results/ Excluded from operative memory routes Inspected as shipped usage/evaluation/artifacts; notebook outputs and checked-in results do not establish observed operation.

The runner's continuation path loads the latest cheatsheet and every prior raw model output from selected JSONL rows:

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:185-189 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

This is implementation evidence for the memory read path. It does not show a particular operator resumed a run or that a model used supplied context as intended.

Core ideas

Dynamic Cheatsheet has two main retained forms. Cumulative variants ask a model to replace a standing natural-language cheatsheet after each task; the Python extractor accepts text inside the expected tags or returns the prior text. Retrieval and full-history variants retain raw problem/solution examples in run rows, then construct prompt context from earlier examples. The retrieval variants use checked-in dense vectors and cosine similarity to choose top-k examples. Retrieval synthesis adds a separate generated text view over selected examples. Cumulative retrieval supplies both the standing cheatsheet and selected examples before updating the standing text.

The runner controls state and durability; the model client does not own a durable memory store. It overwrites the chosen JSONL file with the accumulated rows after each processed task and again at run end. A new row thus retains its returned cheatsheet and raw generator output; continuation reconstructs the latest cheatsheet and history. The preceding version is present in earlier rows of the same result file, but no system route compares versions, rolls back, or selects an older cheatsheet automatically. Reusing a different file is operator-controlled recovery.

The memory write is not evaluation-gated. The curator receives question, generator output and prior text; the answer evaluator later compares the answer to task target, but its result is not an updater input. Curator prompts ask for correctness, useful general strategies, refinement and redundancy reduction. These are instructions to an opaque model, not deterministic validation. Parsing tags accepts a proposal or falls back to the prior string; it does not check the proposal's claims. I found no memory-specific rejection, human approval, automatic rollback, TTL or decay route in the inspected implementation. Replacement may omit content, while its earlier row can preserve a historical copy; the code does not label an omission as a justified invalidation.

The context boundary has no implemented token-budget selector for retained content. Cumulative text is passed as a whole; full-history appending grows with the prior examples; retrieval top-k is the explicit selection control. Model input limits and any provider-side truncation are not established by this source. Embedding rankings are deterministic code operations over supplied vectors, but vector generation and retrieval quality are uninspected.

Shared records

Components

none declared in this member

Operative objects

MEM-OBJ-1 — Persisted cumulative cheatsheet

  • Source-native identity: runner's cheatsheet / returned final_cheatsheet string.
  • Representational form: natural-language interpreted by the generator model.
  • Storage substrate: process memory during a run; JSONL result file between calls/runs.
  • Lineage: generated by curator calls from current task material and prior cheatsheet, or supplied through the optional initialization-file path; origin of any supplied initial text is unknown.
  • Consumers and behavioral authority: later generator prompts receive it as reference material (knowledge); no evidence establishes compliance or improvement.
  • Identity comparison: new operative part of RT-OBJ-1; distinct from the prior-example corpus because it is a replaceable summary rather than per-task raw answer history.
  • Evidence: run_benchmark.py; dynamic_cheatsheet/language_model.py (SRC-1). The updater adopts extracted replacement text and falls back to the old text when tags are absent:

new_cheatsheet = extract_cheatsheet(response=cheatsheet_output, old_cheatsheet=current_cheatsheet) cheatsheet = new_cheatsheet --- dynamic_cheatsheet/language_model.py:434-435 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

MEM-OBJ-2 — Retained task interaction rows

  • Source-native identity: result-row input, raw_input, target, final_output, final_answer, steps and final_cheatsheet; later consumers use prior inputs and final_output values, with the target retained only for result reporting/evaluation.
  • Representational form: natural-language task and response text plus structured JSON field organization.
  • Storage substrate: accumulated JSONL output file and in-process lists reconstructed from it.
  • Lineage: benchmark inputs and model responses are retained as task interaction records; dataset origins and any deployment provenance are outside the evidence.
  • Consumers and behavioral authority: selected history is supplied as reference to later generator calls; target/evaluation fields are retained but do not affect admission or retrieval selection in inspected routes.
  • Raw versus derived: raw interaction rows, not the curator's derived cheatsheet; steps may retain prompts and intermediate outputs, but no separate consumer route reads them on continuation. Part of: RT-OBJ-2
  • Identity comparison: this narrows the supplied combined object to textual task/response material; it is separate from the embedding access structure below.
  • Evidence: run_benchmark.py (SRC-1). The JSONL helper opens the result path in write mode and rewrites all accumulated rows:

with open(file_path, "w") as file: for line in data: file.write(json.dumps(line) + "\n") --- run_benchmark.py:76-78 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

