MemAudit: Auditing Long-Term Agent Memory via Hidden User-State Recovery
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 structured user state can be reconstructed from the memory the agent leaves behind? We instantiate this view in MEMPROBE, a benchmark in which a memor...
| Authors | Enze Ma, Yufan Zhou, Wei-Chieh Huang et al. |
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| Primary category | cs.CL |
| Published | 23 June 2026 |
| Last updated at source | 1 October 2026 |
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