DMV-Bench: Diagnosing Long-Horizon Multimodal Agents' Visual Memory with Incidental Cue Injection
arXiv:2606.27499v1 Announce Type: cross Abstract: Research on agent memory has matured rapidly, but almost entirely on the text side: few existing benchmarks ask, in an interactive environment, when an agent genuinely needs to remember what it saw rather than what it could write down. We introduce DMV-Bench (Code: https://github.com/yyyujintang/DMV-Bench), the first interactive benchmark for multimodal-agent visual memory. DMV-Bench is built on a controlled home-furnishing e-commerce catalogue of 1,000 product variants in which a text-leakage contract keeps the discriminative signal of each ta
The rapid advancement in AI agents demands more sophisticated benchmarking to assess complex capabilities like long-term memory, which current text-focused methods fail to address.
This benchmark addresses a critical gap in evaluating multimodal AI agents' visual memory, which is essential for developing truly autonomous and capable systems interacting with dynamic environments.
The introduction of DMV-Bench provides a standardized, interactive method to diagnose visual memory, shifting the focus beyond text-based memory and enabling more robust agent development and comparison.
- · AI researchers
- · Multimodal AI developers
- · E-commerce platforms
- · Robotics
- · AI models with poor visual retention
- · Benchmarks limited to text-based memory
DMV-Bench will accelerate research and development of AI agents with improved visual memory and long-horizon planning capabilities.
Enhanced visual memory in agents will lead to more reliable and effective AI applications in complex, interactive environments like manufacturing, logistics, and elder care.
The ability of agents to genuinely remember visual cues over long periods could enable new forms of human-AI collaboration where agents autonomously handle multi-step tasks requiring continuous contextual awareness.
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Read at arXiv cs.AI