RESEARCH / AURORA
—TR-2025-28
Aurora
A reasoning-first language model built on the Adaptive Cognition Layer. Aurora integrates emotional memory encoding, multi-source verification, and predictive hallucination detection.
INTRODUCTION
Aurora represents Thynaptic's primary cognitive model implementation. Unlike standalone language models, Aurora operates within a structured cognitive pipeline that provides context awareness, memory integration, and safety validation at every inference step.
The model is designed to be mechanism-aware: it knows what it remembers, what it's uncertain about, and when to defer to external verification. This self-awareness is encoded through the ACL's confidence scoring system.
94%
Hallucination Detection
78%
Emotional Memory Accuracy
96.7%
Local Routing Success
94.83%
Cloud MMLU Accuracy
CORE CAPABILITIES
Emotional Memory Encoding
Aurora tracks not just what users work on, but how it felt. Memory entries include emotional snapshots with valence, tone, and intensity dimensions. This enables emotionally-coherent responses across sessions.
Hallucination Detection
The Safety Layer performs recall-based fact verification against workspace knowledge. 94% identification rate for hallucinated claims. Uncertain responses are flagged with confidence signals.
Hybrid Routing
HybridBridge selects between local and cloud models based on query complexity, availability, and performance requirements. Local-first for privacy, cloud fallback for capability.
Self-Aware Confidence
Aurora calculates confidence metrics (0.0-1.0) based on recall quality, context freshness, and intent signals. 0.73 confidence-accuracy correlation demonstrates reliable self-assessment.