Research map / Vision-language and multimodal / Multimodal LLMs
Imagine While Reasoning in Space: Multimodal Visualization-of-Thought
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes Multimodal Visualization-of-Thought (MVoT), which fine-tunes the Anole-7B (Chameleon-based) model to interleave verbal reasoning with generated image visualizations of its states, using a token discrepancy loss to improve visual token quality on maze, MiniBehavior, and FrozenLake spatial reasoning tasks.
Problem
Chain-of-thought with purely textual reasoning struggles on complex spatial reasoning tasks, and text captions of visual layouts are error-prone; it is unclear whether MLLMs can reason in the visual modality.
Approach
Let a native multimodal autoregressive model generate image visualizations of each intermediate reasoning state alongside verbal thoughts, and add a token discrepancy loss that penalizes predicted visual-token probabilities by MSE distance between codebook embeddings, aligning token-embedding and visual-embedding spaces.
Key results
- MVoT with Anole-7B reaches 92.95% on Maze, 95.14% on MiniBehavior, and 85.60% on FrozenLake, versus 61.48% for CoT on FrozenLake.
- Removing token discrepancy loss drops FrozenLake accuracy from 0.8560 to 0.7260 and Maze accuracy from 0.9295 to 0.7468.
- Combining MVoT and CoT predictions yields an upper bound of 0.9246 on FrozenLake, suggesting complementary strengths.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MVoT instead of CoT | Anole-7B · Maze | accuracy: 0.9792 -> 0.9295 | Hurt |
| Use MVoT instead of CoT | Anole-7B · MiniBehavior | accuracy: 0.9812 -> 0.9514 | Hurt |
| Use MVoT instead of CoT | Anole-7B · FrozenLake | accuracy: 0.6148 -> 0.8560 | Helped |
| Add interleaved image training data | Direct Anole-7B · Maze | accuracy: 0.7171 -> 0.8678 | Helped |
| Add interleaved image training data | Direct Anole-7B · MiniBehavior | accuracy: 0.7250 -> 0.8406 | Helped |
| Add interleaved image training data | Direct Anole-7B · FrozenLake | accuracy: 0.7788 -> 0.6460 | Hurt |
| Add token discrepancy loss (ablation) | MVoT · Maze | accuracy: 0.7468 -> 0.9295 | Helped |
| Add token discrepancy loss (ablation) | MVoT · MiniBehavior | accuracy: 0.7228 -> 0.9514 | Helped |
| Add token discrepancy loss (ablation) | MVoT · FrozenLake | accuracy: 0.7260 -> 0.8560 | Helped |
Surprising findings
- CoT with textual coordinates scores above 97% on Maze and MiniBehavior but drops to 61.48% on FrozenLake, below the Direct baseline.
- Interleaved training without image-token loss hurts FrozenLake performance (64.60%) relative to Direct (77.88%).
Limitations
- Generated visualizations often reproduce task-irrelevant background details while missing intended state changes.
- Explicit visual generation adds inference computational overhead.
- Evaluation limited to three grid-based synthetic spatial tasks with a single 7B backbone.
Experimental setup
Datasets: Maze (generated via maze-dataset) MiniBehavior InstallingAPrinter (RL-agent trajectories) FrozenLake (Gym-based trajectories)
Benchmarks: Maze MiniBehavior FrozenLake
Metrics: accuracy V-Acc. V-Red. V-Steps V-Ratio
Baselines: Direct prompting Zero-shot CoT Interleaved training GPT-4o with CoT Visualization-of-Thought
Builds on: Chain-of-Thought Chameleon Visualization-of-Thought LoRA
Closest papers by meaning
- When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought (CVPR 2026 · similarity 0.92)
- Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought (NeurIPS 2025 · similarity 0.92)
- Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing (NeurIPS 2025 · similarity 0.92)
- Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models (NeurIPS 2024 · similarity 0.92)
- VGR: Visual Grounded Reasoning (ICLR 2026 · similarity 0.91)
- ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning (ICLR 2026 · similarity 0.91)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.