Adaptive Cognition Layer

The ACL structures cognitive processing for human-AI collaboration. It orchestrates ten sequential components that transform user input into contextualized responses through intent classification, memory retrieval, reasoning routing, safety validation, and model selection.

INTRODUCTION

Aurora's Adaptive Cognitive Layer (ACL) serves as the primary inference path for cognitive operations. We implement the layer to maintain consistency across all interaction modes while enabling specialized routing for complex reasoning tasks, safety-critical responses, and domain-specific knowledge retrieval.

The ACL processes requests through ten sequential components. Each component transforms the input incrementally, building context from workspace state, retrieving relevant memories, assessing reasoning requirements, validating safety constraints, and selecting optimal models for inference.

Pipeline Components

96.7%

Local Routing Success

94%

Hallucination Detection

0.73

Confidence Correlation

ARCHITECTURAL FOUNDATIONS

The ACL implements a mechanism-first approach to cognitive processing. Rather than relying on end-to-end model inference alone, we structure the processing into discrete stages with measurable behavior at each transition.

PIPELINE STAGES

01Intent Classifier — Categorizes input type and routing signals
02Context Engine — Builds workspace context via CPS ranking
03Workspace Memory — Retrieves RecallIndexEntry with emotional snapshots
04Long-term Memory — Accesses Memory Graph and cross-conversation patterns
05Reasoning Layer — Routes complex queries to Python brain modules
06Safety Layer — Validates for hallucinations and factual accuracy
07Personality + Style — Adapts tone via StyleAnalyzer and ARTE
08Response Construction — Assembles full prompt with all context
09Confidence Scoring — Calculates self-aware metrics (0.0-1.0)
10HybridBridge Routing — Selects optimal model (local/cloud/hybrid)

MEMORY SYSTEMS

Workspace Memory (RecallIndexEntry)

Tracks all workspace objects with emotional snapshots. We encode not just WHAT users worked on, but HOW it felt — valence, tone, and intensity dimensions.

Hit Rate: 78%

Emotional Dims: 3

Long-term Memory (Memory Graph)

Semantic clustering via DBSCAN for emergent theme discovery. Cross-conversation pattern detection maintains narrative coherence across sessions.

Cluster Accuracy: 82%

Compression Threshold: 40 msgs