MemBubble: Benchmarking How Agent Memory Shapes LLM Response Diversity
Memory serves as the core component of LLM-based agents, enabling personalized responses via extracting and using user preferences from past interactions. However, its use may reduce response diversity, thereby narrowing the range of information available to users. Existing evaluations rely largely on synthetic interactions and cover few memory systems, leaving the effects of memory on response diversity insufficiently evaluated in real-world scenarios. We introduce MemBubble, a benchmark of 1000 instances built on preferences extracted from real user-assistant conversations. To measure the extent to which memory concentrates response distributions, we construct test queries in scenarios ...
| Authors | Jiajun Tan, Fei Sun, Xinyu Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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