MEM-OBJ-3 — Precomputed retrieval embeddings

  • Source-native identity: embeddings/<task>.csv rows parsed into dense numeric vectors and reordered to match dataset inputs.
  • Representational form: distributed-parametric dense representation; exact embedding model and production process are not exposed.
  • Storage substrate: checked-in CSV files and process memory.
  • Lineage: supplied/imported access material of unknown upstream origin; no runtime embedding generation is wired.
  • Consumers and behavioral authority: cosine-similarity top-k code ranks and selects prior task examples, thereby influencing relative priority and the prompt input window. Part of: RT-OBJ-2
  • Identity comparison: it is a distinct access structure, not another declaration of the raw input/output text.
  • Evidence: run_benchmark.py; dynamic_cheatsheet/language_model.py (SRC-1). The runner loads checked-in input and embedding columns into aligned numeric vectors:

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) --- run_benchmark.py:211-214 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The top-k selector applies cosine similarity to the current and earlier vectors:

similarities = cosine_similarity([current_original_input_embedding], prev_original_input_embeddings) top_k_indices = np.argsort(similarities[0])[::-1][:retrieve_top_k] --- dynamic_cheatsheet/language_model.py:510-511 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Routes

MEM-RTE-1 — Cumulative curation and later use

Part of: RT-RTE-1 - Endpoints and progression: current question and generator output plus prior MEM-OBJ-1 → curator model proposal → tag extractor or old-text fallback → runner state and JSONL row → later generator prompt and/or continuation read. - Trigger and principal: each processed item for cumulative and cumulative-retrieval approaches; runner invokes calls, model proposes, parser determines whether tagged content replaces state. - Context and state: generator answer is produced before curator call; updater receives question, raw generator output and prior cheatsheet. In cumulative-retrieval, the generator also sees selected prior examples. - Proposal and decision roles: model proposes replacement content; extraction accepts a tagged string or falls back on parse failure. No human veto or independent correctness oracle gates memory. The evaluator uses the task target after update, so it is an answer oracle for scoring only, not admission. - Operating mode and trigger: bounded benchmark examples; update is triggered by each completed generator response, not by a demonstrated improvement signal. - Admission/rejection/recovery: parsed tagged text replaces the old value, otherwise old text remains. No content-level rejection, approval, or automatic rollback is wired. Earlier row versions can be manually reused; ordinary continuation reads the last row's cheatsheet. - Guidance and persistence: cumulative curator prompt tells the model to assess correctness, preserve useful material, refine it and remove redundancy. Prompt is static repository material; each adopted text persists in the result row. - implementation conclusion status: wired - operation conclusion status: uninspected - Guarantee strength: no claimed guarantee - Immediate return: extracted answer, raw generator output, intermediate steps and replacement/fallback cheatsheet. - Later read-back: current text is included for later cumulative generation; continuation also reloads latest text. Cumulative retrieval adds its separately selected examples. - Delegated visibility: provider receives the question and prompt material supplied to generator/curator; provider-side retention is outside the boundary. - Selection predicate: cumulative text is supplied as a whole; subsequent processing can replace it only when expected tags parse. No memory-specific budget or content validation is implemented. - Invalidation or expiry: no TTL/decay; replacement can omit earlier text, but no explicit invalidation record is made. Earlier JSONL rows may retain previous versions without an automatic consumer selecting them. - Activation or effect: prompt delivery is wired; behavior change is unobserved. - Evidence limits: source and prompt establish the route and intended curation only; model responses, claim correctness and later benefit are unobserved. - Evidence: runtime RT-RTE-1, SRC-1, SRC-2; see source quotation above.

MEM-RTE-2 — Full-history appending

Part of: RT-RTE-2 - Endpoints and progression: earlier input/output rows → append-all text construction → current generator prompt; runner also persists each current response for later tasks or continuation. - Trigger and principal: each item under FullHistoryAppending; Python concatenates all prior pairs in order. - Context and state: task corpus inputs plus prior generator outputs; there is no curator update. - implementation conclusion status: wired - operation conclusion status: uninspected - Guarantee strength: no claimed guarantee - Immediate return: constructed context, generator response and answer. - Later read-back: prior response rows are consumed by the next same-run call; continuation reconstructs those outputs from JSONL. - Delegated visibility: selected history is included in the generator request; provider-side handling is uninspected. - Selection predicate: every prior pair is appended; no similarity filter. - Invalidation or expiry: no TTL; an example leaves the current route only if excluded by run/resume indexing or the run ends. - Activation or effect: context delivery is wired; effect on answer is unobserved. - Evidence limits: implementation establishes append order and delivery, not response quality; growing input and provider truncation are not measured. - Evidence: runtime RT-RTE-2, SRC-1.

