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MemBubble: Benchmarking How Agent Memory Shapes LLM Response Diversity

Jiajun Tan, Fei Sun, Xinyu Ma, Shixuan Zhang, Du Su, Qi Cao · Oct 18, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 45% Moderate protocol signal Freshness: Hot Status: Ready
Pairwise Preference Automatic Metrics General
  • Memory serves as the core component of LLM-based agents, enabling personalized responses via extracting and using user preferences from past interactions.
  • We introduce MemBubble, a benchmark of 1000 instances built on preferences extracted from real user-assistant conversations.
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