Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation

TL;DR

This paper introduces a novel memory system for AI agents based on continuous field dynamics, enabling better context preservation and reasoning over long interactions, with significant empirical improvements on benchmark tasks.

Contribution

The paper proposes a field-theoretic memory model for AI that uses continuous dynamics to improve long-term context retention and multi-agent reasoning, a novel approach in the field.

Findings

116% improvement in multi-session reasoning F1 score

43.8% increase in temporal reasoning accuracy

Near-perfect collective intelligence in multi-agent experiments

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<0.01, d= 3.06), +43.8% on temporal reasoning (p<0.001, d= 9.21), and +27.8% retrieval recall on knowledge updates (p<0.001, d= 5.00). Multi-agent experiments show near-perfect collective intelligence (>99.8%) through field coupling. Code is available at github.com/rotalabs/rotalabs-fieldmem.