# DMV-Bench: Diagnosing Long-Horizon Multimodal Agents' Visual Memory with Incidental Cue Injection Source: [DMV-Bench: Diagnosing Long-Horizon Multimodal Agents' Visual Memory with Incidental Cue Injection]() Collection index: [llms.txt]() Data updated: 2026-06-30T05:00:05.200Z Authors: Yujin Tang, Chenming Shang, Ruize Xu, Nikhil Singh Published: 2026-06-25T19:34:25.000Z ## 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 task in the pixels alone. Across a chain of autonomous shopping sessions, every visited product image carries a unique, pre-rendered incidental cue, and the agent is later asked to recall a particular cued product and navigate to its URL. Inspired by dual-coding theory, we propose DualMem, a memory architecture that maintains a visual and a verbal code in parallel. On DMV-Bench, DualMem outperforms a caption baseline and three recent multimodal agent-memory systems at every chain length J in {5, 10, 15, 50} on both Gemini 2.5 Flash and Qwen2.5-VL-7B, with the lead surviving controls for memory-bank size and encoding-position bias, and an asymmetric dual-coding regime in which vision carries the cue end-to-end while the verbal channel plays a smaller query-grounding role. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Task success rate on DMV-Bench J=5 1.0](): Task success rate; DMV-Bench J=5 1.0; Task Success Rate: 82.7. - [Task success rate on DMV-Bench J=10 1.0](): Task success rate; DMV-Bench J=10 1.0; Task Success Rate: 77.2. - [Task success rate on DMV-Bench J=15 1.0](): Task success rate; DMV-Bench J=15 1.0; Task Success Rate: 75.1. - [Task success rate on DMV-Bench MC J=50 1.0](): Task success rate; DMV-Bench MC J=50 1.0; Task Success Rate: 68.3. ## Original sources and implementation [Original paper]() [PDF]()