Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
Proposes Sketchpad, enabling multimodal models to draw visual sketches, boosting math and visual reasoning by over 12%.
Key Findings
Methodology
This work introduces the Sketchpad framework, empowering multimodal language models with visual sketching capabilities through code synthesis and integration of specialist vision models. The system generates auxiliary lines, boxes, masks, etc., by producing Python code that calls tools like matplotlib, networkx, detection, segmentation, and depth estimation models. The model follows an iterative process: planning (Thought), executing code (Action), observing outputs (Observation), and updating its reasoning path. This chain-of-thought style, extended into visual domain, allows dynamic adjustment of sketches based on intermediate results, significantly improving reasoning accuracy across math and vision tasks.
Key Results
- On geometry problems from Geometry3K, SKETCHPAD boosts GPT-4o accuracy by 12.7%, reaching 80.3% on V*Bench, outperforming baselines. In visual reasoning, it improves BLINK spatial reasoning to 83.9%.
- In mathematical tasks, auxiliary line drawing, function plotting, and graph visualization lead to over 40% performance gains in complex problems. The integration of visual tools enhances the model’s spatial and structural understanding.
- Experiments with detection, segmentation, and depth models demonstrate that visual sketches help models interpret images more accurately, leading to robust multi-modal reasoning and better interpretability of solutions.
Significance
This study addresses the core limitation of current multimodal models relying solely on textual reasoning, by introducing visual sketches as intermediate reasoning artifacts. Mimicking human drawing strategies, it bridges the gap between language and visual-spatial understanding, enabling models to handle complex geometric, graph, and visual tasks more effectively. The approach paves the way for more interpretable, flexible AI systems capable of reasoning in scientific, educational, and practical domains, reducing reliance on static pretraining and enhancing adaptability.
Technical Contribution
The paper presents a novel framework combining code-driven visual sketching with specialist vision models, forming an iterative reasoning loop. It introduces multi-turn planning, dynamic sketch generation, and tool integration, all without additional training. This approach surpasses traditional chain-of-thought and tool-use paradigms by enabling models to manipulate visual artifacts directly, providing transparent reasoning pathways and expanding the scope of multimodal AI capabilities.
Novelty
This is the first systematic integration of visual sketching as an intermediate step in multimodal reasoning, distinct from prior methods that rely solely on text or static image generation. By synthesizing code to produce dynamic sketches and leveraging specialized vision tools, it creates a flexible, human-like reasoning process that significantly improves performance on complex tasks, setting a new standard for multimodal AI.
Limitations
- The current implementation depends on pre-trained vision models and code execution, leading to high computational costs and limited real-time application. Optimization is needed for efficiency.
- Visual sketching quality depends on tool accuracy; errors in detection or segmentation can mislead reasoning. Handling ambiguous or cluttered scenes remains challenging.
- Model robustness in highly complex or noisy scenarios is still limited; further research is required to improve generalization and error correction in diverse environments.
Future Work
Future directions include developing more efficient, end-to-end trainable systems, expanding toolsets for broader scientific reasoning, and enabling autonomous planning. Enhancing robustness, reducing computational overhead, and applying the framework to real-world tasks like robotics, scientific discovery, and education are promising avenues for advancing multimodal AI.
AI Executive Summary
Humans naturally use drawing as a powerful tool for reasoning—sketching auxiliary lines in geometry, marking maps, or visualizing ideas to simplify complex problems. Despite this, current multimodal language models (LMs) primarily rely on text-based reasoning, lacking the ability to create and manipulate visual artifacts that mirror human sketching. This gap limits their performance in tasks requiring spatial understanding and structural reasoning.
To address this, the paper introduces the Sketchpad framework, a novel system that equips multimodal models with the ability to generate visual sketches—lines, boxes, masks—by synthesizing code that calls specialized vision models. The process involves multi-turn interactions: the model plans its reasoning steps, executes code to produce sketches, observes the results, and iteratively refines its approach. This mimics human drawing strategies, allowing models to dynamically adjust their reasoning based on intermediate visual artifacts.
Experimental results demonstrate that this approach significantly enhances performance across diverse tasks. In geometry problems, accuracy improves by an average of 12.7%, with the model achieving 80.3% on V*Bench, surpassing previous state-of-the-art. In complex visual reasoning, the model's accuracy on BLINK tasks increases by 8.6%. The integration of professional vision tools—detection, segmentation, depth estimation—enables more precise interpretation of images, leading to more robust reasoning.
This work represents a paradigm shift in multimodal AI, moving from static, text-only reasoning to dynamic, visual-augmented processes. It opens new avenues for interpretable, flexible AI systems capable of scientific reasoning, education, and automation. Future efforts will focus on improving efficiency, expanding toolsets, and applying the framework to real-world applications, ultimately bringing AI closer to human-like visual thinking.
