SAGE report verification
Type: agentic-system-analyses/types/agentic-system-verification.md
Verification
The reports support a bounded implementation account. The previous training-reachability blocker is repaired. I independently checked the current wording and its dependent conclusions against SRC-1, rather than accepting the correction answer as evidence. The source HEAD matches the boundary, its working tree is clean, and its top-level tracked tree matches the declared coverage. No target execution, provider call or experiment was performed. Source reads below establish implementation; prompts and README establish doctrine/design, not observed operation or benefit.
Runtime and correction disposition
I checked all four component records, both runtime objects, six runtime routes, the function-rules claim and three behavioral-authority paths, including their evidence anchors, consumers, ordering and conclusion statuses. The actor, evaluation endpoint, retrieval encoders and SFT model distinguish parameter mutation, artifact pinning and endpoint mutability. Executable source, prompt guidance and numerical policy authority remain separate: RT-CMP-actor-rollout, RT-CMP-language-model, RT-CMP-retrieval-model, RT-CMP-sft-model, RT-BAP-function-execution, RT-BAP-function-guidance and RT-BAP-policy-weights.
The corrected ordinary invocation accurately traces AppWorld_SAGE from SRC-1 sage/run_sage_2nodes.sh into the exact AppWorld dispatch in sage/verl/trainer/ppo/ray_trainer.py. The alternative constructs RLHFDataset; SRC-1 sage/verl/utils/dataset/rl_dataset.py reads Parquet, whereas patches/appworld/train_scenario.txt is scenario text. SRC-1 sage/verl/trainer/main_ppo.py resolves and forwards configuration without repairing that value. Construction precedes worker initialization and fit. The entry framing, RT-RTE-paired-rollout and RT-RTE-policy-update now consistently state conditional internal capacities, not a working shipped launch. Correcting loader selection is necessary, not a demonstrated sufficient repair. The runtime correction answer is supported; the former blocker is discharged.
Direct inspection of SRC-1 sage/llm_agent/app_world/app_world_env.py and selected progression, context and reward methods in sage/llm_agent/appworld_generation.py supports per-pair dictionaries, non-failure extraction, name replacement, second-world installation, lexical reuse counting and success-masked shaping. Installation precedes prompt admission. An unsuccessful response can clear prompt text; an exception does not guarantee suppression. Neither branch establishes rollback. Function transfer does not preserve the first world's globals or conversation. These findings support RT-OBJ-skill-libraries and RT-RTE-skill-install, with wired implementation and uninspected operation.
I traced reward placement, advantage consumption and update invocation through SRC-1 sage/verl/trainer/main_ppo.py and sage/verl/trainer/ppo/ray_trainer.py, then read the update/generation wrappers in sage/verl/workers/fsdp_workers.py, the optimizer step in sage/verl/workers/actor/dp_actor.py and generation-entry synchronization in sage/verl/workers/sharding_manager/fsdp_vllm.py. These support the internal parameter-change/later-generation path, not successful learning or exercised self-improvement. Checkpoint save/load in the trainer preserves numerical and dataloader state, not interrupted AppWorld worlds. The current reachability qualification preserves these supported mechanisms without upgrading their evidence strength.
Memory, epistemic and workflow dispositions
I checked every memory declaration and annotation against its producer and consumer in SRC-1 patches/appworld/experiments/code/skill_library_agent/skill_library_agent.py, patches/appworld/experiments/code/skill_library_agent/agent.py, patches/appworld/experiments/code/skill_library_agent/skill_library_agent_rollout.py and patches/appworld/experiments/code/skill_library_agent/lite_llm_generator.py. The three access parts differ from executable payloads: MEM-OBJ-skill-embeddings, MEM-OBJ-query-list and MEM-OBJ-query-embeddings. Six ranked candidates, thresholded top-two query/ngram matches, default scenario-substring matching and first-name deduplication are supported. Query tensors are saved without the corresponding query-list writer; predefined loading is distinct from output persistence. These support same-process reuse, not automatic output-to-restart recovery or coherent global withdrawal.
