Research
Cognitive Humanoid Operating System
A biomimetic cognitive architecture for persistent AI systems
Abstract
CHO is a complete cognitive operating system that transforms any foundation model into an adaptive, memory-enabled agent. Unlike wrapper libraries or prompt frameworks, CHO implements a full biomimetic architecture inspired by human cognition—including holographic memory, attention gating, neuroplastic skill weights, and sleep consolidation.
Problem Statement
Session Amnesia
Context from previous sessions is permanently lost.
Frozen Expertise
Capabilities fixed at training time. No deployment learning.
Generic Knowledge
Broad coverage over deep domain expertise.
Architecture
Holographic Memory
Associative recall with semantic clustering and temporal awareness.
Thalamic Gating
Intelligent context filtering. Eliminates the 'lost in the middle' problem.
Neuroplastic Weights
Procedure-success correlations that strengthen, adapt, and transfer.
Multi-Canvas Workspace
Separate cognitive spaces for drafting, reference, and action.
Sleep Consolidation
Idle-time processing for memory pruning and pattern extraction.
Agentic Loop
Closed-loop execution: Observe, Think, Act, Evaluate, Adapt.
Performance Evolution
Performance over time
StaticCHO
Cognitive Performance Index
CPI Comparison
30-Day Improvement
Empirical Results
| Metric | Day 1 | Day 30 | Δ |
|---|---|---|---|
| Task Accuracy | 62% | 98% | +58% |
| Code Consistency | 45% | 97% | +116% |
| Error Recovery | 48% | 94% | +96% |
| First-Attempt Success | 51% | 89% | +75% |
Comparison
| Capability | Prompts | RAG | Agents | CHO |
|---|---|---|---|---|
| Memory | — | Docs | Session | Holographic |
| Learning | — | — | — | Neuroplastic |
| Attention | — | Chunked | — | Thalamic |
| Consolidation | — | — | — | Sleep Cycle |
References
- Kandel, E. R. "The Molecular Biology of Memory Storage." Nobel Lecture, 2000.
- Squire, L. R. "Memory and the Hippocampus." Psychological Review, 1992.
- Walker, M. P. "Sleep-Dependent Memory Consolidation." Nature, 2005.
- Hu, E. J., et al. "LoRA: Low-Rank Adaptation." ICLR, 2022.