MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents

• URL: http://arxiv.org/abs/2508.21475v2
• Date: Fri, 26 Sep 2025 13:36:22 GMT
• Title: MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents
• Authors: Xijia Tao, Yihua Teng, Xinxing Su, Xinyu Fu, Jihao Wu, Chaofan Tao, Ziru Liu, Haoli Bai, Rui Liu, Lingpeng Kong,
• Abstract summary: We introduce MMSearch-Plus, a 311-task benchmark that enforces multimodal understanding.<n>We provide a model-agnostic agent framework with standard browsing tools and a set-of-mark (SoM) module.<n>SoM enables provenance-aware zoom-and-retrieve and improves robustness in multi-step reasoning.
• Score: 44.63565009665076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing multimodal browsing benchmarks often fail to require genuine multimodal reasoning, as many tasks can be solved with text-only heuristics without vision-in-the-loop verification. We introduce MMSearch-Plus, a 311-task benchmark that enforces multimodal understanding by requiring extraction and propagation of fine-grained visual cues through iterative image-text retrieval and cross-validation under retrieval noise. Our curation procedure seeds questions whose answers require extrapolating from spatial cues and temporal traces to out-of-image facts such as events, dates, and venues. Beyond the dataset, we provide a model-agnostic agent framework with standard browsing tools and a set-of-mark (SoM) module, which lets the agent place marks, crop subregions, and launch targeted image/text searches. SoM enables provenance-aware zoom-and-retrieve and improves robustness in multi-step reasoning. We evaluated closed- and open-source MLLMs in this framework. The strongest system achieves an end-to-end accuracy of 36.0%, and integrating SoM produces consistent gains in multiple settings, with improvements up to +3.9 points. From failure analysis, we observe recurring errors in locating relevant webpages and distinguishing between visually similar events. These results underscore the challenges of real-world multimodal search and establish MMSearch-Plus as a rigorous benchmark for advancing agentic MLLMs.

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