GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations
Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems that extract, retrieve, and apply information across long-running conversations. However, both existing memory systems and benchmarks are built around the dyadic, single-user setup, even though real deployments routinely span groups and channels with multiple users interacting with the agent and with each other. This mismatch leaves three properties of group memory unmeasured: (i) group dynamics that go beyond concatenated one-on-one chats, (ii) speaker-grounded belief tracking, where the per-user memory modeling is needed, and (iii) audien...
| Authors | Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang et al. |
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| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 16 May 2026 |
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