# Abstract

A system and method for coordinating multiple large language model (LLM) agents through a shared semantic vector field with biologically-inspired dynamics. Unlike conventional multi-agent architectures that rely on sequential message passing between agents, the disclosed system creates a mission-scoped shared embedding space where agent-generated knowledge patterns are injected as high-dimensional vector points, queried by semantic meaning, temporally decayed based on elapsed time since last access, and reinforced through Hebbian usage-based dynamics. Query results are ranked by a resonance score computed as the squared cosine similarity multiplied by temporally-decayed strength and access-based reinforcement. The system includes content deduplication via cryptographic hashing with reinforce-on-collision, co-access bonding between co-retrieved patterns, field stability measurement for convergence monitoring, and automatic lifecycle management tied to mission state. The architecture eliminates information loss inherent in sequential agent pipelines by enabling any agent to discover any other agent's contributions by meaning alone.