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%.

翻译：语言智能体正日益被部署在积累多模态信息的场景中，然而现有基准测试假设的是以稀疏视觉和直白内容为特征的人-人交互形式，既未评估基于真实多模态文件的交互推理能力，也未评估对隐式用户信息的解读能力。为此我们提出M$^3$Exam——一个基于真实用户-智能体交互、以查询为中心的多模态对话记忆基准测试，具备跨模态定位与隐式信息推理的多维度评估。通过对多模态大语言模型与记忆系统的基准测试，揭示了跨模态定位、跨会话推理以及累积多模态上下文带来的效率成本等持续性差距。我们进一步提出M$^3$Proctor——一种多模态记忆方法，该方法能检测查询模态偏差并仅在需要时消耗原始视觉资源，在将索引构建时间与检索令牌数削减超过70%的同时，将准确率提升13%。