Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models
This work addresses the need for better benchmarks to assess MLLMs in real-world visual-textual search applications, though it is incremental as it builds on existing evaluation frameworks.
The authors tackled the problem of evaluating multimodal large language models (MLLMs) for visual and textual search in fact-finding tasks by constructing the Vision-DeepResearch benchmark (VDR-Bench) with 2,000 VQA instances, and they proposed a multi-round cropped-search workflow that improved model performance in realistic scenarios.
Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision-DeepResearch systems that use search engines for complex visual-textual fact-finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, existing benchmarks are not visual search-centric: answers that should require visual search are often leaked through cross-textual cues in the text questions or can be inferred from the prior world knowledge in current MLLMs. Second, overly idealized evaluation scenario: On the image-search side, the required information can often be obtained via near-exact matching against the full image, while the text-search side is overly direct and insufficiently challenging. To address these issues, we construct the Vision-DeepResearch benchmark (VDR-Bench) comprising 2,000 VQA instances. All questions are created via a careful, multi-stage curation pipeline and rigorous expert review, designed to assess the behavior of Vision-DeepResearch systems under realistic real-world conditions. Moreover, to address the insufficient visual retrieval capabilities of current MLLMs, we propose a simple multi-round cropped-search workflow. This strategy is shown to effectively improve model performance in realistic visual retrieval scenarios. Overall, our results provide practical guidance for the design of future multimodal deep-research systems. The code will be released in https://github.com/Osilly/Vision-DeepResearch.