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
