Beacon: Knowing When and How to Perform Agentic Visual Reasoning

2026-07-30 · 17:46 UTC ·Qixun Wang, Yang Shi, Letian Cheng... ·1 min read

Brief

Beacon addresses agentic visual reasoning by introducing Mode Adaptiveness (MA) and Tool Effect (TE) to quantify when and whether tool use helps. The authors analyze existing models, find limited MA and net-negative effects on easy examples, and propose Beacon — an RL-trained model with Necessity-Aware Adaptive Reward and Hint-Guided Capability Expansion — achieving stronger overall performance and improved MA/TE across benchmarks (Wang et al., arXiv:2607.28595v1, July 30, 2026).

Why it matters

Introduces two evaluation dimensions for agentic visual reasoning: Mode Adaptiveness (MA) — whether an MLLM recognizes when to invoke tools to avoid unnecessary overhead — and Tool Effect (TE) — whether tools extend capability on problems unsolvable by text-only reasoning without harming easy cases.

Key details

Empirical analysis (Wang et al., arXiv:2607.28595v1, 2026-07-30) finds existing agentic visual-reasoning models exhibit limited MA, and tool-induced gains on hard examples are largely offset by harms introduced on easy examples the models can already solve.

Proposes Beacon: an RL-trained model using Necessity-Aware Adaptive Reward and Hint-Guided Capability Expansion to encourage adaptive tool invocation and strengthen tool use on the hardest problems; experiments across diverse benchmarks show improved overall performance, MA, and genuine tool-induced gains.

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.

Comment: 33 pages