Imagine While Reasoning in Space: Multimodal Visualization-of-Thought
Authors: Chengzu Li
Wenshan Wu
Huanyu Zhang
Yan Xia
Shaoguang Mao
Li Dong
Ivan Vulić
Furu Wei
Venue: ICML 2025 poster (2025)
Keywords: Multimodal Large Language Models
Spatial Reasoning
Primary area: deep_learning->large_language_models

Abstract: Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it struggles in complex spatial reasoning tasks. Nonetheless, human cognition extends beyond language alone, enabling the remarkable capability to think in both words and images. Inspired by this mechanism, we propose a new reasoning paradigm, Multimodal Visualization-of-Thought (MVoT). It enables visual thinking in MLLMs by generating image visualizations of their reasoning traces. To ensure high-quality visualization, we introduce token discrepancy loss into autoregressive MLLMs. This innovation significantly improves both visual coherence and fidelity. We validate this approach through several dynamic spatial reasoning tasks. Experimental results reveal that MVoT demonstrates competitive performance across tasks. Moreover, it exhibits robust and reliable improvements in the most challenging scenarios where CoT fails. Ultimately, MVoT establishes new possibilities for complex reasoning tasks where visual thinking can effectively complement verbal reasoning.

Imagine While Reasoning in Space: Multimodal Visualization-o- Thhoughh
Chengzu 12
Wenshan Wu 2
Huanyu Zhang 23
Yan Xia
Shaoguang Mao Dong
Ivan Vulié" 1
Furu Wei?
Abstract
Imago
Direct Prompting
Chain-of-Though (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multi- modal Large Language Models (MLLMs). Yet, it struggles in complex spatial reasoning tasks. Nonetheless, human cognition extends beyond language alone, enabling the remarkable capa- bility to think in both words and images. In- spired by this mechanism, we propose new rea- soning paradigm, Multimodal Visualization-of Thought (MVoT). It enables visual thinking in MLLMs by generating image visualizations of their reasoning traces. To ensure high-quality vi- sualization, introduce token discrepancy loss into autoregressive MLLMs. This innovation sig- nificantly improves both visual coherence and fi- delity. We validate this approach through several dynamic spatial reasoning tasks. Experimental re- sults reveal that MVoT demonstrates competitive performance across tasks. Moreover, exhibits robust and reliable improvements the most chal- lenging scenarios where CoT fails. Ultimately, MVoT establishes new possibilities for complex reasoning tasks where visual thinking can effec- tively complement verbal reasoning.
Input
Output
Chain-of-Thougee
Input
Verbal Thought
Verbal Thought
Output
Multimodal Visulitattoon---Thouuu
Output
Visusl thoughht
Figure I. Multimodal Visualiztioon-ff-Touuus (MVoT) enables Multimodal Large Language Models to generate interleaved rea- soning traces across different modalities. While traditional CoT relies solely on verbal thought, MVoT facilitates visual thought to visualize the reasoning traces. This reasoning paradigm resembles human cognition to think in words and images seamlessly.
reasoning, logical deduction (Lam et al., 2024), and ad- vanced problem-solving capabilities (Zhang et al., 2024a). However, its performance deteriorates significantly when confronted with complex spatial reasoning tasks (Wang et al., 2024a; Li et al., 2024b; Ray et al., 2024).
Recent research efforts have extended CoT to multimodal models through two primary approaches. The first approach utilizes two-stage strategies that initially extract image in- formation through methods such as captioning (Zhang et al., 2024b), scene-graph generation (Mitra et al., 2024), bounding-box detection (Lei et al., 2024) before conducting reasoning. The second approach implements ReAct-style pipelines (Yao et al., 2023) that leverage external tools such as code interpreters or specialized vision models (Yang et al., 2023b; Hu et al., 2024; Zhou et al., 2024b), to obtain image observations from the environment. While these pipelines successfully handle both text and image input, they remain heavily dependent separate visual modules external toolsets. This dependency complicates their adaptation to advanced and more complex spatial reasoning tasks.
