Dynamic Cheatsheet epistemic report

Type: agentic-systems/types/agentic-system-epistemic-report.md

Source-and-claim boundary

The question is whether the shipped Dynamic Cheatsheet routes acquire or produce truth-apt content, check it, grant reliance, retain or integrate it, and let it affect later behavior. The frozen target is the whole shipped repository, including benchmark runner, core library, prompts, provider adapter, evaluation helpers, datasets and result consumers (boundary, SRC-1, SRC-2, and SRC-3). I directly inspected run_benchmark.py, dynamic_cheatsheet/language_model.py, dynamic_cheatsheet/utils/extractor.py, dynamic_cheatsheet/utils/evaluation.py, and the three shipped prompt files. The runtime member supplies the ordinary and alternate route wiring, persistence/reload flow, and code execution account; it does not establish observed provider outputs or runs. The shipped scoring code is additional implementation coverage because it determines what the runner calls correct and whether that result affects memory.

Entry point or operation	Source path	Coverage	Limit / conclusion prevented
Cumulative generation, curation and continuation	run_benchmark.py; dynamic_cheatsheet/language_model.py; dynamic_cheatsheet/utils/extractor.py	RT-RTE-1; RT-OBJ-1; direct reads	Provider responses, correctness of candidate entries, and behavioral benefit are unobserved.
Retrieval, full-history and retrieval synthesis	dynamic_cheatsheet/language_model.py; retrieval prompt	RT-RTE-2; RT-OBJ-2; direct reads	No execution trace establishes selected examples, synthesis content, or changed answer behavior.
Dataset-answer evaluation and score output	run_benchmark.py; dynamic_cheatsheet/utils/evaluation.py	EPI-RTE-1; direct reads	Evaluation implementation is inspected; no run establishes its actual results or dataset provenance.
Prompt-directed answer and curator checks	prompts/generator_prompt.txt; cumulative and synthesis curator prompts	RT-CLM-1; SRC-2; direct reads	Instructions do not establish that a model performed the requested checks or that any result was correct.
Provider inference and provider code execution	text_generation/simple_unified_client.py; dynamic_cheatsheet/language_model.py	runtime coverage via RT-CMP-1 and RT-RTE-4	Provider internals, exact model versions, service-side checking and execution controls are inaccessible.
Notebook and checked-in result artifacts	ExampleUsage.ipynb; EvaluatingResults.ipynb; results/; figures/	boundary coverage SRC-2/SRC-3; not re-evaluated as runs	Provenance, run conditions and causal attribution remain unavailable.
Other checks, admission vetoes, or lifecycle integration	inspected target paths above	no other operative route found within these paths	This bounded source inspection cannot exclude informal human review or unobserved deployments.

The README and curator prompts describe accuracy and verification aims, but no public claim with an evidence-backed acceptance route was found beyond RT-CLM-1. The curator says to assess correctness and retain tested solutions; the code passes its output through tag extraction/fallback and adopts the resulting text. No comparator or evaluator result gates that adoption. Scoring happens after answer and cheatsheet assignment and is not passed to the curator.

Epistemic-object inventory

object ID	candidate truth-apt content or none	claimed role	evidence source ID and local anchor	gap/limit
EPI-OBJ-1	Extracted final answer and free-form answer/solution text	Proposed response to the current task; may assert an answer	SRC-1 dynamic_cheatsheet/utils/extractor.py; run_benchmark.py	Model output is opaque; no trace identifies actual assertions or their truth.
EPI-OBJ-2	Dataset target and task input	Reference outcome and named problem domain for benchmark scoring	SRC-1 run_benchmark.py; dynamic_cheatsheet/utils/evaluation.py	Dataset provenance and target correctness are not established by this repository.
RT-OBJ-1	Cumulative cheatsheet text; may mix factual claims, heuristics, examples and instructions	Persistent context for later model calls	SRC-1 run_benchmark.py; see RT-OBJ-1	No instance provenance or entry-level truth assessment is supplied.
RT-OBJ-2	Prior questions, generated outputs and associated supplied vectors	Retrieved or appended context for a later answer	SRC-1 run_benchmark.py; see RT-OBJ-2	Prior generated answers are not themselves verified truths; vector provenance is unavailable.

