V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval
Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improves candidate reranking. However, existing approaches remain largely language-driven, relying on static visual encodings and lacking the ability to actively verify fine-grained visual evidence, which often leads to speculative reasoning in visually ambiguous cases. We propose V-Retrver, an evidence-driven retrieval framework that reformulates multimodal retrieval as an agentic reasoning process grounded in visual inspection. V-Retrver enables an MLLM to selectively acquire visual evidence during reasoning via external visual tools, performing a multimodal interleaved reasoning process that alternates between hypothesis generation and targeted visual verification.To train such an evidence-gathering retrieval agent, we adopt a curriculum-based learning strategy combining supervised reasoning activation, rejection-based refinement, and reinforcement learning with an evidence-aligned objective. Experiments across multiple multimodal retrieval benchmarks demonstrate consistent improvements in retrieval accuracy (with 23.0% improvements on average), perception-driven reasoning reliability, and generalization.
6985623b4ad556f294b7ebb1
Dongyang Chen
6985623b4ad556f294b7ebb2
Chaoyang Wang
6985623b4ad556f294b7ebb3
Dezhao SU
6985623b4ad556f294b7ebb4
Xi Xiao
6985623b4ad556f294b7ebb5
Zeyu Zhang
claimed_verified
2026-02-18T09:06:42.651Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
SteveZeyuZhang
user
SteveZeyuZhang
6985623b4ad556f294b7ebb6
Jing Xiong
6985623b4ad556f294b7ebb7
Qing Li
6985623b4ad556f294b7ebb8
Yuzhang Shang
6985623b4ad556f294b7ebb9
Shichao Ka
V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval
Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improves candidate reranking. However, existing approaches remain largely language-driven, relying on static visual encodings and lacking the ability to actively verify fine-grained visual evidence, which often leads to speculative reasoning in visually ambiguous cases. We propose V-Retrver, an evidence-driven retrieval framework that reformulates multimodal retrieval as an agentic reasoning process grounded in visual inspection. V-Retrver enables an MLLM to selectively acquire visual evidence during reasoning via external visual tools, performing a multimodal interleaved reasoning process that alternates between hypothesis generation and targeted visual verification.To train such an evidence-gathering retrieval agent, we adopt a curriculum-based learning strategy combining supervised reasoning activation, rejection-based refinement, and reinforcement learning with an evidence-aligned objective. Experiments across multiple multimodal retrieval benchmarks demonstrate consistent improvements in retrieval accuracy (with 23.0% improvements on average), perception-driven reasoning reliability, and generalization.
V-Retrver introduces an evidence-driven retrieval framework that enables multimodal large language models to actively verify visual evidence through an agentic reasoning process, improving retrieval accuracy and reasoning reliability.
Multimodal Large Language Models Chain-of-Thought reasoning multimodal retrieval visual encodings evidence-driven retrieval agentic reasoning visual inspection multimodal interleaved reasoning curriculum-based learning supervised reasoning activation rejection-based refinement reinforcement learning evidence-aligned objective