1. Introduction
Chain-of-Thhughe (CoT) prompting has substantially en- hanced the reasoning capacity of Large Language Models (LLMs) (Jiang et al., 2023; OpenAI, 2023; Dubey et al., 2024). By generating explicit reasoning traces, CoT enables the models to articulate their thought processes step-by-ster This advancement has enabled step-by-step mathematical
Equal contribution 'Language Technology Lab, University of Cambridge *Microsoft Research 'Institute of Automation, Chi- nese Academy of Sciences. Correspondence to: Chengzu Li <cc111 @ cak.ac.u>>.
Human cognition, however, transcends language (and text) alone, demonstrating the capacity to think in both words and images seamlessly. The dual-coding theory (Paivio,
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Multimodal Visualizationno-TTourg
1991) and working memory model (Baddeley, 1992) explain this phenomenon, positing that humans process information through both verbal and non-verbal channels. This dual processing capability is crucial for reasoning and guides decision-making. For instance, humans naturally manipu- late mental images to understand the physical world and conceptualize visual outcomes (Moulton & Kosslyn, 2009).
reasoning quality and model interpretability.
Specifically, in this work, we implement MVoT through fine- tuning an established autoregressive MLLM: Chameleon- 7B (Chameleon Team, 2024). To enhance visualization quality during reasoning, we introduce token discrepancy loss that bridges the gap between separately trained tok- enizers. We validate MVoT's effectiveness through con- trolled experiments across three dynamic spatial reasoning tasks. MAZE (Ivanitskiy et al., 2023) and MINIBEHAV- IOR (Jin et al., 2023) focus on interactions with spatial layouts. FROZENLAKE (Brockman, 2016) emphasizes fine- grained pattern representations in dynamic environments. Experimental results demonstrate that MVoT achieves com- petitive performance across tasks, outperforming traditional CoT by over 20% in challenging scenarios.
In the context of spatial reasoning, similar deficiency of LLMs and MLLMs, thinking in single text modality (termed verbal henceforth) has been observed. Based on verbal COT, the Visualization-of Thought (VoT) (Wu et al., 2024b) elicits the reasoning process by introducing textual 'visualizations' as mental images in spatial reasoning tasks. Put simply, VoT builds visualizations via simplified, text proxies. Besides this too simplistic representation of visu- alization, another significant limitation, which holds both for CoT as well as for VoT, is the dependence on purely textual representation of the reasoning paths. This reliance becomes problematic in complex multimodal tasks, where textual representations often fail to capture the intricate vi- sual patterns and spatial layouts of images (Hu et al., 2024). Users frequently find challenging interpret the reason- ing process without clear and intuitive visual illustrations that complement textual representation. Furthermore, it remains unclear whether MLLMs can engage in genuine reasoning within a visual space while thinking with textual utterances. Given the reasons above, we pose a key question: can MLLMs imagine in visual modality while reasoning?
The main contributions of this paper include:
We propose Multimodal Visualization-of of-Thhtght (MVoT), multimodal native reasoning paradigm that unifies text and vision within the reasoning traces. To our knowledge, it's the first to naturally generate visual thought as part of the reasoning process. It establishes new possibilities for complex tasks where visual think- ing effectively complements verbal reasoning.
We implement MVoT in Chameleon-7B and introduce token discrepancy loss in auto-regressive MLLM to bridge the gap between separately trained tokenizer. We conduct comprehensive experiments and ablation studies across three spatial reasoning tasks with newly collected datasets, demonstrating that MVoT exhibits superior adaptability and robustness compared to CoT in complex scenarios.
