Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning

• URL: http://arxiv.org/abs/2511.19900v2
• Date: Wed, 26 Nov 2025 05:14:57 GMT
• Title: Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
• Authors: Jiaqi Liu, Kaiwen Xiong, Peng Xia, Yiyang Zhou, Haonian Ji, Lu Feng, Siwei Han, Mingyu Ding, Huaxiu Yao,
• Abstract summary: We propose a self-evolving vision-language agent that achieves continual improvement with tool-integrated reasoning.<n>Agent0-VL incorporates tool usage not only into reasoning but also into self-evaluation and self-repair.<n>Our experiments show that Agent0-VL achieves an 12.5% improvement over the base model.
• Score: 52.99434388759101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotated supervision. Recent self-rewarding approaches attempt to overcome this constraint by allowing models to act as their own critics or reward providers. Yet, purely text-based self-evaluation struggles to verify complex visual reasoning steps and often suffers from evaluation hallucinations. To address these challenges, inspired by recent advances in tool-integrated reasoning, we propose Agent0-VL, a self-evolving vision-language agent that achieves continual improvement with tool-integrated reasoning. Agent0-VL incorporates tool usage not only into reasoning but also into self-evaluation and self-repair, enabling the model to introspect, verify, and refine its reasoning through evidence-grounded analysis. It unifies two synergistic roles within a single LVLM: a Solver that performs multi-turn tool-integrated reasoning, and a Verifier that generates structured feedback and fine-grained self-rewards through tool-grounded critique. These roles interact through a Self-Evolving Reasoning Cycle, where tool-based verification and reinforcement learning jointly align the reasoning and evaluation distributions for stable self-improvement. Through this zero-external-reward evolution, Agent0-VL aligns its reasoning and verification behaviors without any human annotation or external reward models, achieving continual self-improvement. Experiments on geometric problem solving and visual scientific analysis show that Agent0-VL achieves an 12.5% improvement over the base model. Our code is available at https://github.com/aiming-lab/Agent0.

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