GMemory report verification

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

Verification

The current reports support a bounded implementation account. The three previous material objections are repaired. One stale attribution in memory remains a local reporting limit, not a competing account of implemented filtering. I read all supplied inputs, including correction answers, the previous verification, types and contracts. The artifact check reports none; there is no structural failure to route. Runtime supplies one corrected answer and epistemic supplies two; memory and workflow answers are empty. Answers were checked against source and the current reports, not accepted as proof.

Independent inspection used SRC-1 under the Source register. Checkout HEAD matches the reviewed revision and source status was clean. All reads below supply implementation or doctrine/design evidence. No target code, provider, test or experiment was run. I checked the meaning of every declared record, its supporting anchors, conclusion statuses, containment and consumer claims; deterministic citation and quotation checks remain the validator's responsibility.

Runtime and workflow

Reads of SRC-1, run_mas.sh, tasks/run.py, mas/module_map.py, mas/agents/base.py, mas/reasoning/reasoning_modules.py, mas/llm.py and mas/utils.py support the runtime account. The launcher and direct CLI converge on a fixed module map and one reused memory object. The default memory-key and README argument limitations are real source discrepancies. Fresh system/user pairs do not imply task-history removal: the workflow rebuilds trajectory-bearing prompts. The component assessments in RT-CMP-gpt-chat and RT-CMP-embedding-func correctly distinguish model-name resolution from immutable weights and local inference from provider-side learning.

Complete reads of all three schedules and their neuron/node helpers support RT-RTE-autogen-actions, RT-RTE-dylan-actions and RT-RTE-macnet-actions. These are local orchestration routes, not independent worker-security principals. Repetition selects a substitute persona; consensus and ranking select candidates; neither supplies an external correctness oracle. DyLAN clears local caches per step and saves without backward. MacNet's latest temporal outputs can cross task resets when temporal edges exist. SRC-1, tasks/mas_workflow/macnet/graph.py, confirms that graph masks can supply those edges; actual realization remains uninspected. Empty responses continue before retry advancement, supporting RT-CLM-bounded-recovery, not a global deadline. The records appropriately separate wired implementation from uninspected operation.

The selected workflow account and annotations accurately follow one ALFWorld/AutoGen schedule rather than joining unrelated capabilities. SRC-1, tasks/mas_workflow/autogen/autogen.py, tasks/mas_workflow/autogen/autogen_prompt.py, tasks/envs/alfworld_env.py, tasks/envs/base_env.py and tasks/run.py, support the handoff inputs, normalization before repetition testing, stronger-than-predicate helper instructions, environment-derived final label, and synchronous save/backward before recorder delivery. Neither negative step reward nor helper agreement is an independent acceptance gate. No workflow correction is required by the repaired retention finding.

Memory, transformation and authority

I read SRC-1, mas/memory/mas_memory/GMemory.py, mas/memory/common.py and mas/memory/mas_memory/memory_base.py completely, along with the GMemory prompt definitions and formatter. RT-RTE-memory-delivery, its memory annotation and RT-BAP-retained-guidance correctly trace task-start selection through optional projection to advisory user-message text. Candidate fallback replaces a category list; count limits are not token limits; an empty numbered-list parse defeats exception fallback. Positive task associations rank insights, while scalar scores govern survival. Delivery establishes neither compliance nor benefit.

All six memory objects have supported, distinct roles: MEM-OBJ-mas-message is readable task text and derived extras; MEM-OBJ-state-chain is serialized interaction structure; MEM-OBJ-task-layer is an access graph; MEM-OBJ-insights-rule is interpreted rule text; MEM-OBJ-insights-metadata controls selection/survival; MEM-OBJ-node-memory is transient temporal history. Initial-store provenance remains unknown without obscuring subsequent software/model writes. Raw graph payloads and maintenance logs are not automatically replayed as solver-visible rationale.

The correction to RT-RTE-task-retention is supported. _extract_mas_message calls pop_state, but the latter pops the fresh slice returned by chain_of_states; backing states remain unchanged. Successful trajectory reconstruction precedes digit erasure, and clean_traj feeds key-step extraction and failure diagnosis. The runtime route now preserves those distinctions in progression, visibility, selection and evidence. MEM-RTE-state-roundtrip also correctly identifies the missing trailing open state after deserialization, limiting loaded graph fidelity rather than proving loss of separately retained display text. Boolean-equivalent label checking, graph-before-document writes and lack of cross-store transactionality are supported.

RT-RTE-insight-maintenance, MEM-RTE-cluster-merge and the maintenance annotations distinguish operation parsing from numbered-list merge/rebuild. The nominal rule threshold is not a hard ADD cap. Merge resets metadata and bypasses ordinary operation checks; backward credits cached raw substrings, not verified use of projected guidance. SRC-1, mas/memory/mas_memory/GMemory.py and mas/memory/mas_memory/prompt.py, support automatic proposal/admission, syntax and index rejection, score pruning and the bounded lack of semantic rollback in these routines. The independent theory-builder, learning, reflection and autonomy assessments do not upgrade requested causal criticism into established content-dependent criticism or demonstrated improved capacity.

