Dynamic Cheatsheet report verification

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

Verification

The five submitted members support a bounded implementation account. I independently read the frozen source before judging their claims. The checkout HEAD matches the reviewed revision; its tracked top-level tree agrees with the boundary coverage. The supplied structural check reports none, and the four correction-answer inputs are empty. There are no structural findings or correction arguments requiring disposition.

Runtime, memory and workflow checks

I traced the complete driver and model wrapper, the extractor and local executor, all three prompts, and both notebooks' source cells. These are SRC-1 implementation and doctrine/design evidence at run_benchmark.py, dynamic_cheatsheet/language_model.py, dynamic_cheatsheet/utils/extractor.py, dynamic_cheatsheet/utils/execute_code.py, prompts/generator_prompt.txt, prompts/curator_prompt_for_dc_cumulative.txt, prompts/curator_prompt_for_dc_retrieval_synthesis.txt, ExampleUsage.ipynb and EvaluatingResults.ipynb. Notebook outputs were not used as observations.

The component and template accounts are supported: RT-CMP-language-model, RT-CMP-input-embeddings and RT-OBJ-generator-prompt distinguish provider computation, loaded numerical vectors and model-interpreted instructions. The same configured backend supplies generator and curator roles. The source issues no local training update, but does not establish immutable remote weights or embedding provenance.

I checked every advanced-generation branch against RT-RTE-cumulative-curation and RT-RTE-context-selection. Their ordering, context inputs, round differences and extraction fallbacks are supported. In particular, synthesis precedes current solving and falls back to assembled examples; cumulative replacement follows solving and falls back to the previous reference. Neither delimiter extraction nor similarity ranking checks truth. The opening-without-closing-tag forcing case follows directly from the extractor. The separately described previous-answer context is conditional within-call retention, not a cross-question answer corpus.

The envelope and its operative parts have the stated consumers: RT-OBJ-output-dict, MEM-OBJ-final-cheatsheet, MEM-OBJ-final-output, MEM-OBJ-steps and MEM-OBJ-retrieved-selection. I checked all four memory Part of: relations. Whole-reference replacement does not remove prior full outputs; scoring does not filter their retention. Steps retain snapshots, not a separate full curator response. Selection fields are not the driver's resume inputs. These distinctions support the memory write-side and read-back accounts without claiming complete rationale retention.

The local continuation and native dispatch records also hold: RT-RTE-local-execution, RT-RTE-native-execution and MEM-OBJ-container-id. Local execution precedes the depth test; direct-child timeout control is not isolation. Native dispatch precedes the local permission test, while advanced-generation native mode disables local execution. Text projection and handle reuse do not establish remote payload read-back or preservation. Provider parameter merges support the warning against interpreting a local execution flag as a universal provider-tool veto. I checked these paths in SRC-1, text_generation/simple_unified_client.py, including initialization, provider dispatch, parameter handling, retry and native-tool methods.

RT-RTE-checkpoint-continuation and RT-RTE-answer-scoring correctly assign persistence, positional restart and measurement to shipped consumers. The driver assigns reference state and appends full output before scoring, then rewrites JSONL. It restores the last reference and full-output list, not curator reasons or native handles. Partial argument equality and non-atomic writes cannot establish exact replay. The workflow account and workflow annotation faithfully select cumulative local execution, keep target/score outside the curator handoff, and show that answer-extraction failure does not block curation. Successful recovery or delivery in an actual run is not inferred.

Epistemic and authority checks

I independently read all active and auxiliary functions in SRC-1, dynamic_cheatsheet/utils/evaluation.py. EPI-RTE-choice-scoring is a genuine part of the broader scoring route. Its negated-option counterexample follows from substring acceptance. The arithmetic evaluators' host eval calls are separate effect boundaries, not controlled by generation-tool flags. The notebook's first-answer-tag and boxed-answer handling differs from the library extractor; equal online/offline accuracy cannot be assumed.

EPI-OBJ-final-answer and EPI-OBJ-target are distinct scored fields within the result envelope; their containment relations hold. EPI-OBJ-curator-prompt and EPI-OBJ-synthesis-prompt are distinct templates, not authority paths or generated reference instances. EPI-RTE-answer-generation and EPI-RTE-reference-admission identify shared stages spanning multiple runtime routes rather than duplicate complete loops.

The epistemic ledger separates requested model correctness assessment, syntactic disposition, retention, selection and later use. Its implemented statuses concern inspected plumbing; its doctrine-only correctness check concerns performance of the requested semantic judgment. Model-generated content relations remain indeterminate without inspected instances. The bounded lack of post-acceptance lifecycle integration is restricted to the inspected reference loop, not all provider or auxiliary evaluator behavior. These assessments are supported, not an unsupported system-wide absence.

RT-BAP-cheatsheet-context and RT-BAP-curator-guidance correctly distinguish solving and update consumers. Delivery supports advisory behavioral authority, not demonstrated reliance. RT-CLM-adaptive-memory, EPI-CLM-verified-solutions and EPI-CLM-zero-shot accurately retain the README and prompt claims while withholding verified-item, realized-learning and causal-benefit conclusions. The separate theory-builder condition assessments and standing improvement-directed mechanism do not assert exercised criticism, iteration, reflection or successful self-improvement.

Reconciliation and omission disposition

I checked all four supersessions in reconciliation. EPI-OBJ-final-output, EPI-OBJ-final-cheatsheet, EPI-OBJ-messages and EPI-RTE-chat-history identify the same referents as their memory counterparts. Supersession preserves lens-specific findings rather than replacing values. Valid containers remain alongside their parts. The remaining overlapping stages, templates, claims and authority paths are not duplicate identities. No unresolved cross-report conflict was found; requested verification versus unchecked admission is a source-system mismatch, not an analyst disagreement.

Consequential omission questions were selected from the frozen tree and producer/consumer dependencies: could a shipped consumer enforce score-based admission, could another read-back path alter the memory inventory, and could client tools bypass the execution/search boundaries? Full driver/library tracing, notebook source inspection and searches for extractor, history, token-counting and hook calls in the shipped Python roots resolved the first two within the claimed scope. Direct reads of SRC-1, text_generation/__init__.py and the complete text_generation/simple_unified_client.py resolved the separate exported history and search surfaces. MEM-OBJ-messages, MEM-RTE-chat-history, EPI-OBJ-search-results and EPI-RTE-search-acquisition preserve these routes without inserting them into the selected benchmark workflow. Raw search prefixes and structured provider metadata are not automatically retained as chat evidence.

I also inspected the auxiliary sonnet evaluator's entry point and doctrine in SRC-1, dynamic_cheatsheet/utils/sonnet_eval.py. Its helper internals remain unassessed; the reports already exclude a system-complete evaluator negative. Full result, notebook-output, dataset and embedding payloads remain uninspected, so measured benefit, source truth, preservation rates and component attribution remain withheld. External provider contracts remain outside the frozen evidence. These are faithfully scoped uncertainties, not defects requiring new verifier limits.

Further omission expansion stopped because the selected questions have supported dispositions, no identified material challenge remains unresolved, and the emitted account makes no exhaustive evaluator, semantic-quality or observed-benefit claim that would require payload or remote-system inspection. All declared records, their source anchors, evidence-strength assessments, containment relations, supersessions and integration dispositions were checked for meaning. None requires correction before publication of this bounded account.

Blockers

none

Limits

none