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DeepEyesV2: Toward Agentic Multimodal Model

ICLR 2026 · Vision-language and multimodal › Multimodal LLMs · approach: Multimodal LLM benchmarks

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Builds DeepEyesV2, a 7B agentic multimodal model that unifies code execution and web search in one reasoning loop via SFT cold start followed by DAPO-based RL, and introduces the RealX-Bench benchmark.

Problem

Existing multimodal models are passive and cannot reliably learn to invoke multiple external tools (code execution and web search) within a reasoning loop; direct RL alone fails to induce stable tool use.

Approach

Two-stage pipeline: a cold-start SFT on curated multi-turn code/search trajectories (plus long CoT) to establish tool-use patterns, then outcome-only RL (accuracy + format rewards) to refine tool invocation, with data filtered for difficulty and beneficial tool use.

Key results

• On MathVerse, DeepEyesV2 reaches 52.7% (+7.1 over Qwen2.5-VL-7B); on MMSearch it reaches 63.7% vs 53.8% for MMSearch-R1.
• On RealX-Bench, DeepEyesV2 averages 28.3%, +6.0 over Qwen2.5-VL-7B with tools, while the best proprietary model Gemini 2.5 Pro with both searches reaches 46.0% and humans 70.0%.
• Ablation shows the full cold-start mix (perception + reasoning + long CoT) and RL data including search yield the best results, e.g. MMSearch rising from 56.8 (SFT) to 63.7 after RL with search data.

Surprising findings

• Direct RL on Qwen2.5-VL-7B led to reward hacking, with the model emitting a single placeholder non-executable code block per query, motivating the cold start.
• After RL, tool-invocation frequency decreased while variance stayed high, indicating adaptive rather than fixed tool use.

Limitations

• RealX-Bench is small (300 QA pairs), limiting statistical power.
• Error analysis identifies tool execution, tool selection, and tool result analysis errors as failure modes.
• Search results depend on proprietary SerpAPI/Google tools, which may affect reproducibility.

Experimental setup

Datasets: V* ArxivQA PixMo Counting TallyQA SeekWorld ReVisual MathCoder ReTool MMSearch-R1 VGR Chain-of-Focus VLM-R^3

Benchmarks: RealX-Bench V* HRBench MME-RealWorld TreeBench OCRBench SEED-Bench-2-Plus CharXiv ChartQA MathVista MathVerse MathVision

Metrics: accuracy

Baselines: GPT-4o Gemini 2.5 Pro o3 Qwen2.5-VL-7B Qwen2.5-VL-32B Qwen2.5-VL-72B DeepEyes Thyme MMSearch-R1 Pixel-Reasoner WebWatcher MM-Eureka

Builds on: DeepEyes DAPO Qwen2.5-VL PyVision Thyme

Closest papers by meaning

• Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks (ICLR 2026 · similarity 0.90)
• Agentic Jigsaw Interaction Learning for Enhancing Visual Perception and Reasoning in Vision-Language Models (ICLR 2026 · similarity 0.90)
• PyVision-RL: Forging Open Agentic Vision Models via RL (ICML 2026 · similarity 0.90)
• AgentVista: Evaluating Multimodal Agent in Ultra-Challenging Realistic Visual Scenarios (NeurIPS 2026 · similarity 0.90)
• ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning (CVPR 2026 · similarity 0.90)
• SenseSearch: Empowering Vision-Language Models with High-Resolution Agentic Search-Reasoning via Reinforcement Learning (CVPR 2026 · similarity 0.90)