Deep Analysis
Background
近年来，多模态学习快速发展，结合视觉与语言信息极大提升模型推理能力。早期工作如VQA、视觉问答主要依赖单一模态，随后链式推理（Chain-of-Thought）策略被引入，显著改善复杂推理表现，但仍局限于文本。近年来，结合工具使用（Tool-use）和视觉提示（Visual Prompting）的方法逐渐兴起，利用目标检测、深度估计等专业视觉模型增强理解能力。然而，现有模型缺乏模拟人类绘图的能力，难以在几何、图结构等任务中实现高效推理。此背景推动研究者探索多模态交互与工具集成的新途径，以突破推理瓶颈。
Core Problem
当前多模态模型在复杂推理任务中表现有限，尤其在几何、图算法和策略游戏等领域，缺乏有效的中间视觉表达手段。文本链式推理难以捕捉空间关系和结构信息，导致推理准确率不足。尽管GPT-4等预训练模型在语言理解上表现优异，但在空间推理场景中仍显不足。引入视觉草图作为中间推理步骤，结合专业视觉模型，能更直观地表达空间关系和结构信息，提升推理能力，成为解决此难题的关键。
Innovation
本研究创新性提出Sketchpad框架，首次将视觉草图作为多模态推理的中间步骤。具体创新包括：
- �� 结合代码生成（Python、Matplotlib）实现动态绘图，支持线条、框、掩码等多种草图形式；
- �� 集成目标检测、分割、深度估计等专业视觉模型，提升视觉理解能力；
- �� 多轮交互式推理流程，模型根据观察结果调整推理路径，增强逻辑性；
- �� 无需微调，直接通过提示实现多模态推理能力的提升。这些创新使模型能更像人类用绘图辅助推理，显著改善复杂数学和视觉任务表现。
Methodology
- �� 输入：多模态查询（文本+图像）
- �� 生成：模型制定推理计划（Thought），包括绘图、分析步骤
- �� 执行：调用代码生成模块，绘制辅助线、标记、掩码等
- �� 观察：视觉模型返回新图像或数据（深度、检测结果）
- �� 更新：模型根据观察调整推理路径，生成下一步计划
- �� 多轮交互：持续迭代，直到模型自信回答
- �� 输出：最终答案，结合所有中间推理和视觉证据
Experiments
采用几何（Geometry3K）、函数（IsoBench）、图算法（IsoBench）、国际象棋（IsoBench）等多任务，使用GPT-4o和GPT-4 Turbo作为基础模型。对比无草图、只用文本推理的性能，评估指标包括准确率、F1、任务特异性得分。每个任务中，模型通过提示生成绘图代码，调用专业视觉工具，逐步逼近答案。所有实验在公开数据集上进行，确保公平性和可复现性。
Results
SKETCHPAD在几何任务中提升平均12.7%准确率，V*Bench达80.3%，超越基线。视觉任务中，整体提升8.6%，BLINK空间推理83.9%。在复杂几何和图算法中，性能提升超过40%。模型利用辅助线、函数图像、深度图、检测框等视觉草图，有效增强空间理解和推理逻辑。多模态结合专业视觉模型后，推理鲁棒性和解释性显著改善，验证了草图作为中间推理工具的有效性。
Applications
该方法适用于自动化数学推理、科学教育、机器人导航、图像理解等场景。模型能在无需大量微调的情况下，通过提示实现复杂推理，降低门槛。未来可结合自主规划能力，应用于科研辅助、智能教育、自动化检测等领域，推动多模态AI的普及与智能化水平提升。
Limitations & Outlook
目前依赖预训练模型和专业视觉工具，计算资源消耗大，实时性不足。草图生成在复杂或模糊场景中可能出现误导，影响推理准确性。模型在极端复杂或模糊场景下表现尚不稳定，需增强鲁棒性。未来需优化工具效率，提升模型自主规划和泛化能力，解决实际应用中的效率与准确性难题。
Plain Language Accessible to non-experts
想象你在厨房做菜，遇到一道复杂菜谱。单靠文字描述很难理解每一步，但如果你用图画画出每个步骤，比如切菜、炒锅、调料的放置，就会更清楚。厨师们经常用图示帮助理解菜谱，尤其是复杂的菜肴。类似地，AI模型在解决数学或视觉难题时，也可以用画图的方式来辅助推理。它们可以画线、框、掩码，就像厨师画出菜的步骤一样。这样，模型就能更直观地理解空间关系、结构信息，推理也变得更准确。这个方法让AI像人一样用画图思考，不仅提高了效率，还让推理过程更透明。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏。光靠看图片和文字说明，你可能很难找到正确的拼法，但如果你在拼图上用笔画出线条，标记关键点，就会更容易找到拼图的正确位置。AI模型也是一样，它们平时只用文字回答问题，但有了这个新方法，它们可以自己画出线条、框框，帮助理解复杂的数学题或图片。比如在几何题中，它们可以画出辅助线，帮忙证明角度关系；在看图片时，它们可以画出物体的轮廓或深度，理解空间关系。这样一来，AI就像一个会画图的聪明助手，能更快、更准地解决难题。是不是很酷？就像你用画笔帮自己理清思路一样！
Abstract
Humans draw to facilitate reasoning: we draw auxiliary lines when solving geometry problems; we mark and circle when reasoning on maps; we use sketches to amplify our ideas and relieve our limited-capacity working memory. However, such actions are missing in current multimodal language models (LMs). Current chain-of-thought and tool-use paradigms only use text as intermediate reasoning steps. In this work, we introduce Sketchpad, a framework that gives multimodal LMs a visual sketchpad and tools to draw on the sketchpad. The LM conducts planning and reasoning according to the visual artifacts it has drawn. Different from prior work, which uses text-to-image models to enable LMs to draw, Sketchpad enables LMs to draw with lines, boxes, marks, etc., which is closer to human sketching and better facilitates reasoning. Sketchpad can also use specialist vision models during the sketching process (e.g., draw bounding boxes with object detection models, draw masks with segmentation models), to further enhance visual perception and reasoning. We experiment with a wide range of math tasks (including geometry, functions, graphs, and chess) and complex visual reasoning tasks. Sketchpad substantially improves performance on all tasks over strong base models with no sketching, yielding an average gain of 12.7% on math tasks, and 8.6% on vision tasks. GPT-4o with Sketchpad sets a new state of the art on all tasks, including V*Bench (80.3%), BLINK spatial reasoning (83.9%), and visual correspondence (80.8%). All codes and data are in https://visualsketchpad.github.io/.