Expert verification gates benchmark-successful trajectories and extracted definitions, not arbitrary future function inputs. The rollout subclass saves prior accepted functions before later tasks and resets its list per scenario. At exhausted retries the base controller records failure and breaks the remaining tasks of that scenario; the runtime's compact recovery phrase must not be read as continuing that failed scenario. The connected route and dataset selection remain supported: RT-RTE-expert-admission, MEM-OBJ-rollout-functions, RT-RTE-sft-admission and MEM-OBJ-expert-dataset. Direct reads of SRC-1 patches/appworld/extract_expert_dataset.py, the supervised encoding method in patches/LLaMA-Factory/src/llamafactory/data/processor/supervised.py and its SFT YAML confirm last-call extraction, no-code filtering, normalization, cutoff and target masking. The unused helper is not the exported dataset route. Background generation without a wait and incompatible temperature expectations are supported by SRC-1 patches/appworld/expert_dataset_generation_claude.sh; the incomplete shell in patches/LLaMA-Factory/run_sft.sh does not erase the intended YAML configuration or establish an executable unattended pipeline.
The adapter supports MEM-OBJ-llm-calls and MEM-RTE-response-cache as an optional memoized-response path, disabled by normal configuration. Direct reads of SRC-1 patches/appworld/experiments/code/skill_library_agent/api_predictor.py and patches/appworld/experiments/code/skill_library_agent/full_code_reflexion.py support MEM-RTE-api-context, EPI-OBJ-predicted-apis, EPI-RTE-api-prediction and EPI-RTE-code-retrial. Namespace filtering, capped predictions, supervisor additions, demonstration-API union and feedback-fed proposals do not establish relevance, formulated criticism or beneficial reflection.
I assessed each function and architectural status in the epistemic ledger, including the two claim comparisons. The source-slicing algorithm supports the deductions in EPI-RTE-function-extraction: omitted globals and starting-line rather than full-end spans defeat universal semantic preservation. Actual unspecified candidate semantics appropriately remain indeterminate. Benchmark arithmetic has a narrower license than test adequacy; substring reuse is not a call trace. Expert trajectory acceptance for training-data use does not imply acceptance of generalized function specifications. RT-CLM-function-rules and EPI-CLM-self-evolution remain design claims, not observed correctness or causal improvement. Internal adaptation remains implemented despite the selected launcher's reachability defect; reconciliation explicitly preserves this qualification.
The selected workflow is one connected normal-evaluation path. The runner, normal config, base controller, skill agent, prompt and logger support its handoffs and WF-RTE-failed-code-filter. Filtering occurs on a subsequent step, leaves model-visible assistant history intact and can miss the final failure. Supervisor signaling, loop termination, benchmark success, default extraction and processed-task counting are distinct. Previous-task label gating is correctly distinguished from current-task success. This account establishes wiring, not successful delivery.
Relations, reconciliation and omission stopping
All seven declared Part of: relations have supported containment: three evaluation access parts, expert function and dataset products inside their producing routes, the extraction subroute and the workflow filter. Cross-kind containment is explained rather than treated as identity. I checked reconciliation's comparisons of all declarations and the memory integration issues. Valid containers coexist with narrower records; shared passages do not make the broader memory API-context route identical to either epistemic subroute. No supersession is proposed, and no unresolved substantive disagreement or missing material part requiring a conflict was identified. The supplied report-check is none, so there is no structural failure to route. The supplied runtime correction answer was checked; the other answer inputs are empty. Reconciliation's contrary statement about answer availability has the local provenance limit below, not a source-behavior conflict.
Omission questions were selected from the frozen tracked patch tree, registered agents, actual trainer-manager import, launch dispatch and producer/consumer dependencies. I challenged whether the shipped launch reaches training, whether persisted outputs close restart reuse, whether alternate retrieval/cache/registered-agent paths change the memory and context account, and whether expert success warrants the exact downstream supervision. Each has a supported bounded disposition above. Additional backend/launcher details and training-validation paths remain uninspected; the records do not claim exhaustive behavior or validated performance across them. Upstream evaluator adequacy and actual libraries, weights and runs remain outside the supplied evidence. Those uncertainties are already faithfully scoped, not new defects. Further optional omission expansion stopped because no identified material challenge remains without disposition and the retained claims do not assert repository-complete negatives or successful operation.
Blockers
none
Limits
- Reconciliation incorrectly says no analyst correction answers were supplied. This invocation supplies one runtime
correctedanswer, independently checked here against the current invocation account, RT-RTE-paired-rollout and RT-RTE-policy-update. Withhold any inference from that sentence about correction-answer availability or correction provenance. The reconciliation's technical connections and explicit reachability qualification remain supported; the local provenance error does not undermine them.