LHTB report verification

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

Verification

The reports support a bounded implementation account. No material blocker was found. I read all supplied members, their types, reconciliation, the empty correction-answer files and the structural report, which reports no findings. I independently inspected source rather than accepting the analysts' validation statements as support. SRC-1 HEAD equals the reviewed commit and the checkout is clean. The top-level tracked tree agrees with the boundary coverage. No target code, tests, examples or services were executed.

Runtime, context and authority

I traced the trial controller from agent invocation through interim decisions, final verification, artifact collection and finalization. The effective configuration and migration task support the stated agent, timeout and separate-verifier selection. The persisted result is an outcome, not a conversation checkpoint or correctness certificate. The job's config equality, completed-result loading, deletion of incomplete directories and remaining-config matching support the recovery findings. These checks uphold RT-RTE-trial-execution, RT-OBJ-trial-result and RT-RTE-job-recovery. Evidence: SRC-1, configs/examples/full_benchmark_2.0.yaml, tasks/langchain-version-migration/task.toml, harbor/src/harbor/trial/trial.py, harbor/src/harbor/job.py.

The model-response/parser/tmux chain supports automatic task-workspace effects without a per-command human gate. The 60-second field bound does not bound subprocess lifetime. Factory selection/import and the Codex adapter are genuine alternate execution paths, so Terminus-specific controls cannot be generalized to all agents. I uphold RT-RTE-terminal-actions, RT-RTE-agent-dispatch, RT-CMP-agent-factory, RT-OBJ-task-instruction and RT-BAP-task-guidance. Evidence: SRC-1, harbor/src/harbor/agents/terminus_2/terminus_2.py, harbor/src/harbor/agents/factory.py, harbor/src/harbor/agents/installed/codex.py.

I checked the continuation predicates, termination-reason branch, timeout handler, process-reward activation constraints, cleanup ordering and feedback reader. Restarted run() makes a new Chat; resume retains Chat/tmux. The live verifier reward controls stopping. The fixed same-conversation message is not universal binary feedback: restarted feedback can read diagnostics, although provider-dependent cleanup can remove their source before that read. Separate grading transfers selected paths, not an authenticated complete environment snapshot. Shared freezing and cleanup remain best effort. These dispositions uphold RT-RTE-verifier-continuation, RT-RTE-verifier-execution and RT-CLM-grader-isolation at their stated scopes. Evidence: SRC-1, harbor/src/harbor/trial/trial.py, harbor/src/harbor/verifier/verifier.py.

I separately traced the successful three-call handoff, recent-pair unwinding and short/ultimate fallback submission. Only the successful handoff explicitly installs the three-message frame. The memory annotation correctly prevents extending that replacement claim to all fallbacks. Question generation receives reduced inputs; summary and answer calls receive remaining history. Neither requests nor installation verify fidelity. The Chat response-chain reset does not establish remote deletion. This supports RT-RTE-context-handoff, MEM-OBJ-handoff-context, RT-CMP-terminus-model and the memory annotations. Evidence: SRC-1, harbor/src/harbor/agents/terminus_2/terminus_2.py, harbor/src/harbor/llms/chat.py, harbor/src/harbor/llms/lite_llm.py. Skill discovery injects metadata rather than complete bodies; subsequent shell access is afforded, not demonstrated consumption.

The patch recipe and replacement controller support an afforded installation path and executable future consumer, not observed adoption or exercised self-improvement. The replacement's binary default and refusal to grade when freezing fails differ from the bundled trial. Thus RT-RTE-runtime-patching and RT-BAP-runtime-module retain their narrower strength. Evidence: SRC-1, harbor/skills/apply-lhtb-patches/PATCH.md, harbor/patches/single_step.py.harbor-0.20.0, scripts/daytona_cleanup/run_daytona_eval.sh.

Retention and post-run interpretation

Trajectory writing, segmentation and subagent save handling support the distinction between payloads, display labels and references in MEM-OBJ-trajectory. Timed checkpoint copying, hidden JSONL writing, metadata insertion and final-checkpoint recording support MEM-OBJ-process-reward and MEM-RTE-checkpoint-retention. The controller consumes the live result; a persisted checkpoint reader is not established. Evidence: SRC-1, harbor/src/harbor/agents/terminus_2/terminus_2.py, harbor/src/harbor/trial/trial.py.

I traced analysis from model request through schema parsing, persistence, cache hits and aggregation inputs. The aggregate receives summaries and check explanations without file tools in that call; a cached file can bypass reanalysis without evidence-sensitive invalidation. This supports MEM-CMP-analysis-model, MEM-OBJ-analysis, MEM-RTE-trial-analysis, MEM-BAP-analysis-guidance, EPI-OBJ-analysis, EPI-RTE-trial-analysis, EPI-RTE-analysis-cache and EPI-RTE-job-aggregation. Structural admission is not truth acceptance. The task checker separately supplies advisory rubric assessments: EPI-RTE-task-check. Evidence: SRC-1, harbor/src/harbor/analyze/analyzer.py, harbor/src/harbor/analyze/backend.py, harbor/src/harbor/analyze/checker.py, harbor/src/harbor/analyze/prompts/analyze-rubric.toml.

Viewer reads, continuation traversal, episode selection and upload packaging support MEM-RTE-artifact-access. Export formatting does not establish deployed training or later solver import. The sweep selector exists but its expected singular reward field differs from ordinary production output; MEM-RTE-sweep-selection already withholds effective filtering. Evidence: SRC-1, harbor/src/harbor/viewer/server.py, harbor/src/harbor/utils/traces_utils.py, harbor/src/harbor/upload/uploader.py, harbor/src/harbor/cli/sweeps.py, harbor/src/harbor/models/verifier/result.py.

