RESEARCH / SAFETY
—TR-2025-08
Safety & Evaluation
Frameworks for hallucination detection, fact verification, and confidence calibration. Ensures reliable operation across all cognitive systems.
INTRODUCTION
Safety in Thynaptic systems is not a post-hoc filter. The Safety Layer operates as component 6 of the ACL pipeline, validating responses before they reach the user. This architectural integration ensures all outputs pass through fact verification and hallucination detection.
94%
Hallucination Detection
89%
Fact Verification Accuracy
0.73
Confidence Calibration
SAFETY MECHANISMS
Hallucination Detection
Recall-based verification against workspace knowledge. Claims about user data, project state, or previous conversations are validated against RecallIndexEntry.
Identification Rate: 94%
Fact Verification
Workspace knowledge claims are checked against indexed content. Responses that cannot be verified are flagged with confidence signals.
Verification Accuracy: 89%
Confidence Scoring
Self-aware confidence metrics (0.0-1.0) based on recall quality, context freshness, and intent signals. Higher scores indicate stronger system certainty.
Accuracy Correlation: 0.73
Graceful Degradation
Component failures do not block the pipeline. Intent classifier failure defaults to question intent. Memory failures continue without context. Model errors trigger fallback routing.
Failover Frequency: 3.3%
KNOWN LIMITATIONS
- —Hallucination detection depends on RecallIndexEntry accuracy
- —Fact verification limited to indexed workspace knowledge
- —Confidence scoring may be miscalibrated for novel domains
- —Model stickiness may maintain suboptimal model for 3 turns