OpenRSI report verification

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

Verification

The pinned reports support a bounded implementation account. No material correction is required. I read all supplied members, their types and the record contract; correction-answer inputs are empty and the structural report-check says none. I rechecked checkout HEAD against the boundary. Additional source inspection used only SRC-1 at that revision. No target code, tests, services or models were executed. The boundary describes the source release, including separately launched stages; it does not assert that a single process closes their loop.

Record support and consequential conditions

I assessed every declaration, annotation, source anchor, status and containment relation in the four reports. I checked the reconciliation's two supersessions, integration dispositions and claim that no unresolved conflict remains. The findings below concern meaning and dependencies, not a repeat of deterministic link or quotation checks.

  • Runtime components, invocation and search. The client constructions and configuration support distinct operator roles, not distinct pinned weights: RT-CMP-generic-llm, RT-CMP-openai-provider, and RT-CMP-training-models. Source inspection of SRC-1, OpenMLE-Evo/third_party/aira-evo/src/dojo/solvers/evo/evo.py, the GenericLLM renderer and LiteLLM backend, and OpenMLE-Gym/builder_core/utils/chat.py supports those distinctions. The ordinary search, async scheduling and evaluator records retain different owners and grants: RT-RTE-evolution-search, RT-RTE-async-search, RT-RTE-sandbox-evaluation. I traced parser ordering, island admission, commit checks and runner guards. Finite deterministic fitness can override model analysis; experience-disabled operation still invokes Analyze for every node. Test-score override and explicitly trusted printed validation scores prevent an unconditional independent-oracle interpretation. These conditions are preserved across the reports.
  • Execution and artifact admission. RT-OBJ-metric, RT-RTE-gym-build, RT-RTE-naturebench-execution, and RT-BAP-execution-authority distinguish compilation, dynamic metric loading, direct preparation execution, isolated preparation and NatureBench local/Docker/SCM dispatch. I checked SRC-1, OpenMLE-Gym/builder_core/utils/nodes.py, OpenMLE-Gym/openmle_gym/process_runner.py, sandbox metric/dispatcher/launcher paths, and OpenMLE-Evo/third_party/aira-evo/examples/nature_bench/base_task.py. Container flags do not establish deployed containment. Dispatcher kill-on-deadline does not prove complete descendant termination. The records correctly avoid those stronger conclusions.
  • Retained search experience. MEM-OBJ-journal, MEM-OBJ-experience-card, MEM-OBJ-rich-summary, MEM-RTE-summary-cache, and MEM-RTE-targeted-memory describe distinct state and transformation parts. I checked export/reload, summary precedence and normalization, related-node selection, and the operator consumer against SRC-1, OpenMLE-Evo/third_party/aira-evo/src/dojo/core/solvers/utils/journal.py, OpenMLE-Evo/third_party/aira-evo/src/dojo/solvers/evo/experience.py, the Evo solver, Improve operator and rich-summary operator. Nested metric-info export does not match flattened-key reload. Existing node/card/cache summaries take precedence over generation. Shared-parent sibling selection and count/character clipping support targeted delivery, not complete evidence transfer or a total token ceiling. RT-BAP-operator-guidance correctly gives this delivery model-interpreted rather than executable force.
  • Training and SQL memory. RT-RTE-sft-training, MEM-OBJ-sft-program, MEM-RTE-sft-search, and MEM-OBJ-alignment-dataset keep production history, dataset admission, minimal training conversations and weight updates separate. Pipeline predicates, GreedySearch selection, checkpoint preload and alignment projection support this account. SRC-1 anchors include OpenMLE-ERL/SFT/tts_search/data_produce/pipeline.py, OpenMLE-ERL/SFT/tts_search/data_produce/alignment.py, OpenMLE-ERL/SFT/tts_search/greedy.py and OpenMLE-ERL/SFT/tts_search/services/scheduler.py. In RL, RT-RTE-rl-training, MEM-OBJ-rl-program and memory's annotation preserve conditional insertion, fitness recomputation, physical top-k pruning and consumer-specific eligibility. SRC-1, OpenMLE-ERL/RL/generate_mle.py, OpenMLE-ERL/RL/program_database.py, and OpenMLE-ERL/RL/airaevo_experience.py support those distinctions. In particular, storage of penalized categories does not imply active-success parent eligibility. RL children-of-parent context is not Evo's shared-parent sibling context. External RL optimizer internals remain unestablished.
  • Epistemic checks and dispositions. EPI-RTE-code-extraction correctly describes last-block code selection, not preservation of the full response. EPI-OBJ-task-evaluation, EPI-RTE-package-validation, EPI-RTE-quality-judgment, and EPI-RTE-quality-disposition distinguish deterministic findings, model judgment, report-schema acceptance, retention and CLI failure. SRC-1, OpenMLE-Gym/openmle_gym/metric_validation.py and OpenMLE-Gym/openmle_gym/local_evaluator.py, support finite sample execution as a smoke check. Low semantic recommendation alone does not cause the failure helper to reject the command. EPI-OBJ-hack-verdict, EPI-RTE-hack-judgment, and EPI-RTE-hack-disposition correctly separate code review, permissive failure paths, missing-credential exceptions, execution bypass, penalties and retention; checked against SRC-1, OpenMLE-ERL/RL/reward_func_utils.py and the generation/reward consumer. The summary object's and route's epistemic assessments remain candidate-content assessments after supersession, not acceptance of explanatory claims.
  • Workflow delivery. WF-RTE-final-delivery and the repair annotation are supported by SRC-1, OpenMLE-Evo/third_party/aira-evo/examples/mle_bench/single_task_runner.py, OpenMLE-Evo/third_party/aira-evo/examples/mle_bench/runner.py and the solver's Debug cycle. Selection can fall back to random code-bearing history; writing code precedes optional final evaluation. Zero subprocess exit, strict resume completeness and program validity are different predicates. These qualifications prevent the filenames and process status from becoming unsupported quality guarantees.

