• Computer Science > Computation and Language [Submitted on 31 Jan 2026] Title:Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation View PDF HTML (experimental)Abstract:We present a memory system for AI agents that treats stored information as continuous fields governed by partial differential equations rather than discrete entries in a database • The approach draws from classical field theory: memories diffuse through semantic space, decay thermodynamically based on importance, and interact through field coupling in multi-agent scenarios • We evaluate the system on two established long-context benchmarks: LoCoMo (ACL 2024) with 300-turn conversations across 35 sessions, and LongMemEval (ICLR 2025) testing multi-session reasoning over 500+ turns • On LongMemEval, the field-theoretic approach achieves significant improvements: +116% F1 on multi-session reasoning (p<0 • 8% on temporal reasoning (p<0 • 8% retrieval recall on knowledge updates (p<0

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  • Computer Science > Computation and Language [Submitted on 31 Jan 2026] Title:Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation View PDF HTML (experimental)Abstract:We present a memory system for AI agents that treats stored information as continuous fields governed by partial differential equations rather than discrete entries in a database. The approach draws from classical field theory: memories diffuse through semantic space, decay thermodynamically based on importance, and interact through field coupling in multi-agent scenarios. We evaluate the system on t

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