M$^3$Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions

Abstract: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 accumulating multimodal context. We further propose M$^3$Proctor, a multimodal memory method that detects query modality bias and consumes raw visual sources only on demand, improving accuracy by 13% while cutting index-construction time and retrieved tokens by over 70%.

Computation and Language (cs.CL)

arXiv:2606.07402 [cs.CL]

(or arXiv:2606.07402v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.07402 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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