Authority-route ledger

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: behavior permitted, blocked, or changed	behavioral-authority path: consumer, channel, force, horizon	evidence source ID and local anchor	claim IDs or none	mismatch marker or none	gap/limit
RT-RTE-1	content transformation	implemented	RT-OBJ-1; EPI-OBJ-1	truth-apt transformation: indeterminate — answer/cheatsheet text may preserve, reshape or add claims; determine per output and entry	Current question and prior text to generated answer and proposed new cheatsheet	Opaque provider model proposes; Python extracts delimited answer/cheatsheet or retains old cheatsheet. No correctness evaluator gates update.	Every benchmark item and each configured generation/curator round	Candidate answer and replacement text; actual results unobserved	Extracted cheatsheet is adopted by runner and sent in later prompts	No warranted truth license from generation, formatting or prompt guidance alone	Changes future context; no rejection other than extraction fallback	Later model invocation; prompt context; permissive influence over generation; subsequent examples and resumed runs	SRC-1 dynamic_cheatsheet/language_model.py; dynamic_cheatsheet/utils/extractor.py; see RT-RTE-1	RT-CLM-1	curator correctness aim exceeds implemented admission check	No execution evidence; entry-level semantics and accuracy are unknown.
RT-RTE-2	content transformation	implemented	RT-OBJ-2; RT-OBJ-1	truth-apt transformation: non-ampliative reshaping for deterministic selection/concatenation; synthesis result is truth-apt transformation: indeterminate pending its content	Earlier question/output pairs and supplied embeddings; current question	Python top-k cosine ranking or append-all ordering; in synthesis mode opaque model proposes text and extractor accepts delimited output or falls back	Per retrieval/full-history invocation; synthesis occurs in synthesis variants	Selected context and optional synthesized text; actual selection/results unobserved	Selected content is inserted in prompt; synthesis may become returned cheatsheet	No truth authority follows from similarity or inclusion; generated prior answers remain unverified	Alters which examples reach generator; synthesis changes context supplied to subsequent generation	Generator (and synthesis curator); prompt channel; ranking/context force; current item, with synthesis sometimes persisted for later continuation	SRC-1 dynamic_cheatsheet/language_model.py; synthesis prompt (SRC-2); see RT-RTE-2	none	none	Supplied embeddings' provenance and any model effect are unknown.
EPI-RTE-1	check/evidence production	implemented	EPI-OBJ-1 against EPI-OBJ-2	no content change	Extracted final answer against task target or task-specific constraints	Python task branch: exact text after limited punctuation removal for AIME; answer-pattern matching for multiple-choice; arithmetic expression evaluation plus input-number comparison for GameOf24; numeric/operator constraints for equation balancing	After output and cheatsheet have been adopted; after each processed example	Boolean correct/incorrect used in running aggregate; no curator input or answer revision	Reports score and persists the output row; no admission, rejection, rollback or future selection is conditioned on the boolean	At most a benchmark criterion-specific pass/fail signal for this answer and supplied target; not a general truth warrant	No effect on the current answer or memory admission; no check-mediated future behavior is wired	Benchmark runner and saved result consumers; score/output channel; reporting force; current benchmark summary and JSONL horizon	SRC-1 run_benchmark.py; dynamic_cheatsheet/utils/evaluation.py	none	none	No executed run is supplied; target quality and evaluator fidelity outside the implemented criterion are unestablished.
RT-RTE-3	operational admission/selection/consumption	implemented	EPI-OBJ-1 (generated code/output)	non-truth-apt policy/content update: executes model-emitted code and appends returned output to the generation conversation	Model-produced code marked for execution	Literal marker/parser and local Python process; no code correctness or safety check	When enabled and marker protocol is met, during generation	Process output/error/timeout can enter next model call; execution unobserved	Executes with benchmark process permissions; not a truth check	No epistemic license; output is evidence only for whatever process actually returned, not its claimed interpretation	Can cause host-side effects and change subsequent generation context	Local interpreter and follow-up model request; executable channel; enforcing execution force; one process with possible lasting side effects	SRC-1 dynamic_cheatsheet/utils/execute_code.py; see RT-RTE-3 and RT-BAP-1	none	none	Actual execution and deployment controls unobserved; RT-BAP-2 separately covers provider-side execution.

The scoring implementation is a source-inspected deduction: it consumes final_answer and original_target after storing the row and assigning cheatsheet; it does not pass its result into curation or the next route. The score therefore evaluates benchmark outputs but does not check or accept memory claims. A boolean match cannot warrant reasoning, transfer, or the accuracy of other entries.

The answer route is an ampliative candidate-generation route relative to the question/context: generated answers are not entailed solely by those inputs. Whether any particular claim is true requires an external criterion or evidence. The curator's semantic update cannot be assigned a single relation from its prompt or output wrapper: it may copy/reorder prior text, remove it, or introduce claims. For a specific update, compare the prior and resulting text and check any novel proposition against warranted premises; absent those artifacts the relation remains indeterminate. No content-level acceptance route for memory entries is implemented.

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
RT-CLM-1	Curator should assess correctness and retain tested/proven reusable material	SRC-2 prompts/curator_prompt_for_dc_cumulative.txt (doctrine/design)	Prompt states a correctness assessment and tested/proven criterion.	RT-RTE-1; EPI-RTE-1	None supplied	None; no controlled comparison or trace	The design requests a correctness assessment; no implemented acceptance criterion enforces it.	Curator output is parsed and adopted without scoring gate; task score runs afterward and is not fed back.

Bounded conclusion

Dynamic Cheatsheet wires model-produced answers and curated text into current and later prompts. It retains cumulative text through runner-managed JSONL and retrieval/full-history variants expose selected earlier questions and model outputs. These routes produce or carry candidate truth-apt content, but generation, semantic curation and delivered context do not themselves establish warrant. The curator prompt requests correctness review, yet no operative evidence-consuming check gates its proposed memory text. Parse failure preserves the prior text; successful extraction is enough for adoption.

