# Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities

arXiv: https://arxiv.org/abs/2406.14562 · HTML: https://mlsanity.com/paper/2406.14562/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [Visual Programming: Compositional visual reasoning without training](https://mlsanity.com/paper/2211.11559/) (2022)
- [Generating Images with Multimodal Language Models](https://mlsanity.com/paper/2305.17216/) (2023)
- [ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs](https://mlsanity.com/paper/2402.11753/) (2024)
- [Patching open-vocabulary models by interpolating weights](https://mlsanity.com/paper/2208.05592/) (2022)
- [GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning](https://mlsanity.com/paper/2312.12241/) (2023)
- [Evaluating Spatial Understanding of Large Language Models](https://mlsanity.com/paper/2310.14540/) (2023)
- [Disentangling visual and written concepts in CLIP](https://mlsanity.com/paper/2206.07835/) (2022)
- [DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models](https://mlsanity.com/paper/2306.14685/) (2023)
- [Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning](https://mlsanity.com/paper/2312.10160/) (2023)
- [OCR-VQGAN: Taming Text-within-Image Generation](https://mlsanity.com/paper/2210.11248/) (2022)
- [Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons](https://mlsanity.com/paper/2103.10480/) (2021)

## Built on by (most cited first, 16 of 16)

- [Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning](https://mlsanity.com/paper/2507.16746/) (2025)
- [A Survey on (M)LLM-Based GUI Agents](https://mlsanity.com/paper/2504.13865/) (2025)
- [Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought](https://mlsanity.com/paper/2505.15510/) (2025)
- [VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning](https://mlsanity.com/paper/2510.14672/) (2025)
- [Let Androids Dream of Electric Sheep: A Human-Inspired Image Implication Understanding and Reasoning Framework](https://mlsanity.com/paper/2505.17019/) (2025)
- [Iterative Graph Alignment](https://mlsanity.com/paper/2408.16667/) (2024)
- [MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning](https://mlsanity.com/paper/2507.07297/) (2025)
- [3D Space as a Scratchpad for Editable Text-to-Image Generation](https://mlsanity.com/paper/2601.14602/) (2026)
- [Measuring AI Reasoning: A Guide for Researchers](https://mlsanity.com/paper/2605.02442/) (2026)
- [When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?](https://mlsanity.com/paper/2605.27932/) (2026)
- [DeltaV: Thinking with Visual State Updates in Unified Large Multimodal Models](https://mlsanity.com/paper/2607.08434/) (2026)
- [Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models](https://mlsanity.com/paper/2508.04427/) (2025)
- [Is Visual Prompting All You Need? Studying VLM Spatial Reasoning under Progressive Visual Scaffolds](https://mlsanity.com/paper/2608.21170/) (2026)
- [CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions](https://mlsanity.com/paper/2608.28958/) (2026)
- [Probe to Act: Elevating Browser-Use Agent via Active Visual Probing](https://mlsanity.com/paper/2609.33646/) (2026)
- [More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training](https://mlsanity.com/paper/2610.19443/) (2026)

## Abstract (arXiv)

When presented with questions involving visual thinking, humans naturally switch reasoning modalities, often forming mental images or drawing visual aids. Large language models have shown promising results in arithmetic and symbolic reasoning by expressing intermediate reasoning in text as a chain of thought, yet struggle to extend this capability to answer text queries that are easily solved by visual reasoning, even with extensive multimodal pretraining. We introduce a simple method, whiteboard-of-thought prompting, to unlock the visual reasoning capabilities of multimodal large language models across modalities. Whiteboard-of-thought prompting provides multimodal large language models with a metaphorical `whiteboard' to draw out reasoning steps as images, then returns these images back to the model for further processing. We find this can be accomplished with no demonstrations or specialized modules, instead leveraging models' existing ability to write code with libraries such as Matplotlib and Turtle. This simple approach shows state-of-the-art results on four difficult natural language tasks that involve visual and spatial reasoning. We identify multiple settings where GPT-4o using chain-of-thought fails dramatically, including more than one where it achieves 0% accuracy, while whiteboard-of-thought enables up to 92% accuracy in these same settings. We present a detailed exploration of where the technique succeeds as well as its sources of error.