| --- |
| language: en |
| license: mit |
| tags: [humanoid, reasoning-optimization, cognitive-ai, decentralized, depth-control] |
| --- |
| |
| # Humanoid Distributed Reasoning Depth Optimizer (HDRDO) |
|
|
| HDRDO is a meta-cognitive optimization model |
| that dynamically adjusts reasoning depth |
| based on uncertainty, risk exposure, |
| and mission criticality. |
|
|
| The model prevents overthinking and under-reasoning |
| in decentralized humanoid systems. |
|
|
| ## Architecture |
|
|
| - Context Severity Encoder |
| - Risk & Uncertainty Analyzer |
| - Adaptive Depth Controller |
| - Resource-Constrained Optimization Layer |
| - Decision Efficiency Evaluator |
|
|
| ## Capabilities |
|
|
| - Optimize reasoning depth in real time |
| - Balance speed vs. accuracy |
| - Reduce unnecessary computation |
| - Increase decision robustness |
| - Adapt to mission-critical scenarios |
|
|
| ## Training Data |
|
|
| han-decentralized-cognitive-state-transition-dataset-v1 |
|
|
| ## Output |
|
|
| - Recommended reasoning depth level |
| - Efficiency gain estimate |
| - Decision latency prediction |
| - Robustness score |
