arXiv:2606.10694v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons. However, due to their finite context window, LLMs cannot retain all past interactions, making long-term memory management essential for storing, updating, and retrieving historical information beyond the context limit. Although recent memory systems attempt to address this issue by storing historical information externally, existing approaches suffer from three key limitations: flat text-based memory organizations fail to capture explicit relati
The proliferation of LLMs interacting over extended periods has highlighted the critical limitation of finite context windows, necessitating advanced memory management solutions.
Effective long-term memory management is crucial for the development of truly autonomous and capable AI agents that can maintain context and learn from continuous interactions.
The proposed reasoning-enhanced graph framework represents a significant step towards enabling LLMs to store, retrieve, and reason over vast amounts of historical data, moving beyond simple text-based memory.
- · AI platform developers
- · Enterprise AI implementers
- · Companies building agentic systems
- · LLMs without advanced memory integration
- · Systems relying on naive contextual windows
LLMs can maintain coherent, long-running conversations and tasks without losing context.
This improved memory leads to more sophisticated, adaptive, and personalized AI agents across various applications.
Advanced AI agents, equipped with robust long-term memory, could accelerate the automation of complex white-collar workflows, profoundly impacting knowledge industries.
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Read at arXiv cs.CL