OpenRSI report verification

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

Verification

The five reports support publication of a bounded implementation account, with the local limits below. I read every supplied declaration, annotation, source attribution, status, containment relation and reconciliation disposition. The correction-answer files are empty; the supplied structural report has no findings. No correction answer or analyst validation was treated as independent semantic evidence. Deterministic citation and quotation checks belong to the validator; this judgment concerns what the passages support.

The checkout HEAD equals the boundary's frozen revision. Its top-level tracked areas agree with the declared artifact. Additional inspection stayed within SRC-1 and supplied implementation evidence, not observed operation. The improvement-plane classification and complete-artifact, partial-loop boundary remain supported. Shipped drivers and consumers were included rather than reclassified as external callers. External weights, corpora, deployment and RL SLIME still prevent an exercised training-to-deployment conclusion.

Runtime and delivery

I independently traced Gym's preparation admission and retry context through SRC-1, OpenMLE-Gym/builder_core/utils/nodes.py, OpenMLE-Gym/openmle_gym/build.py and OpenMLE-Gym/openmle_gym/process_runner.py. RT-RTE-gym-build correctly separates workflow launch, compile/write, execution and expected-file checks. The isolated branch requests container restrictions; the alternate branch directly loads preparation code. The coordinator remains a process worker. The report does not promote these options to blanket deployed confinement. Metric generation compiles/writes rather than running a semantic quality test. The two-message retry tail is a real consumer, not merely retained history.

For RT-RTE-evo-generation, I followed score annotation, both step-method fitness emissions and parser branches in SRC-1, OpenMLE-Evo/third_party/aira-evo/examples/mle_bench/base_task.py and OpenMLE-Evo/third_party/aira-evo/src/dojo/solvers/evo/evo.py. Analyzer fallback, experience-dependent analysis, optional printed-score trust and downstream test-score substitution support the report's conditional-oracle account. A finite supplied fitness does not remove exit-code or validity checks. Conversely, inspection of the deterministic branch alone would not justify evaluator-only scoring; the reports retain the alternatives.

The same solver's async commit, worker retry and shutdown paths support RT-RTE-evo-async, without proving race freedom, exact in-flight budgets or schedule equivalence. NatureBench's executor dispatch and preflight/evaluate/annotation chain in SRC-1, OpenMLE-Evo/third_party/aira-evo/examples/nature_bench/base_task.py support RT-RTE-naturebench-execution. Local execution is a supported alternative to Docker/SCM, and the service-backed score remains an external oracle contract.

I traced the final-submit producer, Boolean consumer, exclusive loop bounds, adapter entry points and final candidate fallback in SRC-1, OpenMLE-Evo/third_party/aira-evo/examples/mle_bench/single_task_runner.py. RT-RTE-final-submit and its workflow annotation are supported: false suppresses this runner's final test calls, not search evaluation or code export. NatureBench supplies a reachable configurable false case. MLE's zero-to-one repeat normalization prevents using the helper alone as evidence that zero disables MLE submission. Direct adapter capability is not an established supported bypass of the runner's decision. Unscored random fallback and empty export justify withholding successful-verification implications from filenames.

WF-RTE-debug-cycle matches the solver's proposal-before-try ordering, execution/parser exception boundary, successful final-node admission and post-failure time check. The workflow's stop guards, checkpoint recovery, reselection, export and finally cleanup match the shipped runner. It neither invents transactional rollback nor joins separately launched training and construction paths into one automatic workflow.

Retained state, epistemic force and training

The model components RT-CMP-openai-llm-provider, RT-CMP-generic-llm, RT-CMP-sft-model and RT-CMP-rl-model distinguish configured inference clients from intended parameter-changing training. Provider construction, GenericLLM's template/direct-message branches, SFT rollout and RL generation hooks support the stated boundaries. Mutable endpoint identity is not treated as exact checkpoint provenance. The execution, training-input and experience-guidance consumers in RT-BAP-candidate-execution, RT-BAP-training-parameters and MEM-BAP-experience-guidance have different channels and force; none establishes observed compliance or benefit.

I checked the journal export/reconstruction pair, card/board derivation, runner persistence/reattachment, summary normalization/cache alternatives, targeted selectors and improve consumer. The relevant SRC-1 paths are 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 solver and runner above, and OpenMLE-Evo/third_party/aira-evo/src/dojo/core/solvers/operators/improve.py and OpenMLE-Evo/third_party/aira-evo/src/dojo/core/solvers/operators/rich_memory_summary.py. MEM-OBJ-journal, MEM-OBJ-experience-card, MEM-OBJ-strategy-board, MEM-OBJ-rich-summary, MEM-RTE-experience-cycle and MEM-RTE-operator-context preserve raw versus derived content and actual later selection. Persisting a board file is not confused with reading that file into prompts.

The epistemic derivation, summary-production, retention and consumption records preserve these distinctions. Arithmetic is warranted only under its input/category definitions; a heuristic family is not an independently established scientific category. Summary shape admission does not check explanatory truth. The 420-character rendering can remove a qualification. Thus EPI-RTE-experience-derive, EPI-RTE-summary-produce, EPI-RTE-summary-retain and EPI-RTE-summary-consume, and their object records, support conditional derivation and advisory reuse, not accepted ampliative explanation or faithful compression of every output.

I independently read metric loading/evaluation, quality normalization, evaluator branching and the failure/report consumers in SRC-1, OpenMLE-Gym/openmle_gym/metric_validation.py and OpenMLE-Gym/openmle_gym/local_evaluator.py. EPI-OBJ-metric-result, EPI-OBJ-quality-scores, EPI-RTE-metric-check, EPI-RTE-quality-check and EPI-RTE-quality-recommendation correctly distinguish finite metric execution, model opinion, reporting thresholds and CLI failure. Skipping model evaluation and deterministic validation failure are separate branches. A low recommendation alone is not the failure predicate. These findings do not assert a universal training or release veto.

