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

Understand

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 agent-specific memories that evolve intrinsically with agent outputs.
• Specifically, our method maintains role-aligned memory that preserves specialized perspectives while focusing on task-relevant information.
• Our approach utilises a generic memory template applicable to new problems without the need to hand-craft specific memory prompts.