MVoT: Multimodal Visualization-of-Thought

Updated 24 July 2025

MVoT is a paradigm that externalizes AI reasoning via integrated visual and language modalities, bridging the semantic gap with clear, interpretable artifacts.
It employs iterative tool-driven visual exploration, programmatic visual manipulation, and intrinsic visual imagination to generate step-wise, interactive thought representations.
Applications span scientific reasoning, navigation, and data exploration, with empirical results showing significant gains in planning accuracy and interpretability.

Multimodal Visualization-of-Thought (MVoT) refers to a class of computational paradigms, tools, and reasoning methodologies in which artificial intelligence systems externalize intermediate steps of reasoning through multiple sensory or representational modalities (notably vision and language), thereby transforming thought processes from internal, opaque flows into manipulable, interpretable, and interactive artifacts. MVoT is rooted in cognitive science observations about human intelligence—humans often supplement or even replace abstract reasoning with sketches, diagrams, or other multimodal aids, especially in domains where spatial, compositional, or structural reasoning is essential. Over the last few years, MVoT has gained significant traction as a response to the limitations of unimodal reasoning (e.g., text-only chain-of-thought), particularly in LLMs and large multimodal LLMs (MLLMs).

1. Foundational Principles and Cognitive Motivation

At the foundation of MVoT lies the recognition that language-only reasoning creates a "semantic gap" between continuous perceptual input and symbolic, discrete reasoning chains (Su et al., 30 Jun 2025). Human cognition often overcomes such a gap via dynamic, externalized aids—a phenomenon seen in diagrammatic proofs, spatial navigation, or even informal problem-solving on paper. MVoT thus repositions visual and other sensory modalities as active, manipulable workspaces in the reasoning loop, enabling iterative perceptual exploration, explicit externalization of intermediate mental states, and visual simulation of future or hypothetical outcomes (Su et al., 30 Jun 2025).

Key properties of the MVoT paradigm include:

Iterative, step-wise construction of materialized "thoughts" (e.g., images, diagrams, annotations) interleaved with language-based reasoning (Li et al., 13 Jan 2025, Wu et al., 4 Apr 2024, Wang et al., 12 Apr 2025).
The use of these intermediate artifacts as cognitive scratchpads, supporting grounded validation, revision, and planning.
Alignment of machine reasoning processes with observed patterns in human visual and abstract thinking (Liu et al., 26 May 2025).

2. Methodologies and Core Mechanisms

MVoT encompasses a spectrum of mechanisms for multimodal reasoning, distinguished by both architecture and interaction style. The literature proposes a trajectory of three representative stages (Su et al., 30 Jun 2025):

Tool-Driven Visual Exploration: Here, models invoke external visual modules (e.g., segmentation, bounding box extraction, directed annotation) as needed. For example, the VTool-R1 system interleaves text-based chains-of-thought with explicit invocations of Python-based visual editing tools, allowing the model to "think with images" by generating and iteratively refining visual modifications over input data (Wu et al., 25 May 2025). The outcome of each tool call is fed back as a new perception input to the model.
Programmatic Visual Manipulation: The model generates executable code snippets describing visual reasoning operations (e.g., drawing auxiliary geometric lines, cropping image regions, transforming views), as in whiteboard-of-thought prompting (Menon et al., 20 Jun 2024) and conceptual diagram generation for planning (Borazjanizadeh et al., 14 Mar 2025). These code-generated artifacts allow for transparent, human-interpretable inspection of the model’s intermediate logic.
Intrinsic Visual Imagination: At this most autonomous stage, the MLLM synthesizes visual intermediate states within its own latent space—using learned image-generation capabilities—in an interleaved sequence with text, without depending on external tool execution (Li et al., 13 Jan 2025, Cheng et al., 21 May 2025). The output may merge generative and edited visual content (e.g., model-generated sketches or visual rationales), offering direct insights into the evolving state of model cognition.