Epistemic overlay and reconciliation

I checked every epistemic object and ledger function against its declared record and actual consumer. EPI-OBJ-task-trajectory, EPI-OBJ-key-steps and EPI-OBJ-fail-reason retain different evidence views and interpretations. EPI-RTE-trajectory-cleanup, the inventory, ledger and bounded conclusion now consistently describe ineffective attempted deletion separately from effective numeric erasure. The numeric counterexample is static reasoning, not an observed run. No dependent assertion of effective reward pruning remains in epistemic.

EPI-OBJ-insights-rule, EPI-OBJ-insights-score and EPI-OBJ-projected-insights preserve text, access metadata and transient transformation distinctions. Indeterminate semantic relations remain appropriate without concrete outputs. In EPI-RTE-rule-disposition, the repaired AGREE countercase follows the complete lookup: no match returns -1, so valid numeric checking can still be followed by credit to the last rule. It no longer asserts a None indexing failure. The bounded conclusion carries the corrected consequence. Syntax/index admission does not supply semantic acceptance.

Direct reads of SRC-1, tasks/envs/fever_env.py, tasks/envs/utils.py, tasks/envs/sciworld_env.py, tasks/envs/alfworld_env.py and the PDDL wrapper's outcome routines support EPI-OBJ-wiki-document, EPI-RTE-wiki-acquisition, EPI-RTE-fever-check and EPI-RTE-subgoal-check. Imported paragraphs have unknown source warrant and no enforced displayed URL. Normalized label equality and latched regex completion have narrow licenses; wrong FEVER Finish termination is not final success. The ledger properly separates checks, operational admission, retention, selection and adaptation from semantic acceptance and post-acceptance integration.

SRC-1, README.md and the GMemory prompts support RT-CLM-evolving-memory, EPI-CLM-general-principles and EPI-CLM-output-improvement as attributed design claims. A fresh analyze_mas_pattern search across mas/ and tasks/ found definitions and attribute exposure, not a consequential caller. The bounded self-improvement account withholds exercised dependence and successful improvement; neither persistence nor output format closes those evidential gaps.

I checked every annotation, Part of: relation, supersession and integration disposition in reconciliation. The two supersessions correctly identify the same rule-text and access-metadata objects without erasing specialist findings. Other parts do not replace valid containers. Environment subroutes are not exclusive to AutoGen merely because that route is their declared container. No evidenced-absence records are declared. Reconciliation correctly recognizes the repaired filtering and AGREE findings. Its remaining conflict identifies both positions, source evidence and the prevented conclusion: memory still attributes the former filtering claim to the now-corrected runtime. That local stale attribution is retained as a limit below, not mistaken for unresolved implementation behavior.

Omission questions and stopping decision

The selected omission questions were whether shipped callers bypass the described lifecycle, whether alternative admission/read paths change guidance authority, and whether another evaluator supports stronger warrant or improvement. The search basis was the frozen tree, all three complete schedules, direct model callers, persistence loaders/writers and consumer dependencies. Fresh call-site searches for save, backward, projection and clear-state corroborate the retained consumer differences and the optional trajectory-conditioned projection branch. SRC-1, tasks/prompts/__init__.py, tasks/configs.yaml and tasks/envs/__init__.py, additionally connect task instructions, examples, configuration and eager dataset loading to the shipped runner. Critic/ranker/decision prompt reads confirm model judgments rather than an additional independent oracle.

Selected headers of the three registered logs/ files show retrieval settings and task starts, but do not identify GMemory mode or authenticate the generating revision. They do not justify upgrading operation status. Dataset contents, remaining static examples and simulator internals remain unassessed for coverage, contamination and correctness, as already scoped by the reports. These are not evidenced absences. Further optional omission search stopped because the selected challenges have supported dispositions and no identified material challenge remains without disposition; an exhaustive prompt/data/dependency audit is not needed for these bounded implementation conclusions.

Blockers

none

Limits

  • Memory's annotation on RT-RTE-task-retention and integration issue 1 still say that the supplied runtime asserts effective negative-state removal. The current route instead explicitly describes ineffective slice-only deletion, consistent with MEM-RTE-state-roundtrip and SRC-1, mas/memory/common.py and mas/memory/mas_memory/GMemory.py. Reconciliation accurately isolates this stale attribution. Readers should withhold the inference that a current runtime correction remains necessary or that the reports still disagree about implemented filtering. This is local: the memory report's own mechanism finding is correct, the current runtime and epistemic findings agree with it, and no emitted curation or comparison finding depends on the obsolete attribution.