achiepatricia/han-cognitive-graph-transition-network-v1

Humanoid Cognitive Graph Transition Network (HCGTN)

HCGTN is a graph-based neural architecture designed to model and predict cognitive state transitions within decentralized humanoid agents.

The model operates on structured cognitive graphs, where nodes represent reasoning states and edges represent probabilistic transitions.

Architecture

• Input Cognitive State Encoder
• Contextual Embedding Layer
• Graph Attention Transition Module
• Probabilistic State Predictor
• Confidence Calibration Head

Capabilities

• Predict next cognitive state
• Estimate transition probability
• Detect unstable reasoning loops
• Model uncertainty propagation
• Support consensus-aware transitions

Training Data

han-decentralized-cognitive-state-transition-dataset-v1

Output

• Predicted next_state
• Transition probability distribution
• Uncertainty score
• Stability index