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

• URL: http://arxiv.org/abs/2606.07402v1
• Date: Fri, 05 Jun 2026 15:44:18 GMT
• Title: M$^3$Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions
• Abstract summary: M$3$Exam is a query-centric multimodal conversational memory benchmark built on realistic user-agent interaction.<n>We propose M$3$Proctor, a multimodal memory method that detects query modality bias and consumes raw visual sources only on demand.
• Score: 21.009839718901855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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