arXiv:2607.01935v1 Announce Type: new Abstract: Long term memory lets LLM agents act as persistent assistants, but user facts change. A useful memory system must know what is true now, what used to be true, and what changed. We study \emph{ghost memory}, a state coordination failure in which old, current, and transition facts coexist in the memory bank, remain mixed during retrieval, and mislead the answer model. We argue that memory systems should be understood and optimized from three levels: bank maintenance, retrieval, and answer time resolution. We propose ATMA, a state aware overlay for
The proliferation of LLM agents as persistent assistants highlights immediate and critical challenges in long-term memory management and factual consistency.
Improving agent memory decouples state-aware failures, enabling more reliable, trustworthy, and autonomous AI systems for complex tasks.
This research introduces a structured approach to memory system optimization, moving beyond monolithic memory architectures to address factual inconsistencies more effectively.
- · AI agent developers
- · Enterprises adopting AI assistants
- · Users of complex AI applications
- · Legacy AI memory systems
- · AI applications prone to hallucination
- · Developers ignoring memory integrity
More robust and less error-prone AI agents will be deployed across various industries.
Increased trust in AI agents will accelerate their adoption and lead to greater automation of white-collar tasks.
The enhanced reliability of autonomous agents could fundamentally reshape organizational structures and decision-making processes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI