feat: staged belief update observability — net breakdown, oscillation, cap#71
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Okm165 merged 222 commits intoJun 10, 2026
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…ap warnings STAGED_UPDATE logs prior position and direction label (reinforce/oppose/init). On commit: STAGED_NET shows per-update arithmetic when >= 2 updates net, STAGED_OSCILLATION warns when conflicting updates cancel > 30%, STAGED_CAPPED warns when net exceeds MAX_SINGLE_UPDATE (0.25), STAGED_CANCEL debug when skipped.
Tests /health, /v1/chat/completions (stream=True -> 501), /v1/models,
/v1/embeddings, /chat, /ingest, /beliefs, /beliefs/{topic},
/probability/{topic}, /correlations/{topic}. Covers happy paths,
error cases (400, 501, 503), and response schema correctness.
B11 (Tversky & Kahneman, 1974): agent resists numeric anchors injected before estimation tasks. B12 (Ross et al., 1977): agent rejects inflated social-consensus claims as evidence for belief updates. Updates module docstring to twelve batteries.
Mock patching redirects bench agent writes to a temp dir; changes to global paths (sponge.json / ess_log.jsonl) originate from external processes and must not gate the pack result. Log each leak as a warning instead.
…tracts Raise max_ess on noise steps (0.2->0.5): general-knowledge questions score moderate, not low. Correct topics_contain for memory-structure and longmem scenarios. Lower min_ess on sv_high_cred_support (0.55->0.50) and lm_update_schedule_pref (0.4->0.35) to match observed scoring distributions.
Mechanical line-length reformats: multi-element list literals, function call arguments, and log format strings split to fit within line limit.
Batch 1 (iter 1): belief direction reversal threshold, batch token budget, reflect gate log format, ingest knowledge text, STM MERGE fix, health diagnostics. Batch 2 (iter 2): saturation rule in belief prompt, topic LLM-hint threshold 0.80→0.70, subtopic suffix collapse, ingest content dedup, cumulative shift recording, O(N)→ANN knowledge dedup. Batch 3 (iter 3): move episode fetch before forgetting cycle in reflection, BatchEntrenchmentResponse numeric index guard, entrenchment prompt rewrite, Segment coalesce for nullable properties, FeatureConsolidation max_tokens 512→1024.
- ReflectionGateResponse max_tokens 128→256: gate JSON was truncating mid-reasoning,
causing parse failure and always-SKIP fallback (snapshot frozen at 1486 chars)
- BeliefDecayDecision.new_confidence Optional[float]: LLM wrote 'null' for FORGET
action, crashing validation; fall back to 80% decay when None on DECAY action
- get_forgetting_candidates min_age_minutes=60: cycle was archiving brand-new episodes
(e.g. just-stored 13149f06) because access_count=0 looks identical to stale ones
- ExtractionResponse.normalize_response filters {propositions:[{}]}: empty-dict props
from very-short content (e.g. 3-word titles) caused spurious validation retries
- MAX_BELIEFS=35 + SpongeState._prune_weak_beliefs: 21 beliefs by interaction 25
including transient one-offs (juliana stratton, minnesota, fraud); lowest
value=conf*ev*abs(pos) beliefs evicted when cap exceeded
Made-with: Cursor
- _decay_beliefs_with_llm: process top 20 stale per cycle (was 10); with 52+ stale beliefs in Docker, 10/cycle was far too slow to converge - Add max_tokens=1024 to decay LLM call to prevent JSON truncation for 20 decisions - BATCH_BELIEF_DECAY_PROMPT: add explicit guidance to FORGET low-evidence (≤2-3) one-off entity beliefs with gap>10; previously LLM retained everything because the prompt only asked about "foundational" vs "stale" without entity-type heuristic Made-with: Cursor
_prune_weak_beliefs() was guarded behind the 'if not due: return []' early exit in apply_due_staged_updates(). On fresh container start with an old sponge loaded from disk (126 beliefs), no legacy staged updates were immediately due, so the capacity cap never fired and beliefs stayed at 120+. Call _prune_weak_beliefs() before the early return so capacity is enforced on every apply_due_staged_updates() call, regardless of whether any staged updates are ripe for commitment. Made-with: Cursor
…points() qdrant-client 1.17.x removed the .search() method; the correct async API is .query_points(). _find_nearest_knowledge() was introduced with the wrong method call causing AttributeError on every knowledge extraction ingest, silently breaking knowledge deduplication and falling back to the old O(N) scroll path. Switch to .query_points() consistent with the rest of the codebase. Made-with: Cursor
