MiroShark report verification

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

Verification

The submitted members support a bounded implementation account. The previous report-context blocker is repaired. I read the boundary, all five current members, correction answers, structural check and previous verification completely. The structural check reports none; there is no structural failure to route. The runtime answer is an argument checked against source, not acceptance by itself. The other answer files are empty.

I independently rechecked checkout HEAD and the tracked top-level tree against the Source register. They agree with the frozen whole-system application boundary. All source inspection below is SRC-1 implementation or attributed doctrine/design evidence at that commit. No target code, tests, examples, providers or services were executed. No observed-run or causal-experiment support was supplied.

Runtime records and control boundaries

I assessed every runtime component, object, route, claim and behavioral-authority declaration against its evidence scope and actual consumer. Searches of model creation in backend/scripts/run_parallel_simulation.py, backend/app/utils/llm_client.py, the Claude Code adapter, embedding/reranker services and recommendation encoders support the five distinct runtime components. Configurable names and unpinned pretrained loading do not establish immutable deployment identities; inspected inference is not parameter training. External provider state remains uninspected. Persona prose and symbolic configuration fields have different consumers in RT-OBJ-agent-profiles and RT-OBJ-simulation-config.

Source searches of backend/app/services/simulation_manager.py and backend/app/services/simulation_runner.py connect selected graph entities, profile/config generation, retained files and shipped subprocess selection to RT-RTE-prepare-population. Driver inspection supports RT-RTE-synchronized-rounds: common context is constructed before concurrent platform work; timeout continuation does not imply rollback or complete action accounting. Process groups and working directories are not sandboxes. Direct interview/model calls and full environment-function manual dispatch in backend/wonderwall/social_agent/agent.py support the alternate paths in RT-RTE-interview-manual, not a universal selected-tool/iteration-cap guarantee.

For RT-RTE-agent-mcp, I compared the synchronized driver's registry/eligible-ID path with backend/app/services/agent_mcp_tools.py and backend/scripts/mcp_agent_bridge.py. The helper allowlist is not the driver's authorization policy. Pending results are popped for later activation, and subprocesses inherit the host environment. These facts support capacity and delivery, not actual remote effects. Director injection source supports RT-RTE-director-context and the interpreted force of RT-BAP-agent-context. Flask's guard in backend/app/__init__.py is a scoped HTTP policy; the CLI header helper does not establish authenticated parity. Dispatch in backend/mcp_server.py supports RT-RTE-storage-mcp, including missing-community auto-build and explicit report/trace lookup. RT-CLM-graph-memory remains an attributed optional-retention claim, not complete or observed retention.

Memory and epistemic records

I followed backend/scripts/round_memory.py through rendering, pending-count context, asynchronous individual compaction, synchronous ancient merging and resident-record deletion. MEM-OBJ-round-memory, MEM-RTE-round-history and EPI-RTE-round-compaction support lossy retained context without summary-faithfulness acceptance. Failed ancient merging can discard earlier narrative. Raw database survival does not restore the model window. Searches of backend/scripts/cross_platform_digest.py confirm agent/platform/tail selection and marker-tail truncation for MEM-OBJ-cross-platform-log. Comment selection in backend/lib/env_compact.py supports MEM-OBJ-agent-observation, but not the stronger semantic-equivalence claim retained as MEM-CLM-observation-equivalence.

Checkpoint loading, saving and injection in backend/scripts/belief_integration.py, and substring/numerical rules in backend/wonderwall/social_agent/belief_state.py, support MEM-OBJ-belief-state, MEM-RTE-belief-feedback and EPI-RTE-belief-revision. The negation witness follows the code. Restored stance/confidence/trust omits exposure hashes and is not full conversation restoration. Social adaptation is independently supported; truthful belief, actual model dependence and improvement are not inferred.

I inspected the ingestion progression and source passages in backend/app/storage/neo4j_storage.py, cleaning in ner_extractor.py, threshold admission in entity_resolver.py, bounded pair checking in contradiction_detector.py, and activity batching in backend/app/services/graph_memory_updater.py. These support MEM-OBJ-episode, MEM-OBJ-relation, MEM-RTE-graph-ingestion, and the epistemic extraction, resolution, retention, adjudication and invalidation records. Missing endpoint reconstruction is a valid code witness. Exact replay skipping does not deduplicate relation CREATE. Category labels and episode linkage preserve some lineage, not evidence-consuming truth acceptance.

SearchService filters and GraphTools normal/fallback formatting support MEM-RTE-graph-retrieval, EPI-RTE-graph-ranking and EPI-RTE-edge-invalidation. Current-edge exclusion is not universal withdrawal: node hits, historical reads, unfiltered fallback and derived copies differ. Profile formatting drops provenance and limits supplied text. Community sampling and separate delete/write sessions support MEM-OBJ-community, MEM-RTE-community-rebuild and their epistemic counterpart, rather than atomic/fresh replacement. Recorder inspection supports MEM-OBJ-reasoning-trace: emitted thought text and truncated observations are explicit inspection material, not private provider reasoning or automatic learning.

