Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models
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.
69817bb2ce18b18628096119
Yu Zeng
claimed_verified
2026-02-03T10:06:45.575Z
665d652e0f35c005de892108
https://cdn-avatars.huggingface.co/v1/production/uploads/665d652e0f35c005de892108/OGLbgZekX-3XTBkwS8k86.jpeg
Yu Zeng
YuZeng260
user
YuZeng260
69817bb2ce18b1862809611a
Wenxuan Huang
claimed_verified
2026-02-03T13:59:32.269Z
67dc162ec8c00778e8689f42
https://cdn-avatars.huggingface.co/v1/production/uploads/67dc162ec8c00778e8689f42/_y_tO6W3ONOkOWbumAFXA.png
Wenxuan Huang
Osilly
user
Osilly
69817bb2ce18b1862809611b
Zhen Fang
claimed_verified
2026-02-10T09:30:27.366Z
64b0a5037a475fba70a7260d
https://cdn-avatars.huggingface.co/v1/production/uploads/64b0a5037a475fba70a7260d/MauBbb6raMA23yrR1Zq21.jpeg
Zhen Fang
CostaliyA
user
CostaliyA
69817bb2ce18b1862809611c
Shuang Chen
69817bb2ce18b1862809611d
Yufan Shen
69817bb2ce18b1862809611e
Yishuo Cai
69817bb2ce18b1862809611f
Xiaoman Wang
69817bb2ce18b18628096120
Zhenfei Yin
69817bb2ce18b18628096121
Lin Chen
claimed_verified
2026-02-03T13:59:35.014Z
64b02ec0e5000ae8a572ced5
https://cdn-avatars.huggingface.co/v1/production/uploads/64b02ec0e5000ae8a572ced5/6ifLntBU2ICQK7SW8WxKU.png
Lin Chen
Lin-Chen
user
Lin-Chen
69817bb2ce18b18628096122
Zehui Chen
69817bb2ce18b18628096123
Shiting Huang
claimed_verified
2026-02-03T13:59:20.349Z
66ae3fbf491b555fef3bac0c
/avatars/47353470d46097ce108d32792dbbf2a2.svg
Shiting Huang
chocckaka
user
chocckaka
69817bb2ce18b18628096124
Yiming Zhao
claimed_verified
2026-02-03T10:06:43.236Z
665ec9ef60c9027be03b4a18
https://cdn-avatars.huggingface.co/v1/production/uploads/665ec9ef60c9027be03b4a18/a3iwo6fR2969nL9YyvJM1.jpeg
Yiming Zhao
gaotiexinqu
user
gaotiexinqu
69817bb2ce18b18628096125
Yao Hu
69817bb2ce18b18628096126
Philip Torr
69817bb2ce18b18628096127
Wanli Ouyang
69817bb2ce18b18628096128
Shaosheng Cao
Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models
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.
Vision-DeepResearch benchmark addresses limitations in evaluating visual-textual search capabilities of multimodal models by introducing realistic evaluation conditions and improving visual retrieval through multi-round cropped-search workflow.
Multimodal Large Language Models Vision-DeepResearch visual-textual fact-finding VQA vision search text-search visual retrieval multimodal deep-research systems