Papers with MemBench
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents (2025.findings-acl)
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| Challenge: | Recent studies have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments. |
| Approach: | They propose a dataset and benchmark to evaluate the memory capability of LLM-based agents from multiple aspects including their effectiveness, efficiency, and capacity. |
| Outcome: | The proposed benchmark incorporates factual memory and reflective memory as different levels, and proposes participation and observation as various interactive scenarios. |