For RT-RTE-sft-training, MEM-OBJ-sft-programs, MEM-OBJ-sft-messages and MEM-RTE-sft-reuse, I checked the producer filter sequence, scheduler insertion condition, greedy parent consumer, database pruning and vendored rollout. SRC-1 paths include OpenMLE-ERL/SFT/tts_search/data_produce/pipeline.py, OpenMLE-ERL/SFT/tts_search/services/scheduler.py, OpenMLE-ERL/SFT/tts_search/greedy.py, OpenMLE-ERL/SFT/tts_search/program_database.py, OpenMLE-ERL/SFT/slime/slime/rollout/sft_rollout.py and OpenMLE-ERL/SFT/slime_scripts/common/run_slime_sft.sh. Search retention is not conditional on rejection-policy acceptance; launcher dataset admission does not attest upstream semantic filters. Messages become tokens/masks with zero SFT reward. The reports preserve these separate admission decisions.

For RT-RTE-rl-training, MEM-OBJ-rl-programs and MEM-RTE-rl-reuse, I followed categorization before sandbox execution, invalid-category insertion, surviving-row summary updates, selector-class dispatch, prompt delivery and fitness pruning in SRC-1, OpenMLE-ERL/RL/generate_mle.py, OpenMLE-ERL/RL/program_database.py and OpenMLE-ERL/RL/prompt_builder.py. Invalid retention is not successful-program endorsement. The evo and airaevo classes are different, and evaluation's draft branch avoids training-time parent-memory selection. Async launch hooks and OpenMLE-ERL/RL/train_async_with_patch.py also support the distinction between hard trainer-path checks and nonfatal patch failures. External optimizer correctness is not inferred from local hooks.

The three claim records were compared with SRC-1, README.md and OpenMLE-ERL/RL/README.md: RT-CLM-meta-evolution, RT-CLM-training-validation and EPI-CLM-model-harness. Bounded design intent, reported operation and bundled model–harness contrasts remain distinct. No inspected run or independently varied component effect supports upgrading their evidence strength. All route implementation/operation status pairs remain defensible; none claims observed activation. No evidenced-absence declaration or positive theory-builder verdict requires additional warrant.

Relations, omission challenges and stopping decision

Every Part of: relation was assessed semantically: memory objects and subroutes, epistemic derivation/summary functions and workflow repair belong to the cited search or training container without replacing it. Shared use by async search does not require a second store. Reconciliation correctly supersedes only the duplicate rich-summary object. The combined epistemic card/board referent can remain alongside its separately declared parts because its derivation findings are not invalidated by that separation. The reconciliation disposes every memory integration issue without changing an owner's values. Recovery qualifications, additional Gym checks and workflow delivery qualifications do not contradict the narrower runtime claims. No unresolved conflict was found requiring an anchored conflict paragraph.

Omission questions were selected from the tracked areas, alternate executors and producer/consumer dependencies: could another scoring branch defeat evaluator-only interpretation; could export occur without final testing or scored verification; could rejected material later enter a store or prompt; could dataset launch bypass selection; and could static skill loading hide a separate experience-maintenance route? The first four have supported dispositions above and the submitted text already retains the countercases. For skills, I additionally traced SRC-1, OpenMLE-Evo/tts_search/task_skill_guidance.py and OpenMLE-Evo/scripts/evaluate_airaevo.py: filename-stem lookup and metadata-driven injection establish optional static guidance, not a historical synthesis cycle or demonstrated matching external task keys. MEM-OBJ-task-skills remains appropriately bounded. Scoped searches also located the separate Evo-local database implementation without establishing its live consumers; that inventory limit remains below.

Optional expansion stopped because these challenges have dispositions, no identified contradiction or unsupported emitted comparison remains, and the reports do not depend on complete inspection of remaining metadata, skill contents, scheduler variants, sandbox recovery or RL helper/lifecycle code. Their uninspected alternatives prevent complete inventories and system-wide negative conclusions; they do not negate the supported positive routes. Missing external assets and execution evidence are already faithfully scoped uncertainty, not defects to repair or grounds to erase architectural findings.

Blockers

none

Limits

• MEM-OBJ-journal and MEM-RTE-experience-cycle leave recovery fidelity limited: the direct exporter stores nested metric metadata, while reconstruction reads flattened keys. SRC-1, OpenMLE-Evo/third_party/aira-evo/src/dojo/core/solvers/utils/journal.py supports this discrepancy. Retained code, metric value and separate card/cache recovery remain supported, but readers must withhold complete evaluator-metadata or semantically equivalent restart conclusions. The runtime and reconciliation already avoid claiming those guarantees.

  metric_info = { k[len("metric_info/") :]: v for k, v in node_data.items() if k.startswith("metric_info/") } --- OpenMLE-Evo/third_party/aira-evo/src/dojo/core/solvers/utils/journal.py

• The inventories remain partial around the Evo-local database, static-skill contents and provenance, full SFT collection variants, and RL helper/cache/lifecycle consumers. These are local coverage limits of MEM-OBJ-task-skills, MEM-RTE-sft-reuse, MEM-RTE-rl-reuse and the epistemic coverage account, not evidence of missing system behavior. Positive selected-memory and training-input routes remain valid. Readers must withhold complete cross-implementation database equivalence, full maintenance/restart inventories and a system-complete negative about epistemic acceptance or criticism.