Task checks, epistemic licenses and workflow delivery

I traced the public signature/payload disjunction and the private candidate/reference comparison through dense conversion and reward parsing. The public-pass counterexample follows directly from the disjunction. The private acoustic reference is arithmetic over finite scenario inputs; neither tolerance agreement nor reward warrants physical-model recovery or generalization. The stated stage-one example yields 0.1667 by inspected arithmetic, not an executed experiment. These checks uphold EPI-OBJ-reconstruct, EPI-OBJ-check-report, EPI-RTE-public-feedback, EPI-RTE-hidden-semantics and EPI-RTE-dense-reward. Evidence: SRC-1, tasks-2.0/acoustic_beamforming_peak__01/environment/app/tools/feedback, tasks-2.0/acoustic_beamforming_peak__01/tests/private/judge.py, tasks-2.0/acoustic_beamforming_peak__01/tests/private/offcode20_oracle.py, tasks-2.0/acoustic_beamforming_peak__01/tests/private/dense_reward.py, tasks-2.0/acoustic_beamforming_peak__01/tests/test.sh.

The benchmark claim in EPI-CLM-benchmark-warrant is correctly treated as doctrine/reported operation, with its binary-default discrepancy retained rather than promoted to a guarantee. The epistemic ledger separates checking, scoring, disposition, retention and interpretation. Its indeterminate model-content relations do not infer ampliation merely from failure to prove entailment. Its bounded conclusion makes no system-complete absence claim about knowledge production. Evidence: SRC-1, README.md and the implementations above.

For WF-RTE-migration-grading, I checked required-path rejection, editable installation, source import location, stale replay removal, sequential failure handling, public/private fixture comparison, session follow-up and final reward. The workflow correctly distinguishes a reconstructed verifier environment from the solver's live installation, partial reward from pass, and persisted delivery from successful migration. Interim pass does not remove outer final verification. Evidence: SRC-1, tasks/langchain-version-migration/tests/test.sh, tasks/langchain-version-migration/tests/test_outputs.py, harbor/src/harbor/trial/trial.py.

Reconciliation and omission-search stopping

I checked every declaration's support and evidence strength, the annotations and each Part of: relation. Checkpoint retention belongs to the continuation controller; the private judge, dense conversion, check-report intermediate object and migration grading belong to generic verification without replacing it. Cross-kind containment is explained. No evidenced-absence record is declared, and scoped non-observation is not silently converted into absence.

The reconciliation has one supported supersession: EPI-CMP-analysis-model and MEM-CMP-analysis-model identify the same SDK backend component. The broader memory analysis route remains a valid container alongside finer epistemic functions. The analysis objects overlap without exact identity because the epistemic object also covers task checking. The fallback qualification and doctrine/implementation discrepancies are not inter-report conflicts. No unresolved disagreement requiring an Unresolved conflict: paragraph was found; all six memory integration issues have dispositions.

Omission questions were selected from the frozen tree and actual dispatch/producer-consumer edges: whether retained logs have model consumers beyond solver continuation; whether other grading channels change binary-feedback conclusions; whether saved rewards actually reach sweep selection; and whether viewer routes introduce an additional context consumer. The first two are supported by the analysis and public-feedback records. The sweep and viewer findings are bounded below. Additional viewer inspection found task-definition chat, not an automatic solver-memory or harness-update loop. Optional expansion stopped because no identified alternative overturns the central terminal/verification/context account, while exhaustive agent, task, export-format and remote-service inventories would exceed its emitted claims. This stopping decision does not establish absence in unsearched paths.

Blockers

none

Limits

  • The viewer coverage around MEM-RTE-artifact-access, MEM-RTE-trial-analysis and the epistemic route inventory omits task-definition chat. SRC-1, harbor/src/harbor/viewer/server.py dispatches user messages to harbor/src/harbor/viewer/chat.py, which injects task instruction/configuration, exposes Read/Glob/Grep, reuses a connected SDK session by task and supports explicit reset. Its session implementation says:

    if task_name in self._sessions and self._sessions[task_name].connected: return self._sessions[task_name] --- harbor/src/harbor/viewer/chat.py This is an additional operator-facing model/context route, not evidence of task-solver reuse or automatic adoption of its replies. The retained inventory is therefore incomplete for viewer conversational contexts and model components. The positive handoff, analysis and verification findings remain supported; readers should withhold an exhaustive inventory of all model-dependent context and advisory-consumption routes.

  • MEM-RTE-sweep-selection supports a wired selector, not effective success removal from ordinary bundled trial results. SRC-1, harbor/src/harbor/cli/sweeps.py reads verifier_result.reward; harbor/src/harbor/models/verifier/result.py declares rewards, and harbor/src/harbor/verifier/verifier.py produces that plural mapping. The report already expresses this compatibility uncertainty, so no unsupported operational claim needs repair. Withhold comparisons crediting this selector with successful outcome-driven pruning of current standard outputs; job recovery and live continuation have independently supported consumers.
  • MEM-OBJ-trajectory, MEM-OBJ-process-reward and MEM-RTE-artifact-access do not establish complete retention/delivery across every trajectory form. SRC-1, harbor/src/harbor/trial/trial.py copies only the primary trajectory for timed checkpoints, whereas harbor/src/harbor/utils/traces_utils.py separately traverses continuation files and selects exported episodes. The reports preserve these distinctions, but complete export normalization, all referenced subagent payloads and checkpoint-to-published-CSV derivation remain uninspected. Withhold comparisons requiring complete audit-history delivery or end-to-end published-data lineage; the narrower persistence, explicit export and model-analysis routes remain supported.