arXiv:2606.24595v1 Announce Type: new Abstract: Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interaction forms. In practice, however, this memory is evaluated mostly through downstream behavior, such as later answers, personalization quality, or task success, which tests that understanding only indirectly and leaves the memory artifact itself largely unaudited. We argue that long-term memory should instead be evaluated as an auditable post-interaction artifact: after ordinary assistance, what struc
The rapid advancement and widespread deployment of LLM agents necessitate robust evaluation methods beyond just downstream task performance as their capabilities grow more complex.
This development proposes a critical method for auditing the internal memory states of AI agents, which is essential for developing reliable, trustworthy, and increasingly autonomous systems.
The focus of AI agent evaluation shifts from solely behavioral outcomes to include auditable internal memory artifacts, enabling direct inspection of how agents learn and retain user information.
- · AI researchers
- · LLM developers
- · Developers of AI agent platforms
- · Enterprises deploying AI agents
- · Developers relying solely on black-box evaluation
- · Less transparent AI memory systems
Improved understanding and debugging of AI agent long-term memory capabilities.
Accelerated development of more sophisticated and personalized AI agents that maintain consistent user understanding.
Enhanced trust and broader adoption of AI agents in critical applications due to increased interpretability and auditability.
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Read at arXiv cs.CL