In parallel, recent research has expanded beyond multimodal understanding of visual inputs to include multimodal gen- eration, where foundation models can also produce outputs in the visual modality. This advancement has led to the development of sophisticated systems such as Chameleon (Chameleon Team, 2024), Transfusion (Zhou et al., 2024a), LatentLM (Sun et al., 2024b) and Janus-Pro (Chen et al., 2025). These multimodal- ficiency in both interpreting and producing high-quality outputs across textual and visual domains. The emerging capability of multimodal generation opens new possibilities for extending verbal reasoning to native visual thinking, en- abling the visualization of reasoning traces through images.
2. Related Work
Multimodal Chain-of-Thought Reasoning Chain-of- Thought (CoTT) (Wei et al., 2022) prompting has consider- ably enhanced the reasoning capacities of LLMs. To adapt CoT for multimodal models, recent research has explored various methodologies. Some investigations adopt two- stage approach, where image information is initially trans- formed and grounded into text (Zhang et al., 2024b), graph structure (e.g., scene graphs (Mitra et al., 2024) or knowl- edge graphs (Mondal et al.. 2024)), or bounding boxes (Lei et al., 2024) before reasoning. Other studies use ReAct-style pipelines (Yao et al., 2023) that integrate external tools to process and reason with image observations. These tools in- clude code interpreters and specialized vision models (Yang et al., 2023b; Hu et al., 2024; Zhou al., 2024b; Gao et al., 2024). Although these approaches effectively manage both textual and visual inputs, they rely heavily on separate visual modules or toolsets which limits the expressiveness of the methods. To address these limitations, we propose MVoT. a novel reasoning method designed to leverage multimodal-
models demonstrate pro-
Building upon these advancement, we propose Multi- modal ootaliization-oo-houggee (MVoT). It leverages multimodal-nan native architectures to transcend the text-form thinking into multimodal native reasoning through gener- ating image visualizations of their reasoning traces. This reasoning paradigm enables the model to 'think' in words and images in combination seamlessly, while avoiding the potential errors being introduced in captioning the images. By incorporating native visual thought during reasoning process, MVT offers more straightforward illustrations of the reasoning process and simultancously enhances both
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Multimodal Visualizaiinnof-Thoogg
native understanding and generative capabilities. MVoT enables the generation of interleaved reasoning traces across multiple modalities, providing an integrated and flexible ap- proach to multimodal reasoning with better interpretability and more robust reasoning quality.
resent a pre-trained MILM with parameters 0, a denote multimodal input sequence, and a language sequence of verbal thoughts and image sequence of visual thoughts.
In multi-hop spatial reasoning tasks with input œ, CoT prompting generates intermediate steps 21, "., 2m. where each & is sampled sequentially based on the inputs and pre- vious generated steps. The final output is concluded based on all prior steps. MVOT enhances this process by adding a image visualization o to each intermediate step 2;, then the subsequent step Zi+-1 is sampled conditioned on prior steps 81 ... 2, and visualizations U1 ... -;, as shown in Figure 1.
Multimodal Spatial Reasoning Multimodal spatial rea- soning involves understanding and reasoning about the spa- tial relationships among objects, their movements, and in- teractions with the environment (Chabris et al., 2006; Li et al., 2024a; Newcombe, 2024; Zhang et al., 2025). Despite advancements achieved with LLMs and MILMs, this re- mains a significant challenge and attracts growing research interest (Achiam et al., 2023; Yang al., 2024; Ramakr- ishnan et al., 2025). To systematically evaluate multimodal spatial reasoning capabilities, several benchmarks have re- cently been introduced, covering diverse perspectives and tasks (Liu et al., 2023a; Wang et al., 2024a; Li et al., 2024b; Ray et al., 2024). However, few research has touched base on the interplay between actions and changes in spatial layout (Li al., 2024b; Wu et al., 2024a), which requires dynamic imagination and tracking the states actions alter the environment. Various approaches have been proposed to tackle the challenges associated with spatial reasoning for MILMs and LLMs: Spatial (Chen et al., 2024) and SpatialRGPT (Cheng et al., 2024) improve multimodal spatial reasoning by leveraging 3D VQA or scene graph data for supervised finetuning. VoT (Wu et al., 2024b) proposes novel prompting approach by introducing textual imagery representation to facilitate dynamic reasoning for LLMs. Despite these efforts, existing methods fail to unlock the inherent reasoning capabilities within the multimodal-nati models to imagine the spatial dynamics. Our approach uni- fies text and vision within the reasoning traces and improve the interpretability and robustness by generating mental images aligned with spatial reasoning.
