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

Source: arXiv - 2606.07402v1

Overview

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%.

Key Contributions

This paper presents research in the following areas:

• cs.CL

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CL.

Authors

• Zhengjun Huang
• Wenxuan Liu
• Zhoujin Tian
• Wei Chen
• Junle Chen
• Yuqian Wu
• Fangyuan Zhang
• Qintian Guo
• Xiaofang Zhou

Paper Information

• arXiv ID: 2606.07402v1
• Categories: cs.CL
• Published: June 5, 2026
• PDF: Download PDF