Dynamic Cheatsheet report verification

Type: agentic-system-analyses/types/agentic-system-verification.md

Verification

The submitted reports support a bounded implementation account. I independently inspected the frozen checkout, confirmed its HEAD equals the reviewed revision, and compared the top-level tracked tree with boundary coverage. The shipped driver and both notebook callers remain inside the account; provider internals are not attributed to the wrapper. The supplied report-check says none; all four correction-answer files are empty. There is no structural finding to route and no correction claim to presume established.

Runtime and delivery

I traced SRC-1, run_benchmark.py in full and dynamic_cheatsheet/language_model.py in full, rather than relying on the reports' own checks. RT-RTE-benchmark-cycle, RT-OBJ-output-dict and the workflow account correctly separate library return, caller state handoff, scoring and disk persistence. The actual driver supplies question/corpus slices but not targets to generation, adopts the returned sheet before scoring, and writes after scoring. Resume restores the last row's non-null sheet and all final outputs, not hosted state; its argument checks are partial and file rewriting is non-atomic. Best-effort recovery, not exact replay, is supported.

All six approach branches were checked against RT-RTE-context-selection. Pure retrieval reverses selected pairs for display; synthesis precedes the answer and falls back to raw pairs; hybrid combines retrieval with one post-answer cumulative update. Repeated rounds and optional previous-answer injection occur in cumulative mode. Default supplies an empty reference to generation and returns no retained sheet. These differences support the reports' context-selection findings without implying a token-fit guarantee.

I inspected SRC-1, dynamic_cheatsheet/utils/execute_code.py and the wrapper's execution branches for RT-RTE-local-execution and RT-RTE-provider-execution. Execution precedes the depth comparison, so the depth-four case and terminal transcript duplication are supported. Ambient subprocess execution is not isolation. The native branch precedes the local guard; a direct supported API call can request native execution with local execution disabled and a truthy session identifier. Missing native identifiers have the different fallthrough behavior described in the runtime report.

I also checked ordinary and native provider request assembly, dispatch and retries in SRC-1, text_generation/simple_unified_client.py. OpenAI and Claude forward tool-bearing keyword arguments, while Gemini and xAI restrict ordinary passthrough. Thus the local flag is not a universal provider-tool prohibition, including at curator calls. Whether a provider accepts and exercises those requests remains external and unobserved. RT-CMP-language-model correctly separates no source-owned weight update from unknown provider internals and mutable model aliases.

Memory identities and consumers

I followed each retained payload from producer through extraction, caller handoff and later prompt construction. MEM-OBJ-cheatsheet, MEM-OBJ-final-output and MEM-OBJ-previous-answers are genuinely different parts of the result envelope: replacement guidance, raw rendered solution text, and round-labeled extracted answers. Their Part of: relations are supported by the returned dictionaries. Current generator output reaches the cumulative curator; later retrieval uses final outputs, not targets or every intermediate round. Old sheet versions can remain archived without an automatic old-entry restoration route.

MEM-OBJ-input-embeddings correctly identifies selector inputs rather than an answer body or updated model. Exact-string reordering and slicing are implemented; provenance and vector quality are not established. MEM-OBJ-container-id correctly keeps the identifier, opaque hosted payload and returned display text distinct. Repeated OpenAI identifier supply does not establish actual hosted-state reuse, and Claude's sentinel establishes no equivalent durable container.

For MEM-OBJ-messages and MEM-RTE-history-controls, I read adapter generation, chat and history-control methods. Reset/replacement is an exposed affordance; explicit wrapper messages bypass that history management. The reports do not turn an unspecified external caller into a deployed reload route. The fresh generator/curator contexts and extracted return explain context isolation without asserting semantic preservation or successful compression.

Checking, warrant and authority

The three shipped prompts, full extractor and full task-evaluation implementation were read independently. RT-RTE-cumulative-update, EPI-RTE-curator-review and EPI-RTE-sheet-admission preserve the consequential distinction: requested semantic review versus deterministic substring admission. The review is a proper subroute of the cumulative update, not an independent oracle. Empty bodies and missing closing tags can be admitted; missing opening tags select the route-specific fallback. The epistemic report's concrete extraction deduction follows the implementation without claiming execution.

EPI-OBJ-target, EPI-OBJ-score-result and RT-RTE-score-answer correctly bound scores to their predicates. I checked the multiple-choice negation counterexample against every earlier acceptance branch and the target-text extractor. I separately read all evaluator notebook cell source, excluding saved outputs: its first-tag extraction differs from runtime last-tag extraction as stated. GameOf24 evaluates generated text independently of the local generation flag. None of these checks warrants explanations or strategy transfer.

For EPI-ABS-score-feedback, full driver/wrapper/extractor flow and evaluator cell-source consumers support the bounded absent feedback edge: ordinary false results alter counters, not sheet admission or later reference selection. This is not an absence of checking generally or of opaque model criticism. The ledger's separate transformation, checking, admission and retention functions are supported; its indeterminate classifications do not mislabel unseen model output as demonstrated conjecture or derivation. Theory-builder, learning, reflection, autonomy and self-improvement qualifications remain separate and do not negate the implemented text-update pathway.

RT-BAP-reference-context accurately locates advisory semantic authority at generator interpretation and distinguishes it from enforced text selection. RT-CLM-adaptive-memory, EPI-CLM-verified-solutions and EPI-CLM-performance-gains match SRC-1 README/prompt claims. Wiring does not independently establish their requested reliability or reported gains.

All declarations, source anchors, evidence statuses and containment relations were assessed, including EPI-OBJ-cheatsheet and EPI-OBJ-generator-output. Their supersessions in reconciliation connect identical payloads to the memory records without replacing their findings. The envelope remains a valid container. Distinct route functions, target/score objects and the three claims are not improperly collapsed. Integration issues have dispositions; no conflicting findings or missing material part requiring an unresolved-conflict paragraph was identified.

Omission questions were selected from the frozen executable tree and producer/consumer dependencies: could another shipped caller close score-to-memory feedback, could another approach bypass the stated admission/timing account, and could provider request assembly bypass the local execution control? Full branch/caller tracing resolved these questions as above. I inspected both notebooks' cell source, searched Python callers of solution extraction, score results and history controls, and checked package exports. The auxiliary poetry evaluator's interface and call search establish a separate checking capability, not a discovered sheet-admission consumer. Its internals and standalone acquisition APIs remain outside the reports' assessed route claims, not evidenced absences.

Further optional expansion stopped because the selected challenges have supported dispositions and no material contradiction remains. Historical result payloads, dataset/vector contents and provider internals would be needed for quality, provenance, exercised behavior and causal comparisons, which the reports already withhold. Their availability alone does not defeat this bounded implementation account. No target code, tests, examples or provider calls were executed. Quotations retained in the cited records supply the load-bearing passages; source inspection here independently checked their meaning and consumer paths, not merely occurrence.

Blockers

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Limits

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