Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

📰 ArXiv cs.AI

Vision-DeepResearch incentivizes deep research capability in multimodal large language models

advanced Published 25 Mar 2026

Action Steps

Augment multimodal large language models with external knowledge sources
Implement 'reasoning-then-tool-call' approach for visual and textual search engines
Evaluate the model's performance on tasks requiring extensive factual information
Fine-tune the model to improve its deep research capability

Who Needs to Know This

Researchers and AI engineers working on multimodal large language models can benefit from this approach to improve the model's ability to conduct deep research and retrieve factual information

Key Insight

💡 Multimodal large language models can be improved by augmenting them with external knowledge sources and implementing a 'reasoning-then-tool-call' approach

Key Takeaways

Vision-DeepResearch incentivizes deep research capability in multimodal large language models

Full Article

Title: Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

Abstract:
arXiv:2601.22060v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a nai

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