MEM-RTE-3 — Embedding top-k example selection

Part of: RT-RTE-2 - Endpoints and progression: current and prior precomputed vectors plus prior textual inputs/outputs → cosine similarities and descending top-k indices → ordered selected pairs in generator input; in cumulative retrieval, pairs are appended alongside MEM-OBJ-1. - Trigger and principal: retrieval variants per current item; Python computes and orders similarity scores using repository-provided vectors. - Context and state: current input, earlier inputs and outputs, embedding file, and retrieve_top_k parameter. No in-loop encoder is present. - implementation conclusion status: wired - operation conclusion status: uninspected - Guarantee strength: no claimed guarantee - Immediate return: selected indices/pairs are returned in output fields and formatted into generator context. - Later read-back: selected pairs are consumed by current generator; output history can participate in later retrieval calls. - Delegated visibility: chosen textual pairs are supplied to the generator; vectors and provider internals are not shown as prompt content. - Selection predicate: cosine similarity to current vector, descending order, truncated to configured top-k. - Invalidation or expiry: no index refresh or TTL; pairs outside top-k are simply omitted from this request and can be selected for another one. - Activation or effect: selector and prompt delivery are wired; embedding relevance and answer effect are unobserved. - Evidence limits: vector generation/provenance and retrieval quality are unavailable; no observed run confirms actual selections. - Evidence: runtime RT-RTE-2, SRC-1; source quotation above.

MEM-RTE-4 — Retrieval-synthesis text view

Part of: RT-RTE-2 - Endpoints and progression: selected examples, next input and previous returned cheatsheet → synthesis curator proposal → parser or fallback to retrieved context → generator prompt → returned text saved as final_cheatsheet. - Trigger and principal: every item using retrieval-synthesis; Python selects examples and calls model; model proposes a synthesized text view. - Context and state: selected prior pairs and prior returned text go to curator; resulting context and current question go to generator. - Proposal and decision roles: curator proposes; parser accepts tagged content or retains selected-context fallback; there is no correctness oracle or human veto. Dataset target is used after generation by evaluation, not in synthesis admission. Mode is a bounded benchmark; each item triggers synthesis. - Admission/rejection/recovery: parse failure falls back to retrieved context, not necessarily the previous standing cheatsheet. No content-level rejection or rollback is wired; saved rows can be manually selected for recovery. - Guidance and persistence: synthesis prompt requests reusable strategy and preservation of useful material; returned synthesis is stored with the output row and may be the latest cheatsheet on continuation. - implementation conclusion status: wired - operation conclusion status: uninspected - Guarantee strength: no claimed guarantee - Immediate return: selected pairs, answer and synthesized context. - Later read-back: synthesized text is delivered to the current generator, becomes the runner's current cheatsheet, and is passed as previous cheatsheet to the next retrieval-synthesis curator; continuation reloads the latest result. Selected raw history is independently available for subsequent retrieval. - Delegated visibility: curator sees selected text and prior returned context; generator sees synthesis; provider retention is uninspected. - Selection predicate: top-k similarity selects raw examples; parser selects tagged synthesis or falls back to retrieved context. - Invalidation or expiry: no TTL; synthesis can replace prior returned context; no explicit withdrawal reason is retained. - Activation or effect: delivery is wired; model use and benefit are unobserved. - Evidence limits: synthesis content and usefulness depend on unobserved provider output; no execution trace or causal evidence is supplied. - Evidence: runtime RT-RTE-2, SRC-1, SRC-2. The curator receives both selected prior examples and the previous returned text:

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}] --- dynamic_cheatsheet/language_model.py:533-536 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The runner adopts the returned cheatsheet for its next item:

cheatsheet = output_dict["final_cheatsheet"] --- run_benchmark.py:280-280 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Claims

none declared in this member

Evidenced absences

none declared in this member

Behavioral-authority paths

none declared in this member

Annotations

On RT-OBJ-1 — Cumulative cheatsheet text

  • Storage substrate: process memory and result JSONL file.
  • Representational form: natural-language.
  • Lineage: curator-generated from a current interaction and old cheatsheet, except optional externally supplied initial text.
  • Memory consumers and behavioral authority: later generator receives it as reference/knowledge; actual compliance is unobserved.
  • Raw versus derived: derived summary, distinct from retained per-task rows.
  • Write agency and curation: automatic curator proposal and parser adoption/fallback; consolidation/evolution and instructed de-duplication are wired, not quality-verified.
  • Trace learning/source: automatic trace-fed durable text; session inputs/outputs, with optional tool feedback embedded in generator output.
  • Read-back: included wholesale in later cumulative prompts; continuation pulls latest row's text; no implemented content budget, version selector or automatic rollback.
  • Status and limits: update and delivery wired, operation/activation uninspected; provider behavior outside evidence.

