Distributed intelligence

Core objective

This research direction investigates a paradigm where cognition and decision-making are not centralized within a single model, but are instead distributed across multiple autonomous agents that operate independently while communicating in natural language. In this setup, each agent relies on its own local knowledge, perspective, and computational resources, allowing global intelligence to emerge organically from their interactions. A guiding analogy for this system is a network of sensors where each sensor functions as a chatbot-like agent that can be queried directly in English.

Fundamental advantages

Studying distributed intelligence provides several key benefits over monolithic, centralized systems: * Energy efficiency: computation is distributed, meaning each agent performs only localized reasoning. * Explainability: the internal states, reasoning, and rationales of the agents are explicitly verbalized through natural language communication. * Modularity and hot-plugging: new agents can seamlessly join or leave the system on the fly. * Robustness: the failure of a single agent does not compromise the operational integrity of the entire system. * Scalability: the collective intelligence and capability of the system grows as the number of participating agents increases.