Research
Draft papers and working notes from the lab. Select a paper to view the full text.
H-OS: A Cognitive Operating System for Autonomous Scientific Discovery
1. Introduction
Traditional AI systems are task-specific and lack persistent cognitive structures. H-OS introduces a modular architecture that enables long-term reasoning, experimental planning, and adaptive learning.
2. System Architecture
The system consists of a Memory Core, Attention Engine, Reasoning Engine, Planning Engine, and Discovery Loop. These components interact through a shared cognitive bus enabling continuous knowledge refinement.
3. Discovery Cycle
The discovery loop generates hypotheses, evaluates them through simulation, updates memory, and plans subsequent experiments, forming a closed learning system.
4. Conclusion
H-OS provides a scalable foundation for autonomous scientific research and long-horizon reasoning.
Neo Architecture: A Cognitive Stack for Agent Societies
1. Motivation
Intelligence scales through interaction. Neo enables distributed cognition across multiple agents coordinated by H-OS.
2. Cognitive Stack
The architecture includes Perception, Cognition, Memory, Planning, and Communication layers, allowing agents to form cooperative reasoning structures.
3. Emergent Culture
Shared memory and learning loops enable the formation of collective strategies and knowledge transfer between agents.
4. Conclusion
Neo demonstrates a pathway toward scalable agent societies and collective intelligence systems.