The benchmark runner checks each extracted answer against a task-specific target after adopting the memory update. That check has a narrow scoring license: it can report conformity to the supplied target and implemented matching rule. It does not validate explanations or memory entries, and its boolean does not alter admission, answer revision or later context. Targets and dataset provenance are not independently validated here. No acceptance-and-integration route for curated knowledge is evidenced.

The report establishes inspected implementation, not operation: provider outputs, retained content, check outcomes, activation and improvement remain unobserved. The result notebooks and figures are not independent execution evidence in this boundary. No causal claim about performance or memory benefit follows from this source set. Local model-requested code execution has operational authority under the process environment and is not a knowledge check; provider-side execution has separate, uninspected service controls.

Shared records

Operative objects

EPI-OBJ-1 — Extracted final answer

• Source-native identity: final_answer, extracted from model response by extract_answer and consumed by the benchmark evaluator.
• Representational form: explicit text; may encode propositions, computations or non-propositional output depending on task.
• Storage substrate: call result and benchmark JSONL output row.
• Evidence: run_benchmark.py; dynamic_cheatsheet/utils/extractor.py (SRC-1).
• Identity comparison: distinct from RT-OBJ-1 and RT-OBJ-2; those are retained state/history, while this is the current response candidate. No supplied record names this answer object.

EPI-OBJ-2 — Benchmark target and task criterion

• Source-native identity: dataset target paired with task input and interpreted by the task branch in run_benchmark.py.
• Representational form: reference text / expression; the criterion also imposes task-specific symbolic comparisons.
• Storage substrate: supplied dataset, including checked-in files or configured dataset loader.
• Evidence: run_benchmark.py; dynamic_cheatsheet/utils/evaluation.py (SRC-1).
• Identity comparison: distinct from RT-OBJ-1 and RT-OBJ-2; this is a scoring reference, not retained model context. No supplied record names the evaluator target.

Routes

EPI-RTE-1 — Post-answer benchmark scoring

• Endpoints and progression: EPI-OBJ-1 plus EPI-OBJ-2 → task-specific Python evaluator → Boolean result → running score and saved output row.
• Trigger and principal: each processed benchmark example; runner dispatches one evaluator based on task name.
• Next-step owner and decision policy: Python evaluator returns a boolean under a task-specific comparison. No result-consuming admission or revision decision follows.
• Context and state: current final answer, task input and dataset target. The evaluator does not receive the cheatsheet as a check target.
• Action effects and boundary: increments score counters and prints aggregate; it does not change answer, cheatsheet, retrieval history, or later task selection.
• implementation conclusion status: wired
• operation conclusion status: uninspected
• Guarantee strength: no claimed guarantee
• Immediate return: Boolean task-match result.
• Later read-back: inapplicable — no later invocation reads the score as an answer-check or memory-admission signal; output rows can be loaded for continuation but the score result itself is not a curator input.
• Delegated visibility: inapplicable — check is local Python over runner values; no evaluator delegation is wired.
• Selection predicate: task identifier dispatches GameOf24, AIME exact-match, multiple-choice, or equation-balancer comparator; unsupported tasks raise an error.
• Invalidation or expiry: inapplicable — the result is a per-example score contribution, not a retained content claim.
• Activation or effect: score counter and printed aggregate change; no effect on answer generation or memory acceptance is wired.
• Evidence limits: implementation inspected in run_benchmark.py and dynamic_cheatsheet/utils/evaluation.py (SRC-1); no executed run or independent validation of targets is supplied.
• Evidence: task dispatch and score/update order:

if args.task == "GameOf24": result = eval_for_GameOf24(original_input, final_answer) elif args.task in ["AIME_2025", "AIME_2024", "AIME_2020_2024"]: result = eval_for_exact_matching_with_no_punctuation(final_answer.lower(), original_target.lower()) elif args.task in ["GPQA_Diamond", "MMLU_Pro_Engineering", "MMLU_Pro_Physics"]: result = eval_for_multiple_choice(current_input, final_answer, original_target) elif args.task == "MathEquationBalancer": result = eval_equation_balancer(None, final_answer, original_target) --- run_benchmark.py:290-297 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The check's timing is explicit: the row and new cheatsheet are assigned before task scoring.

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:274-281 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

The correctness result only increments reporting counters and the row is saved after that; this code does not send the result to the curator or use it to revise state.

if result: correct_so_far += 1 total_so_far += 1

    print(f"---- Correct so far: {correct_so_far}/{total_so_far}")
    print("###" * 50)

    # Save the outputs to a file after each example (for crash recovery)
    write_jsonl(args.save_path_name, outputs)

--- run_benchmark.py:301-309 @ 5cfe3c37e8e52b1d858d0f3df46e7f17c50991b9

Components

none declared in this member

Routes

none declared beyond EPI-RTE-1

Evidenced absences

none declared in this member

Behavioral-authority paths

none declared in this member