Beacon: 知道何时以及如何进行智能体视觉推理

摘要

智能体视觉推理的根本目标在于提升多模态大语言模型（MLLMs）在复杂任务上的成功率，而非仅仅为模型配备一种看似精密却低效的推理范式。在本工作中，我们通过工具使用的两个关键维度重新审视智能体视觉推理：模式自适应性（Mode Adaptiveness, MA）与工具效应（Tool Effect, TE）。模式自适应性刻画了多模态大语言模型能否识别出工具真正必要的时机并据此调用工具，从而在避免不必要计算开销的同时，提升其在需要工具辅助的难题上的表现。工具效应则刻画了工具使用的实际影响：工具应当扩展模型在纯文本推理无法解决的问题上的能力，同时避免在模型无需工具即可解决的问题上引入额外错误。我们进行了全面分析以量化这两个性质，并通过实验揭示：现有的智能体视觉推理模型在模式自适应性上表现有限，且工具使用在难题上带来的收益，在很大程度上被其在模型本已能够解决的简单问题上引入的损害所抵消。基于这些观察，我们提出了Beacon，一种新型智能体视觉推理模型，其在整体性能、模式自适应性以及真实的工具驱动性能提升方面均表现更优。Beacon的核心在于强化学习阶段的“必要性感知自适应奖励”与“提示引导的能力扩展”机制，二者分别鼓励模型基于任务必要性进行自适应的工具调用，并强化模型在最具挑战性问题上的工具使用能力。在多个基准上的大量实验表明，Beacon在整体性能上表现优异，并在模式自适性与工具效应两方面均取得了显著提升。

English

The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks, rather than merely equipping them with a sophisticated yet inefficient reasoning paradigm. In this work, we rethink agentic visual reasoning through two key dimensions of tool use: Mode Adaptiveness (MA) and Tool Effect (TE). Mode Adaptiveness characterizes whether an MLLM can recognize when tools are truly necessary and invoke them accordingly, thereby avoiding unnecessary computational overhead while improving performance on challenging problems that require tool assistance. Tool Effect characterizes the actual impact of tool use: tools should extend the model's capabilities on problems unsolvable through text-only reasoning, while avoiding additional errors on problems that the model can already solve without tools. We conduct a comprehensive analysis to quantify these two properties and empirically reveal that existing agentic visual reasoning models exhibit limited Mode Adaptiveness, while the gains produced by tool use on hard examples are largely offset by the harm introduced on easy examples that the models can already solve. Motivated by these observations, we propose Beacon, a novel agentic visual reasoning model that achieves stronger overall performance, improved Mode Adaptiveness, and genuine tool-induced performance gains. At the core of Beacon are the Necessity-Aware Adaptive Reward and the Hint-Guided Capability Expansion mechanism in the reinforcement learning stage, which respectively encourage adaptive tool invocation based on task necessity and strengthen the model's tool-use capability on the most challenging problems. Extensive experiments across diverse benchmarks demonstrate the strong overall performance of Beacon and its substantial improvements in both Mode Adaptiveness and Tool Effect.