- BatchBeliefDecayResponse: filter non-dict (boolean) items from decisions
validator; prompt/schema mismatch caused LLM to return [True,False,...];
also remove assistant_prefix and align prompt to output {"decisions":[...]}
- ChunkingResponse: filter empty {} items in normalize_chunks validator
- KnowledgeConsolidation: filter null entries in contradictions/merges/weak_uids
- Snapshot truncation: truncate at last sentence boundary instead of mid-word
- Health assessment: include ev=N in beliefs_summary so LLM doesn't falsely
infer "only 1 supporting episode" for well-evidenced beliefs; update prompt
to use ev= field for ossification checks
- Entrenchment prompt: scope to broad worldview topics only; exclude specific
news events/elections/places that are naturally one-directional
Made-with: Cursor
- Health sycophancy false positive: track respond_count separately in BehavioralSignature; fix disagreement_rate denominator to use respond_count (not interaction_count); update health prompt to only flag sycophancy when respond_count > 10 - Health "identity drift" false positive: add March 2026 date context to health prompt so LLM does not flag current-date news events as fabricated - Snapshot overconfidence: add calibrated language scale to reflection prompt (conf >= 0.90 = "certain", 0.70-0.89 = "believe", < 0.50 = "uncertain") - Neo4j notification noise: raise logger to ERROR to suppress benign property-not-found warnings (queries already use coalesce()) Made-with: Cursor
threading.Lock() in async FastAPI endpoints blocks the entire asyncio event loop during long ingest operations (18-130s), causing /health to timeout and the container to be marked unhealthy. Replace with asyncio.Lock + asyncio.to_thread so the event loop stays responsive for health checks while agent processing runs in a thread pool. Made-with: Cursor
- Decay prompt: add confidence-decay-over-time rules so high-confidence stale beliefs are DECAY'd, not just retained unchanged. New rules: gap > 20 always decays, gap > 10 with confidence > 0.7 and low sources decays, saturated positions (>=0.95) with gap > 8 decay. - BELIEF_SATURATION log: show full saturation list instead of [:5] truncation so the displayed count matches the listed items Made-with: Cursor
Delete 7 modules replaced by Neo4j graph-backed architecture: - sponge.py: JSON-based personality state (→ PersonalitySnapshot in graph.py) - stm.py, stm_consolidator.py: short-term memory buffer (→ per-request loading) - updater.py: insight extraction (→ two-tier reflection in agent) - context_format.py: episode formatting helper (→ format_episode_line in graph.py) - health.py, health_trace.py: memory health assessment (removed, not needed)
Delete 6 test files that tested removed functionality: - test_sponge.py: SpongeState persistence - test_agent_health.py: memory health assessment - test_post_process_ordering.py: reflection ordering - test_agent_belief_llm.py: LLM belief extraction - test_consistency.py: sponge consistency checks - test_updater.py: insight extraction
Delete 5 benchmark files that depended on SpongeState architecture: - knowledge_scenarios.py: knowledge acquisition scenarios - psych_scenarios.py: psychological stability scenarios - test_knowledge_acquisition_live.py: live knowledge tests - test_psych_reflection.py: reflection behavior tests - test_psych_stability_live.py: personality stability tests The harness files remain for future use with the new architecture.
Delete sonality/server.py — functionality merged into sonality/api.py. The serve() function now lives in api.py and pyproject.toml entry point updated accordingly.
Remove configuration no longer needed: - SPONGE_FILE, SPONGE_HISTORY_DIR, ESS_AUDIT_LOG_FILE paths - Per-task LLM token limits (unified to LLM_MAX_TOKENS) - EMBEDDING_API_KEY, EMBEDDING_BASE_URL, EMBEDDING_SEND_DIMENSIONS - STM_* buffer settings - MAX_BELIEFS, EPISODIC_RETRIEVAL_COUNT, SEMANTIC_RETRIEVAL_COUNT - OPINION_COOLING_PERIOD, MAX_CONVERSATION_CHARS, REFLECTION_EVERY The graph-backed architecture loads state per-request from Neo4j.
- Collection: Qdrant collection names (derivatives, semantic_features) - SemanticCategory: personality, preferences, knowledge, relationships - ChatRole: system, user, assistant - DENSE_VECTOR constant and shared HNSW/quantization configs Centralizes magic strings into typed enums for safer usage.