On RT-OBJ-2 — Prior question/output corpus

  • Storage substrate: result files and process memory; supplied dense vectors are separately declared as MEM-OBJ-3.
  • Representational form and lineage: the memory analyst splits readable task/output history (MEM-OBJ-2) from dense-vector access material (MEM-OBJ-3); see each operative object.
  • Consumers and authority: raw pairs supply knowledge/reference; embeddings rank and route candidate examples. Dataset target fields are retained for evaluation but do not supply a selector or admission criterion.
  • Raw versus derived: prior task interaction is raw retained history; vector rows are a supplied access structure, not a derived artifact shown to be rebuilt by this code.
  • Write agency and curation: runner automatically records responses; no curator changes raw history. Retrieval selects without modifying source rows.
  • Read-back: append-all or top-k push, plus explicit continuation reconstruction; precise selection is route-specific.
  • Status and limits: wiring inspected, operation and effect unobserved; embedding provenance is unavailable.

On RT-RTE-1 — Cumulative generation, curation and persistence

  • Memory-specific route: MEM-RTE-1 specifies the cheatsheet update and later use. Earlier final_output rows are consumed by retrieval routes, not by the cumulative curator path itself.
  • Storage/form/lineage: natural-language replacement text in JSONL, generated from current interaction and prior text, with optional external initial value.
  • Authority, agency and curation: knowledge at generator consumer; automatic proposal/adoption through tagged extraction; no validation or approval gate.
  • Read-back and trace: whole cheatsheet later pushed to generator; explicit continuation can pull last row; traces comprise current input and raw generator output, with conditional embedded tool feedback.
  • Evidence/status: code wiring, no observed operation or demonstrated improvement.

On RT-RTE-2 — Retrieved or full-history context construction

  • Memory-specific route: material is separated into MEM-RTE-2 full-history append, MEM-RTE-3 embedding-selected context, and MEM-RTE-4 synthesis view. Cumulative-retrieval combines MEM-RTE-1 update with MEM-RTE-3 selection.
  • Storage/form/lineage: natural-language input/output pairs in result JSONL plus externally generated dense vectors in CSV; vector derivation is unknown.
  • Authority: prior pairs are knowledge; vector similarity ranks and selects the generator's input window.
  • Agency/curation: raw pair history is automatically appended to rows; retrieval itself is selection, while synthesis mode adds a model-proposed view.
  • Read-back/status: append-all or top-k push to current generator, with optional synthesis; only wiring is established.

Write side

The runner has an explicit manual initialization affordance: it reads a chosen cheatsheet file into process state. For cumulative approaches, each generator response becomes curator input along with current question and prior cheatsheet; a tag extractor adopts the proposed text or returns the prior text. Retrieval synthesis instead uses selected examples, current input and previous returned context, and its parse-failure fallback is the retrieved text it was asked to synthesize. The full-history and raw retrieval approaches do not curate the historical examples; the runner appends model outputs to generator_outputs_so_far and result rows. When code execution returns text as part of generator output, cumulative curation can receive that response as part of the trace, but no particular tool run is evidenced.

No evaluator score is sent back to either curator. The answer oracle is task target data used after answer generation; it cannot validate memory admission. Curator language about verifying solutions remains an instruction, with no independent verifier. JSONL retains rows as checkpoints by rewriting the accumulated list after each example; this is a recovery affordance, not a guarantee of filesystem durability or atomic rollback. Rejection is limited to malformed/missing expected tags falling back to a prior string. Content omission during replacement has no explicit withdrawal marker or automatic retrieval of earlier versions.

Read-back

The cumulative generator receives the current cheatsheet in its model prompt. Full-history mode appends each prior input/output pair. Retrieval modes rank earlier questions against the current question's precomputed embedding, then place selected text pairs into the current prompt; top-k and similarity are code-level selectors, not evidence that the examples are relevant or correct. Retrieval synthesis adds an intervening curator consumer, then supplies its parsed text to the generator. Its returned context becomes the runner's current cheatsheet and is supplied as previous context to the next synthesis curator or through continuation. The cumulative-retrieval mode supplies both the cheatsheet and selected examples before its update call. These are push routes: automatic current-item processing supplies retained material without the consumer separately requesting each item.