MVoT is operationalized via frameworks such as:

Interleaved image–text chain-of-thoughts (I-MCoT), where models dynamically alternate between language and visual token generation in a single chain (Cheng et al., 21 May 2025).
Dynamic draft augmentation, where evolving environments (such as grid worlds in navigation tasks) are represented as overlays with visualized agent paths, allowing for real-time reasoning about changing spatial layouts (Ou et al., 22 May 2025).

Typical implementations are grounded by formal expressions, e.g.: $v_i \sim p_\theta (v_i \mid \text{context}, z_1, ..., z_i, v_1, ..., v_{i-1})$

$z_{i+1} \sim p_\theta (z_{i+1} \mid \text{context}, z_1, ..., z_i, v_1, ..., v_i)$

where $v_i$ and $z_i$ denote visual and textual reasoning steps, respectively (Li et al., 13 Jan 2025, Wu et al., 4 Apr 2024).

3. Multimodal Thought Representations: Typology and Internal Mechanisms

The forms that multimodal visualizations-of-thought take within reasoning models are well-studied (Cheng et al., 21 May 2025, Liu et al., 26 May 2025):

Text-Based Visual Thoughts: Distilled descriptions of salient features or relations, often in natural or structured language (N-LANG or S-LANG).
Edited Visuals: Visual maps, images with overlays, or highlighted elements, generated by external or in-model editing tools (E-IMG).
Generative Visuals: Model-synthesized diagrams or sketches using built-in vision generation modules (G-IMG).
Visual Abstracts: Simplified, information-rich sketches (e.g., silhouettes, edge maps, semantic outlines) obtained by mapping images through abstraction functions to filter redundant information (Liu et al., 26 May 2025).

The utility of each form is determined by clarity and conciseness in expressing intermediate logic; e.g. image forms are favored in complex, fine-grained tasks, while text forms often suffice for broad semantic summarization (Cheng et al., 21 May 2025). Empirical studies show that incorporating and appropriately “caching” such intermediate visual states within model architectures (e.g., as attention targets in higher transformer layers) improves both performance and interpretability (Cheng et al., 21 May 2025).

4. Applications and Evaluation Benchmarks

MVoT’s approach has been validated—and continues to be extended—in a diverse set of domains:

Spatial and Embodied Reasoning: Navigation and planning in dynamic environments (including grid mazes, robotics, and video games), where interleaved visual drafts or conceptual diagrams are critical for robust path finding and error reduction (Ou et al., 22 May 2025, Borazjanizadeh et al., 14 Mar 2025, Wang et al., 12 Apr 2025).
Scientific, STEM, and Mathematical Reasoning: Education and problem-solving tasks where auxiliary constructions (such as geometric diagrams) and iterative visualization directly mirror human approaches (Su et al., 30 Jun 2025, Li et al., 13 Jan 2025).
Data Exploration and Visualization Design: Systems such as DataBreeze and Umwelt leverage multimodal visualizations, natural language, sonification, and direct manipulation to support exploratory data analysis and externalization of mental models (Srinivasan et al., 2020, Zong et al., 29 Feb 2024).
Idea and Knowledge Mapping: Immersive tools (e.g., VIVRA) transform free-form, multimodal user input into visual structures for ideation, reflection, and knowledge organization (Xing et al., 23 Sep 2024).

Evaluation frameworks for MVoT now transcend accuracy-only metrics and include benchmarks that explicitly measure the quality, grounding, and logical coherence of intermediate multimodal reasoning steps—see the CoMT benchmark, which requires both multimodal inputs and outputs in reasoning chains, advancing beyond prior text-only evaluation paradigms (Cheng et al., 17 Dec 2024).

