v0.9.0 — Neuroscience-Grounded Cognitive Architecture

What's New

16 cognitive improvements derived from three expert analyses (computational neuroscience, CS theory, information geometry / simulation theory).

Retrieval

• Query-conditioned BFS — spreading activation now weights edges by query relevance (Synapse paper, arXiv:2601.02744)
• GNN neighborhood aggregation — retrieval scores incorporate graph neighbor context, not just isolated node similarity
• Multi-anchor retrieval — Thousand Brains-inspired consensus voting across multiple query formulations
• Adaptive clustering — thresholds auto-adjust based on local embedding density (information geometry)

Consolidation

• Two-phase dream — dreamPhaseA() (NREM: compress) + dreamPhaseB() (REM: integrate) — modeled on biological sleep stages
• Cluster evidence preservation — observation content is now retained during clustering, fixing the highest information-loss point in the pipeline
• Temporal replay ordering — observations processed in creation order, matching biological consolidation sequence
• Abstraction provenance — exemplifies edges link abstractions back to source memories
• Schema congruence scoring — sparse-neighborhood observations held as episodic rather than prematurely clustered
• Post-refinement edge re-validation — edges are checked for continued validity after memory definitions change
• FSRS interval filter — only reviews memories actually due, reducing Phase 5 from O(n) to O(scheduled)

New Cognitive Features

• goal_set tool — store desired future states that generate forward prediction error (VTA/dopamine value channel)
• Epistemic foraging — wander is now information-gain-weighted, biasing toward under-explored, uncertain, and goal-adjacent memories
• Fiedler value metric — algebraic connectivity of the memory graph, measuring knowledge integration quality
• PE saturation detection — monitors prediction error trends to prevent identity schema ossification

Infrastructure

• getRecentMemories(days, limit) — time-bounded store queries for O(1) scaling (SQLite + Firestore + ScopedStore)
• First test file — 8 unit tests for retrieval functions

Stats

• 1,275 lines added across 15 files
• 3 new files: graph-metrics.ts, goal.ts, memory.test.ts
• 27 MCP tools (was 26)
• Zero breaking changes — all existing APIs preserved

Full npm: npm install cortex-engine@0.9.0