Synapse-Graph — Status & Gaps
Making AI "black boxes" traceable — current state and open challenges
🎯
Our Mission: Transform opaque AI models into traceable, governable systems. By discovering causal attention circuits, tagging defective components in OpenMetadata, and providing differential traces, we enable verifiable behavior change in production AI.
✅
8/10
Core Features Done
Core pipeline working — significant gaps remain for production-grade interpretability
✅ Completed Features
✓
Webhook secret validation with secure hmac comparison✓
OpenMetadata tagging helper with retry/backoff✓
Unit + integration tests for quarantine flow✓
Frontend: synapse graph & differential overlays✓
Causal discovery UI with attention visualization✓
GitHub Actions CI: pytest + TypeScript checks✓
HeadMaskStore runtime state management
⚠️ Open Challenges
→
Head ↔ Behavior Causality Not Proven (HIGH RISK)→
No Ground-Truth Validation (HIGH RISK)→
Scalability Gap: Head Explosion O(n²) (HIGH RISK)→
Token-Level vs Layer-Level Tracing (MEDIUM)→
Latency Overhead in Production (MEDIUM)
⚠️ Key Research Gaps
Real product gaps that prevent production-grade interpretability tooling
🔬
Causality Not Proven
Ablating heads shows output changes, but proving causation vs correlation is unsolved. Need counterfactual validation.
HIGH
🎯
No Ground-Truth
Without hallucination datasets, can't measure false positives. Ranking heuristics may find noise.
HIGH
📊
Head Explosion
GPT-4 class models need O(n²) pair ablation. Current approach doesn't scale to large models.
HIGH