As defined in the Equation 1 and 2, it forms interleaved reasoning traces and image visualizations.
0g ~ Po(us 221, 22,01,-11 ... "0....,,000 00,-1, 24)
(1)
2,41 Pa(zi-1 I 21,,,,01, 01, 0z)
(2)
To empower MILMS with MVoT capa we train the model on multimodal inputs x and their correspond- ing output labels, which include multimodal rationales 21, U1 ... En, U/ and the final answer. This training strat- egy enables the model to learn interleave verbal reasoning steps and corresponding visual thoughts, enhancing its abil- ity to handle complex reasoning tasks that require thinking in multimodality.
3.2. Training with Autoregressive MLLMs
In this section, we focus on autoregressive MLLMs with dis- crete image tokens for both training and inference. However, as a reasoning paradigm, MVoT can be extended to other model architectures and modalities, provided they meet the requirements for interleaved multimodal generation.
Multimodal Sequence Modeling As shown in 3, we fol- low the architecture of Chameleon (Chameleon Team, 2024), which leverages unified Transformer to process both image and text tokens. The architecture integrates two tokenizers: an image tokenizer based on Esser et al. (2021) and a text tokenizer, which convert images and text into discrete token sequences, respectively. The image tokenizer uses a discrete codebook to encode input images into a sequence of im- age tokens, while the text tokenizer maps textual data into corresponding token sequences. These token sequences are concatenated and processed by a causal transformer model.
3. Multimodal Visualization-f--hooe
Humans often create mental images to inform decision- making. Rather than relying on verbal thoughts as text prox- ies to mimic these mental images, MVoT enables models to reason in multimodality by generating image visualizations as their visual thoughts. By combining thoughts in both modalities, this novel reasoning paradigm offers a more in- tuitive and effective way to elicit the multimodal reasoning process with enhanced expressiveness.
3.1. Formulation
Notation We denote MILM's codebook as C E TR where N is the number of codebook entries, and D is the dimensionality of codebook entries. Let tvis and Evis denote the visual codebook indices and embeddings. The predicted values are indicated with a hat symbol.
We formulate the process of MVoT as follows. Given a mul- timodal input sequence, the model is expected to generate interleaved multimodal thoughts as part of the reasoning process and ultimately produce a final answer. Let Po rep-
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Multimodal Visualization-o--Thorg
Input
Generated Response
Maze
Action Sequence:
Go left. Go left. Go up, left. ups
Go
Go left.
up
left.
Go
The answer is
MiniBehavior
Action Sequence:
Go right.
Caryinng: right.
Caryying None. Pick
Carrying printer. Go left.
Go right. right. Pick up. Go left. Go up. Drop.
Carrying printer. Go left.
Carrying: printer. Go up.
Carrynng: printer. Drop.
Carrying: None. Action Success.
Lake
Action Sequence:
Go down. down. Go right. Go right. Go up. Go left. Go
Gown
Go down.
Go right.
Action Failed: Fall into the Hole.
Figure 2. Illustrations of M reasoning process. Interleaved verbal thoughts and visual thoughts generated by MLLM seamlessly.
Image Tokens
Text Tokens
(MSE), as described in Equation 3, where larger distances indicate lower similarity.
Transformer
St.i MSS((evis) Evisi). MSE(eviss Evisx) (3)
The model predicts the probability distribution P(4) RIxN for the i-th image token over the image token vocab- ulary. Ln penalizes probabilities assigned to tokens that deviate significantly from their corresponding label tvis, in the visual embedding space. By aligning the visual embed- dings of predictions with those of the ground-truth tokens, Ln aims enhance the quality of the generated images.