5. Benefits, Limitations, and Empirical Findings

Empirical research consistently demonstrates substantial gains in reasoning accuracy, interpretability, and robustness when MVoT principles are applied in complex tasks (Li et al., 13 Jan 2025, Borazjanizadeh et al., 14 Mar 2025, Wang et al., 12 Apr 2025, Liu et al., 26 May 2025). Illustrative findings include:

17% average accuracy gain with visual abstracts over strong baselines such as GPT-4o, and additional improvements when combining visual and textual reasoning (Liu et al., 26 May 2025).
Significant boosts in planning accuracy—from 35.5% to 90.2% in challenging domains—when integrating self-generated diagrams into reasoning cycles (Borazjanizadeh et al., 14 Mar 2025).
Marked performance increases (up to 92% accuracy) in challenging visual reasoning scenarios when models employ dynamic, code-driven visual sketchpads or visual-handoff methods (Menon et al., 20 Jun 2024).

Nonetheless, challenges remain. Token and compute efficiency are ongoing issues, as visual chain-of-thoughts can explode in resource demands. There are also questions about the safe integration of generated visual evidence and the risks of adversarial or misleading in-model visuals (Su et al., 30 Jun 2025).

6. Theoretical, Practical, and Future Directions

From a theoretical perspective, MVoT is underpinned by probabilistic formulations of intermixed token generation, action-sequence planning, and attention-based mechanisms that align visual cache tokens, static percepts, and dynamic externalizations (Cheng et al., 21 May 2025, Su et al., 30 Jun 2025). Programmatic, tool-based, and imaginative approaches offer different strengths: flexibility, transparency, or efficiency.

Looking forward, key avenues for deeper research and systems development include:

Adaptive strategies that dynamically modulate the depth and modality of visual elaboration (e.g., deploying abstractions vs. detailed constructions only as needed) (Su et al., 30 Jun 2025).
The expansion of benchmarks to cover temporal, video, and active interaction settings, requiring richer modeling of dynamic perceptual streams (Ou et al., 22 May 2025).
Robust safety and verifiability checks for internally generated visual evidence, especially in workflows with high-stakes or adversarial potential (Su et al., 30 Jun 2025).
Integration of MVoT architectures into unified world models that can fluidly coordinate rapid symbolic inference with deeper visual simulation and deliberation (Su et al., 30 Jun 2025).
Broader application in accessibility, human-computer interaction, and collaborative creative domains, particularly through immersive and cross-modal interfaces (Zong et al., 29 Feb 2024, Xing et al., 23 Sep 2024).

7. Summary Table: Key Methods in Multimodal Visualization-of-Thought

Method/Class	Core Approach	Representative Papers
Tool-Augmented	Calls external tools for visual edits/drafts	(Wu et al., 25 May 2025, Wang et al., 12 Apr 2025)
Programmatic	Generates code for diagrams/visuals	(Menon et al., 20 Jun 2024, Borazjanizadeh et al., 14 Mar 2025)
Intrinsic Imagination	Generates visual tokens natively, internalizes intermediate states	(Li et al., 13 Jan 2025, Cheng et al., 21 May 2025)
Visual Abstract	Transforms images to simple conceptual forms	(Liu et al., 26 May 2025)
Multimodal Reasoning Chains	Alternates or fuses visual & text steps	(Cheng et al., 17 Dec 2024, Cheng et al., 21 May 2025)

References

For further research and implementation specifics, see the following key references:

"Imagine while Reasoning in Space: Multimodal Visualization-of-Thought" (Li et al., 13 Jan 2025)
"Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers" (Su et al., 30 Jun 2025)
"Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought" (Cheng et al., 21 May 2025)
"Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning" (Ou et al., 22 May 2025)
"VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search" (Wang et al., 12 Apr 2025)
"Visual Abstract Thinking Empowers Multimodal Reasoning" (Liu et al., 26 May 2025)
"VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use" (Wu et al., 25 May 2025)
"Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities" (Menon et al., 20 Jun 2024)
"CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-LLMs" (Cheng et al., 17 Dec 2024)

In sum, Multimodal Visualization-of-Thought is a rapidly evolving paradigm, shifting the locus of machine reasoning from opaque, internal processes to dynamic, accessible, and multifaceted externalizations—aligning computational intelligence more closely with the mechanisms and transparency of human cognition.