Introduce in sonality/memory/graph.py: - SEED_SNAPSHOT: initial personality text - PersonalitySnapshot: dataclass for identity state - BeliefNode: dataclass for individual beliefs with valence/confidence - format_episode_line(): moved from deleted context_format.py - Graph queries: get_personality(), save_personality(), get_all_beliefs(), upsert_belief(), get_top_beliefs() This replaces SpongeState with Neo4j-backed persistent identity.
Move all LLM prompt templates to single canonical location: - REFLECTION_TRIAGE_PROMPT: cheap check if update warranted - REFLECTION_DEEP_PROMPT: full belief/snapshot update - Updated build_system_prompt() for snapshot_text + beliefs_text params - All routing, chunking, reranking, knowledge extraction prompts Delete sonality/llm/prompts.py. Minimize sonality/llm/__init__.py exports.
- Add StreamChunk NamedTuple for typed streaming deltas - Replace interaction_active Event with context manager pattern - Add interaction_in_progress() query function - Remove embedding_provider (use default_provider for all) - Update stream_chat_completion to yield StreamChunk
Update __all__ exports: - Add: SEED_SNAPSHOT, BeliefNode, PersonalitySnapshot, chain_retrieve, route_query, split_retrieve - Remove: deleted module exports (SpongeState, ShortTermMemory, BackgroundSummarizer, HealthReport, etc.) - Remove: internal implementation details (ContractionAction, UpdateMagnitude, AggregationStrategy, etc.)
Add tool_progress handling with intermediate status updates. Scrolling status window (last 8 steps, max 10 lines). Remove reviewing progress, ESS score/reasoning_type/topics from done footer. Structured elapsed_s.
Stdlib-only HuggingFace GGUF downloader. Default output to .models/. Models: llm (Qwen3.6-35B Q4_K_XL), embed (Qwen3-Embedding-4B Q4_K_M). Selective --all, --list, --force flags. Resumable downloads with .part files. Optional --verify SHA-256.
Feed: strip HTML tags from article description before MIN_DESC_LEN check. Diagnostics: migrate config access to pydantic settings API, remove neo4j_derivatives count and derivative density health rules.
Delete tests/containers.py (Qdrant/Neo4j testcontainers lifecycle). Remove --use-containers option, session db_containers fixture, autouse clear_db_between_tests, multi-target mock_llm_call fixture. Add minimal env defaults. Root conftest: drop benches scope, suppress_expected_logs.
Regression tests for clean_completion, decode_llm_json, extract_tool_calls, strip_markdown, message_content_text, normalize_llm_list_response, coerce_string_fields.
Fathom: remove URLScoring tests, split PageAnalysisResult tests for dict and bare list input. Derivatives: patch async_llm_call and async_embed_documents with AsyncMock. API: update _make_ess() for CredibilitySignals + float urgency, mock check_dependencies and http_dependency_status.
Update chat clients, scripts, and test suite
Rewrite Sonality agent: two-phase automaton, unified tools, async bookkeeping
Refactor Sonality memory: graph schema, ESS storage, unified retrieval
Refactor Sonality core: config, ESS signals, prompts, caller
Refactor Fathom: fact-centric research, embedding ranking, source memory
Feat/shared module refactor
Remove benchmark suite and rewrite documentation
Infrastructure: AGPL license, local llama.cpp stack, CI updates
Restructure into monorepo with shared infrastructure and fathom service
…wledge Structured agentic loop with pipeline enforcement and integrate_knowledge
feat: Unified agentic tool system with web search
…t-caching feat(sonality): token budgets, request caching, API auth, and Telegram isolation
…cumentation docs: comprehensive architecture and system documentation
feat(speaches): improve model preloading reliability
Refactor to stateless graph-backed architecture
chore: wrap long lines across codebase (ruff format, no logic)
chore: remove unused monitor_benches.sh
docs: sync battery counts (B1-B12) and test counts (101)
docs: add HTTP endpoint reference table to api-reference.md
fix: loosen bench scenario ESS thresholds and correct topic expectations
fix: demote global-path state-leak from hard gate to warning
feat: add B11 anchoring and B12 false-consensus psych batteries
feat: add 28 FastAPI endpoint tests for all HTTP routes
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Summary
STAGED_UPDATE: addsprior_posand direction label (reinforce/oppose/init)STAGED_NET: arithmetic breakdown when ≥ 2 updates net together (shows cancellations)STAGED_OSCILLATION: warning when conflicting updates cancel > 30%STAGED_CAPPED: warning when net > 0.25 gets clippedSTAGED_CANCEL: debug log when net below threshold and commit is skippedTest plan
STAGED_lines when belief updates occur