DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents

Key points

• Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management and business operations.1
• DSV-Mem comprises expert-reviewed scenarios and 1,000 questions across five user-oriented categories (Current State, Past State, Derived State, Change History, and Conflict/Refusal).1
• We also introduce a generation harness that produces evaluation suites by decoupling state-transition synthesis from conversation filling.1
• Raw conversation/haystack length, OCR, and arithmetic are not the primary bottlenecks; 2) models often fail to verify user premises against prior state updates before answering; 3) increased reasoning effort and memory management methods yield limited gains, whereas state-aware designs prove more effective.1

Sources (1)

• [1]DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 10:30 AM

  To address these challenges, we introduce DSV-Mem, a benchmark for evaluating Dense Stateful Visual Memory.

  Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management and business operations.

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