Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning

Xingang Guo, Utkarsh Tyagi, Advait Gosai, Paula Vergara, Ernesto Gabriel Hernández Montoya, Chen Bo Calvin Zhang, Bin Hu, Yunzhong He, Bing Liu, Rakshith Sharma Srinivasa

Keywords: Multi-Modal, MLLM, Vision Tool Use, Reasoning

TL;DR: We present VisToolBench, the first vision tool-use reasoning benchmark that requires model think with images.

Abstract: Multimodal Large Language Models (MLLMs) are increasingly applied in real-world scenarios where user-provided images are often imperfect, requiring active image manipulations such as cropping, editing, or enhancement to uncover salient visual cues. Beyond static visual perception, MLLMs must also *think with images*: dynamically transforming visual content and integrating it with other tools to solve complex tasks. However, this shift from treating vision as passive context to a manipulable cognitive workspace remains underexplored. Most existing benchmarks still follow a *think about images* paradigm, where images are regarded as static inputs. To address this gap, we introduce VisToolBench, a vision tool-use reasoning benchmark that rigorously evaluates MLLMs’ ability to perceive, transform, and reason across complex visual–textual tasks under the *think with images* paradigm. VisToolBench comprises 1,204 challenging, open-ended vision tasks (603 single-turn, 601 multi-turn) spanning across five diverse domains, each paired with detailed rubrics to enable systematic evaluation. Our evaluation shows that current MLLMs struggle with tasks requiring effective integration of vision and general-purpose tools. Even the strongest model (GPT-5-think) reaches only 18.44\% pass rate. We further observe divergent tool-use behaviors, with OpenAI models benefiting from diverse image manipulations while Gemini-2.5-pro shows no improvement. By introducing the first benchmark centered on *think with images*, VisToolBench offers critical insights for advancing visual intelligence in MLLMs.

Primary Area: datasets and benchmarks

Submission Number: 1127