Continuation is a distinct pull path selected by a CLI file argument. It reloads all prior outputs and the most recent cheatsheet, skips indices already represented by rows, then lets the selected approach decide which prior data to consume. In full-history mode raw outputs feed appended context; cumulative-only uses cheatsheet text; retrieval modes use prior outputs and vectors. The API result or prompt construction establishes availability/delivery, not activation. No source supplied here demonstrates model compliance, successful response, improvement, vector validity, host execution, or provider-side retention.

Comparison rationale

The profile includes files and in-memory because the runner persists rows and carries live context; it includes natural language and distributed-parametric form because the memory boundary contains interpreted text and dense-vector retrieval structure. The latter is not the language model's weights. Lineage is partial: source code shows task and model responses being recorded and vectors being loaded, but not who authored initialization files or how checked-in data and vectors were derived.

Knowledge describes the effect of retained examples and cheatsheet at the generator, where they serve as reference material. Embeddings additionally affect ranking and route which examples enter a prompt window. The cumulative cheatsheet and retrieval-synthesis text count as trace learning in the axis's route-specific sense: automatic interaction-fed writing persists behavior-shaping content that a later model call receives. This is a wiring classification, not a claim that future capacity improved. It applies only to those curation routes. Source values are session logs and, conditionally, tool traces included in generator outputs retained for later curation; no qualifying write consumes a demonstrated linked trajectory or environmental event stream.

consolidate, evolve and dedup follow the cumulative updater's prompt and whole-text replacement behavior; synthesize is wired in retrieval synthesis. No separate decay or promotion mechanism is exposed. Omission can remove currently delivered content while old rows may retain it, but the implementation does not mark that change as an invalidation operation. Push signal is coarse for whole-text and append-all supply and inferred-embedding for similarity-based top-k; explicit resume is pull and is not another push signal.

Integration issues

  • RT-OBJ-2 combines readable task/output history with a dense-vector access structure. This report declares MEM-OBJ-2 and MEM-OBJ-3 as its material parts, both with Part of: RT-OBJ-2, because they have different representational forms, origins, checks and consumers. Keep RT-OBJ-2 as a containing record; the split does not establish that its identity is wrong.
  • The returned storage_substrate.files evidence now includes MEM-OBJ-3, whose declaration and runner evidence establish checked-in embedding CSV input. The profile note still covers result JSONL and embeddings.
  • The returned curation_operations.synthesize evidence now cites MEM-RTE-4, the retrieval-synthesis route, rather than MEM-RTE-2, the append-all route. The trace_learning, trace_source, behavioral_authority.knowledge, read-back and curation profile entries include MEM-RTE-4 where its generated text is persisted and passed to later model calls. This route distinction is supported by SRC-1 dynamic_cheatsheet/language_model.py; operation and effect remain unobserved.
  • RT-RTE-1's continuation account mentions prior final_output values alongside latest cheatsheet. The outputs are loaded by the runner, but cumulative-only generation/curation consumes the cheatsheet and current interaction; historical raw outputs are consumed by retrieval/full-history branches. RT-RTE-1 annotation narrows that route-level memory interpretation; retrieval consumption remains in RT-RTE-2 and MEM-RTE-2/3/4.
  • RT-RTE-2 groups materially different selectors and consumers. MEM-RTE-2, MEM-RTE-3 and MEM-RTE-4 declare append-all, embedding top-k and synthesis routes as contained parts. The supplied record remains a valid grouping; the parts should retain their own classifications.
  • The source permits both a task evaluator and curator prompt. Runtime findings correctly state the evaluator's target is not a memory-admission oracle; no amendment is requested on that fact.
  • No observed operation, activation, memory correctness, improvement, embedding generation provenance, provider-side persistence, or host isolation can be recovered from the frozen evidence. These unknowns constrain conclusions as stated above.

Limitations and checks

Evidence is limited to the frozen source commit, supplied runtime findings and repository artifacts. Model-generated text is opaque and no run artifact is treated as an observed trace. The selected boundary does not expose provider internals or deployment permissions; it prevents conclusions about service retention, exact model versions, deployed execution controls, and realized benefit. Embedding provenance and any upstream dataset construction are also unavailable. The source scan found no separate hook registration, memory cleanup API, or content-level withdrawal route in the checked-in Python tree; this cannot rule out host- or provider-side behavior.

The source identity was rechecked against the boundary and generated quote anchors: SRC-1, https://github.com/suzgunmirac/dynamic-cheatsheet, commit 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9. The method identity remains the memory analyst correction job for run AAS-2026-10-03-dynamic-cheatsheet-02, within the supplied boundary. commonplace-validate --full kb/agentic-systems/reports/state/AAS-2026-10-03-dynamic-cheatsheet-02/memory-report-1.md passed cleanly with no warnings or failures.