Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory

• URL: http://arxiv.org/abs/2508.08997v1
• Date: Tue, 12 Aug 2025 15:05:00 GMT
• Title: Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory
• Authors: Sizhe Yuen, Francisco Gomez Medina, Ting Su, Yali Du, Adam J. Sobey,
• Abstract summary: Multi-agent systems built on Large Language Models (LLMs) show exceptional promise for complex collaborative problem-solving.<n>Yet they face fundamental challenges stemming from context window limitations that impair memory consistency, role adherence, and procedural integrity.<n>This paper introduces Intrinsic Memory Agents, a novel framework that addresses these limitations through structured agent-specific memories.
• Score: 3.8482387279540555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent systems built on Large Language Models (LLMs) show exceptional promise for complex collaborative problem-solving, yet they face fundamental challenges stemming from context window limitations that impair memory consistency, role adherence, and procedural integrity. This paper introduces Intrinsic Memory Agents, a novel framework that addresses these limitations through structured agent-specific memories that evolve intrinsically with agent outputs. Specifically, our method maintains role-aligned memory templates that preserve specialized perspectives while focusing on task-relevant information. We benchmark our approach on the PDDL dataset, comparing its performance to existing state-of-the-art multi-agentic memory approaches and showing an improvement of 38.6\% with the highest token efficiency. An additional evaluation is performed on a complex data pipeline design task, we demonstrate that our approach produces higher quality designs when comparing 5 metrics: scalability, reliability, usability, cost-effectiveness and documentation with additional qualitative evidence of the improvements. Our findings suggest that addressing memory limitations through structured, intrinsic approaches can improve the capabilities of multi-agent LLM systems on structured planning tasks.

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