I assessed every epistemic inventory object, ledger function and source claim, including distinctions between acquisition and production, checking and disposition, and retention and accepted integration. Ask cleaning/cache and enrichment source support EPI-RTE-ask-briefing and EPI-RTE-web-research. Actual generated content is unavailable; semantic indeterminacy is justified without asserting absent conjecture. Interview, tool-result and director-event content are correctly distinguished from their delivering routes; entity summaries are not relation assertions or persona outputs.

Direct reads of resolution/outcome admission and searches of quality/cache logic in backend/app/api/simulation.py, followed by signal/Polymarket adapters and gallery filtering, support EPI-RTE-quality-diagnostics, EPI-RTE-prediction-resolution, EPI-RTE-outcome-annotation and EPI-RTE-signal-derivation. Activity health, agreement with a caller reference, annotation presence and adapter arithmetic have distinct licenses and force. The 80/10/10 witness is conditional arithmetic, not observed calibration or financial-risk evidence. The producer remains explicitly incompletely traced, so no unconditional probability claim follows.

Architecture and report-guidance passages support the attributed claims and mismatches in the claim comparison. Grounding, category filtering and universal reasoning-retention wording exceed what their implementation alone licenses. The scenario caveat does not validate stronger trading labels. Lexical contradiction hints remain advisory; formatting/deletion changes presentation or local availability, not truth warrant. No system-wide negative about evaluation, learning or knowledge production is warranted or emitted.

Correction and selected workflow

I freshly read the section, synthesis and orchestration bodies in SRC-1, backend/app/services/report_agent.py, and searched request, background-task and download handling in backend/app/api/report.py. These support WF-RTE-report-delivery: placeholders can survive completion, synthesis sees truncated returned text and cannot veto sections, and delivery means retained/retrievable files rather than the start response or factual acceptance.

The current RT-RTE-report-sections and its dependent account consistently distinguish conditional helper excerpts from actual parallel delivery. The caller passes an empty previous-sections list; the helper supplies a placeholder when that list is empty. The endpoints, delegated visibility, ordinary-invocation account and evidence limits no longer imply sibling-draft access. This agrees with workflow and the memory annotation. The previous blocker is therefore discharged, not moved into Limits. Normal minimum-tool/conflict handling and its force-finish bypass remain supported. A tool-count policy is not a truth oracle.

Relations, conflicts and omission disposition

I checked all declared Part of relations for substantive containment and all six supersessions for shared referents and preserved findings. Relation content, belief state, community material, compaction, belief revision and community rebuilding have the stated identity evidence. The RoundMemory container validly coexists with its narrative part. Broader ingestion versus finer epistemic functions, retrieval versus MCP dispatch, and outer workflow versus section composition are not interchangeable identities. Annotations retain lens-specific fields rather than replacing generic identities. No evidenced-absence record is declared. Wired/implemented findings do not become observed or beneficial through reconciliation.

The current disagreement disposition correctly removes the settled report-context conflict. Checkpoint restoration and background-versus-ancient compaction are compatible refinements. I found no remaining conflict requiring both positions to be carried as irreducible disagreement, nor an undeclared material part needed to preserve an emitted central conclusion. Source anchors and load-bearing passages were assessed for meaning; deterministic form and quotation checking remain code's responsibility.

Omission questions were selected from the tracked source tree, alternate entry paths and producer/consumer dependencies: whether manual/stdio/extensions defeat universal controls; whether withdrawal reaches actual model consumers; whether public labels affect downstream selection; whether helper context reaches the shipped caller; and whether market/media feedback is missing from the context inventory. The first four have supported scoped dispositions above. Complete direct reading of backend/scripts/market_media_bridge.py and searches of its synchronized caller confirm the additional feedback mechanism retained as the limit below. The tree also contains auxiliary publication/export/oracle services; their mere presence does not contradict these explicitly partial inventories or impose a system-complete negative. Further omission expansion stops because no identified material challenge remains without a disposition and the retained breadth does not assert exhaustive auxiliary coverage. Dedicated-script parity, provider internals and downstream retraction remain unestablished, not absent.

Blockers

none

Limits

• The memory inventory's market/media coverage gap, beside MEM-RTE-round-history, MEM-RTE-belief-feedback and RT-RTE-synchronized-rounds, leaves cross-market feedback comparisons incomplete. SRC-1, backend/scripts/market_media_bridge.py, maintains database-price snapshots and stance/action-derived sentiment for later prompt injection; backend/scripts/run_parallel_simulation.py wires update and consumption. This local omission does not overturn the supported positive routes or their explicitly partial inventory. Withhold exhaustive context-transformation, cross-platform aggregation and recovery-equivalence conclusions; round narratives, per-agent digests and belief labels are not the only feedback mechanisms. The supply passage is:

  return self.latest_sentiment.to_trading_prompt() --- backend/scripts/market_media_bridge.py