M$^3$Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions
Language agents are increasingly deployed over accumulating multimodal information, yet existing benchmarks assume a human-human form with sparse visuals and straightforward content, evaluating neither reasoning over authentic multimodal file interaction nor the interpretation of concealed user information. We therefore introduce M$^3$Exam, a query-centric multimodal conversational memory benchmark built on realistic user-agent interaction, with multi-dimensional evaluation spanning cross-modal grounding and implicit information inference. Benchmarking MLLMs and memory systems reveals persistent gaps in cross-modal grounding, cross session reasoning, and the efficiency cost of accumulatin...
| Authors | Zhengjun Huang, Wenxuan Liu, Zhoujin Tian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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