Multimodal agents use web and image tools to answer questions beyond their internal knowledge. Some search tasks use images mainly to identify an initial entity, then rely on text retrieval. These tas
VisSearch-Bench evaluates multimodal agents on visually intensive search tasks that require integrating visual context with external information, showing that even the best system fully solves only 26.47% of tasks.
Multimodal agents use web and image tools to answer questions beyond their internal knowledge. Some search tasks use images mainly to identify an initial entity, then rely on text retrieval. These tasks offer limited insight into how agents connect information across images, objects, and search steps. We therefore focus on visual context management. This involves organizing and using visual information and linking it to other findings during search. Visually intensive tasks require agents to combine object appearance, local details, or spatial relations with external information. We introduce VisSearch-Bench, with 204 human-written tasks across 7 main domains and 25 subdomains. It covers…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
VisSearch-Bench: Evaluating Long Horizon Visual Search in Multimodal Agents was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 30 checks on 3 different days. Its best position was #11 of 30, reached on Nov 7, 09:28 UTC. It is #12 right now, holding steady at #12 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #12, holding steady at #12 over the last 6 checks.
#11 on Hugging Face · Daily papers, on Nov 7, 09:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (30 checks) since.