When Correct Memory Goes Wrong: Fuzzing Persistent Memory Use in LLM Agents
Persistent memory helps LLM agents carry information across long interactions, but correct memory can still be used incorrectly when queries change or memory states evolve. Existing work mainly studies memory content errors or evaluates fixed test cases, leaving memory-use failures hard to discover systematically. We formulate this issue as a fuzzing problem and categorize such failures into query-related and memory-state failures. We then develop U-Fuzz, which starts from memory checkpoints as test seeds, mutates queries or memory states under explicit mutation obligations, validates each mutant, and uses observed memory behavior to guide iterative testing while keeping failure labels ou...
| Authors | Yuqiao Meng, Luoxi Tang, Yingxue Zhang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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