All route declarations' wired implementation and uninspected operation statuses are consistent with their static evidence. The two claim records, RT-CLM-meta-evolution and EPI-CLM-verifiable-packages, remain attributed design claims. No report declares an evidenced absence. The epistemic overlay does not infer accepted ampliative content from retention, or causal explanation from score differences. Its indeterminate classifications for unseen generated prose remain appropriate. Neither training wiring nor prompt delivery establishes exercised self-improvement or improved capacity.

Relations and reconciliation

Every Part of: relation denotes a proper artifact or route segment inside its supplied container. Cross-kind object-to-route relations describe retained workflow state, not identity. Post-build evaluation routes are distinct from the Gym build graph; no missing split is needed to preserve their findings.

The reconciliation correctly identifies EPI-OBJ-rich-summary with the memory summary object, and EPI-RTE-summary-generation with the memory cache/generation route. Both pairs share source-native identity, endpoints and status; targeted rendering remains separate. Neither amendment discards a differing warrant assessment. I found no unresolved source-supported contradiction requiring an Unresolved conflict: paragraph. In particular, journal fidelity, empty graph updates under add_messages, legacy Analyze activation and terminal fallback qualify compatible findings rather than contradicting explicit universal claims. Empty correction answers provide no additional evidence or repairs.

Omission questions and stopping decision

I selected omission questions from the tracked top-level tree and producer/consumer dependencies: whether final delivery bypasses population eligibility; whether alternate protocols replace deterministic scoring; whether retained summaries reach a later operator after clipping; whether post-build quality judgment actually vetoes task use; whether invalid RL rows become uniformly trusted parents; and whether shipped guidance or training launchers add materially different paths. The first five have the supported dispositions above, including the reports' deliberately nonuniform consumer rules.

Two additional shipped paths warrant local inventory limits below. Source inspection found optional task-skill injection in SRC-1, OpenMLE-Evo/scripts/evaluate_airaevo.py and OpenMLE-Evo/tts_search/task_skill_guidance.py. It also found that the inspected SFT launcher names train_async.py, whose source retains actor training and periodic rollout weight updates. These additions do not overturn the positive memory or training findings. They do prevent treating the reports as complete guidance or training-scheduler inventories.

I stopped optional expansion because these selected challenges now have supported dispositions or explicit local limits, and no identified material contradiction remains. Other available database copies, metadata tools, providers and deployment branches need not be exhaustively inspected to preserve this bounded account. Their uninspected status cannot support absence, equivalence, security, historical reproduction or whole-release completeness claims. External weights, corpora, task releases and services still prevent the observed-operation and causal conclusions already withheld by the reports.

Blockers

none

Limits

  • The guidance inventory around RT-BAP-operator-guidance and MEM-RTE-targeted-memory is partial. Besides use-derived experience, the shipped MLE-Bench adapter optionally injects task-name-selected Markdown skills into evaluation prompts. SRC-1, OpenMLE-Evo/scripts/evaluate_airaevo.py and OpenMLE-Evo/tts_search/task_skill_guidance.py. This does not contradict the reports' explicit skill-loading uncertainty or establish that every skill is loaded. Withhold exhaustive comparisons of static guidance selection, loading and budget; retain the supported journal-derived read-back account.

    patched = f"{public_user_prompt.rstrip()}\n\n{block}\n" --- OpenMLE-Evo/tts_search/task_skill_guidance.py

  • The training-scheduler inventory in RT-RTE-sft-training and RT-CMP-training-models is coarse. SRC-1, OpenMLE-ERL/SFT/slime_scripts/common/run_slime_sft.sh launches train_async.py; OpenMLE-ERL/SFT/slime/train_async.py overlaps generation and training and conditions rollout weight synchronization on update_weights_interval. The positive actor-update/checkpoint/rollout-update pathway remains supported, but the cited synchronous loop is not a complete account of the launcher's timing. Withhold exact synchronization, staleness and scheduler-equivalence comparisons, not the implemented numerical adaptation finding.

    if (rollout_id + 1) % args.update_weights_interval == 0: --- OpenMLE-ERL/SFT/slime/train_async.py