MemDerive: A Diagnostic Benchmark for Agent Memory over Evolving Data
Long-running agents rely on memory of their past interactions for task state, such as what has been completed and which conditions still hold. Such state is usually a derived state. No record states it, and it must be computed by rules from multiple records of an evolving history. Existing memory benchmarks score only the final answer, with no reference for the intermediate states behind it. A wrong answer therefore cannot be traced to the state that was missed, and a state that silently became outdated goes undetected. We present MemDerive, a diagnostic benchmark for agent memory over evolving data. It specifies each scenario symbolically, derives exact gold answers with Datalog, records...
| Authors | Sen Gao, Yongan Li, Han Chen et al. |
|---|---|
| Primary category | cs.DB |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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