arXiv:2606.19847v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong reasoning and generation abilities, but their fixed context windows limit long-term information accumulation and reuse across multi-session interactions. Existing memory-augmented systems often construct memory in a coarse and unstable manner, relying on inefficient memory representations or unstable unconstrained updates. To address these challenges, we propose AtomMem, a long-term memory system designed for value-dense storage and stable memory evolution. AtomMem introduces a Fact Executor, which
The rapid advancement and scaling of LLMs are pushing the immediate need for more efficient and stable memory systems to overcome current context window limitations and enable persistent agentic behavior.
Efficient long-term memory is critical for LLM agents to evolve from stateless tools to truly autonomous entities capable of sustained, complex interactions and learning across sessions.
This research introduces a novel approach to memory management for LLMs, moving towards more stable and value-dense storage that could significantly enhance agent performance and reliability.
- · LLM developers
- · AI agent platforms
- · End-users of AI agents
- · AI infrastructure providers
- · Inefficient memory-augmented systems
- · Stateless LLM applications
Improved long-term memory for LLMs will enable more robust and capable AI agents.
Enhanced agentic capabilities will collapse more complex white-collar workflows, increasing productivity across various sectors.
The proliferation of highly capable AI agents could fundamentally reshape human-computer interaction and organizational structures.
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Read at arXiv cs.CL