Image Tokenizer
Text Tokenizer
Codebock Entiko
Grounddsuus
TEXT
Figure 3. MVoT training with token discrepancy loss Cp-
Token Discrepancy Loss While language modeling uni- fies both text tokens and image tokens within a single autore- gressive model, the discrepancy between these separately trained tokenizers
LD
Strrs P(t.)
degrade the quality of generated im- ages. To mitigate this issue, introduce token discrepancy loss into the autoregressive MLLM architecture, shown in Figure 3. This loss design bridges the gap between language modeling and visual embedding space while ensuring that gradients remain intact.
The causal Transformer model is fine-tuned us- ing the next-token prediction objective, while the image tokenizer and text tokenizer are kept frozen throughout the process. With token discrepancy loss En for image tokens, and the cross-entropy loss Lo for both text tokens and image tokens, the loss function is described follows.
Training
Token discrepancy loss Cp minimizes the discrepancy be- tween the predictions and labels in visual embedding space. Equation 3 and 4 illustrate the token discrepancy loss Lp- To capture relationship among image tokens, we first com- pute the similarity matrix S E B N The similarity be- tween Evis, and other image tokens is measured using their pairwise distances in the visual embedding space. Specifi- cally, the similarity determined by the mean squared error
L La LD
4. Spatial Reasoning Tasks
Motivated by Ray et al. (2024), we select three dynamic reasoning tasks in space that requires the model to dy- namically locate objects, understand how the environ- ment evolves, and predict outcomes when actions are ap-

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Multimodal Lopalization-o-Thhuglh
plied. To assess the MLLMS with controllability for anal- ysis, we ground these spatial reasoning tasks within grid- based environments: MAZE navigation (Ivanitskiy et al., 2023), Installing&Prrnter from MINIBEHAVIOR (Jin et al., 2023) and FROZENLAKE. simulation (Brockman, 2016; Wu et al., 2024a). MAZE navigates in abstract mazes and MINIBEHAVIOR includes the spatial properties of the ob- jects and interactions with the environmental layouts, while FROZENLAKE contains fine-grained pattern details instead of abstract symbols. The tasks encompass various levels of controlled complexity as the abstraction of the real world.
frozen lake layout with the start and goal position, the model has to determine the consequence of following given actions. Compared to MAZE and MINIBEIIAVIOR, FROZENLAKE contains more diverse image details and its environment is more complex considering the number of key entities such as holes.
5. Experiments
5.1. Experimental Setups
Data. We construct datasets for three spatial reasoning tasks, as described in Section 4, encompassing varying levels of complexity in patterns and action spaces. The dataset statis- tics are presented in Table 4. Detailed information on data collection is provided in App. B. We structure the data as interleaved text-image pair to train M as in Section 3.
4.1. MAZE
In maze navigation, the model is provided with an initial image describing the maze layout and the starting position of the agent in the maze. The agent should navigate through the maze and reach the destination. In our work, we define the MAZE task as follows. Given an initial maze with a starting point and sequence of actions, the model supposed to follow the actions and predict the final destination chosen from the locations labeled A, B, C or D in the maze.
Model and Experiments. We use Anole-7B (Chern et al., 2024) model as the backhone. Anole is tuned on Chameleon (Chameleon Team, 2024) and can generate interleaved text and image, making it well-suited for MVoT. We only tune part of the model's parameters with LoRA (Hu et al., 2021) in an instruction tuning manner (Liu et al., 2023b) on MI300X for 40 epochs, where only the loss from the pre- dictions is optimized. Additionally, we evaluate the perfor- mance of GPT--4o (OpenAI, 2024) with zero-shot inference, CoT and MVoT in the ReAct-style pipeline (Yang et al., 2023b). Detailed prompting templates and hyperparameters for each task and system variant are provided in App. C.
