PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on
  Structured Images
Authors: name: Shuoshuo Zhang; name: Zijian Li; name: Yizhen Zhang; name: Jingjing Fu; name: Lei Song; name: Jiang Bian; name: Jun Zhang; name: Yujiu Yang; name: Rui Wang
Published: 2025-09-29 17:59:49

Abstract: Structured images (e.g., charts and geometric diagrams) remain challenging
for multimodal large language models (MLLMs), as perceptual slips can cascade
into erroneous conclusions. Intermediate visual cues can steer reasoning;
however, existing cue-based methods are constrained with low-fidelity image
processing and linear, rigid reasoning patterns, limiting their effectiveness
on complex structured-image tasks. In this paper, we propose PixelCraft, a
novel multi-agent system for high-fidelity image processing and flexible visual
reasoning on structured images. The system comprises a dispatcher, a planner, a
reasoner, critics, and a set of visual tool agents. To achieve high-fidelity
processing, we construct a high-quality corpus and fine-tune an MLLM into a
grounding model, whose pixel-level localizations are integrated with
traditional computer vision (CV) algorithms in tool agents. Building on this
foundation, PixelCraft facilitates flexible visual reasoning through a dynamic
three-stage workflow of tool selection, agent discussion, and self-criticism.
Moreover, unlike prior linear reasoning patterns that simply append historical
images, PixelCraft maintains an image memory to allow the planner to adaptively
revisit earlier visual steps, explore alternative reasoning branches, and
dynamically adjust the reasoning trajectory during discussion. Extensive
experiments on challenging chart and geometry benchmarks demonstrate that
PixelCraft significantly improves visual reasoning performance for advanced
MLLMs, setting a new standard for structured image reasoning. Our code will be
available at https://github.com/microsoft/PixelCraft.