Beacon: Knowing When and How to Perform Agentic Visual Reasoning

2026-07-30

AI Summary

This work addresses the limitations of existing vision-language reasoning agents, which often lack adaptive judgment regarding task difficulty, leading to erroneous tool invocations on simple tasks and marginal gains on complex ones. To overcome this, the authors propose a reinforcement learning framework grounded in multimodal large language models, featuring a necessity-aware adaptive reward mechanism and a prompt-guided capability expansion strategy. This approach enables agents to invoke tools selectively based on task demands. The method significantly improves both the accuracy of tool invocation timing and the agent’s ability to solve challenging problems, achieving concurrent gains in overall performance and tool utilization efficacy across multiple benchmarks.

Abstract

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.

Problem

• agentic visual reasoning
• Mode Adaptiveness
• Tool Effect
• multimodal large language models
• tool use

Innovation

• Mode Adaptiveness
• Tool Effect
• Agentic Visual Reasoning
• Necessity-Aware Adaptive Reward
• Hint-Guided Capability Expansion