4.2. MINIBEHAVIOR
We select Ariineer from MINIBEHAVIOR (Jin et al., 2023) embodied AI tasks a representative sce- nario for our experiments. It requires the agent to first locate the printer on the floor, pick it up and carry it to the ta- ble to toggle it on. As shown in Figure 2, the printer is represented by a small printer symbol, the agent is repre- sented by a red triangle and the table is represented by a brown area. Aligned with previous settings, we define the MINIBEHAVIOR task in our work follows. Given a se- quence of actions and an environment layout in image, the model should predict the outcome of conducting the actions. The outcomes include whether the agent successfully ex- ecutes tasks, such as picking up the printer or placing it on the table, and whether objects are missing from the en- vironment. This task expands the action space of MAZE by introducing the interaction with the environment, while maintains similar level of perception difficulty with simple symbolic representations.
We compare the MVoT with the following families of system variants: 1) Direct Prompting (Direct): The model directly outputs the choice index without intermediate reasoning. 2) Chain-of-Thougeee (CoT): The model is instruction-tuned to reason step-by-steg incorporating coordinates and envi- ronment layout described in text, before concluding with the final answer. 3) Training with Interleaved Text-Image Pairs (Interleaved): This method follows the standard train- ing approach for MLLMs, interleaving text and image data. However, Interleaved calculates the loss only on text tokens while excluding image tokens. In contrast, MVTT computes the loss across all token types. The comparisons among these system variants are summarized in Table 1.
4.3. FROZENLAKE
We extract the predicted answer from model out- put by pattern matching with 'the answer is'. Accuracy for multiple-choice question answering is calculated by com- paring the predicted choice with the ground-truth answer.
Metrics
FROZENLAKE is initially proposed by Wu et al. (2024a) implemented with Gym (Brockman, 2016), which is similar to maze navigation but with more complex patterns and details. As shown in Figure 2, it simulates a grid-based frozen lake. The agent is supposed to start from the des- ignated position and reach the destination safely without falling into the "holes'. Based on the spatial reasoning task from Wu et al. (2024a), we define the FROZENLAKE task as follows. Given a sequence of actions and the grid-based
5.2. Experimental Results
MVOT outperforms Direct and GPT-40 with inter- pretability. Experimental results across all three simulation tasks reveal that Direct struggles with overfitting to spatial
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Multimodal Visualization-o-TThogr
Table Experimental results from different system variants across tasks, Coord denotes whether the system is instructed use textual coordinates during inference; Layout denotes whether the system first caption the environment with text for further inference; Image refers to whether the images of intermediate states are visible to the model during training. Best and second best performance are illustrated with corresponding colors. Methods with underlines are fine-tuned on our datasets. indicates worse performance than Direct baseline.
Training Variants
Task
Model
Method
Output
Coord
Layout
Image
MAZE
MINIBEHAVIOR
FROZENLAKE
Zero-Shot Direct
Text
0.7100
0.4576
0.4976
GPT-40
Zero-Shot CoT
Text
0.7386
0.4676
0.4664
With Visual Thought"
Text
0.8556
0.6440
0.8021
Direct
x
x
X
Text
0.7171
0.7250
0.7788
CoT
/
X
Text
0.9792
0.9812
0.6148
Anole 7B
w/o layout
X
X
Text
0.7023
0,6000 I
0.5974 1
Interleaved
x
V
Text
0.8678
0.8406
0.6460
M
x
Text, Image
0.9295
0.9514
0,8560
* Visual thought generated by Anole implemented with MVoT.
reasoning tasks, achieving an accuracy of approximately 70%. GPT-4o performs even worse, both with and without CoT prompting, underscoring the inherent difficulty of these reasoning tasks. In contrast, MVoT demonstrates consis- tent improvements. surpasses Direct by 7% on FROZEN- LAKE and achieves over 90% accuracy on both MAZE and MINIBEHAVIOR. Beyond its superior performance, MVoT also provides verbal and visual thoughts of intermediate rea- soning states. This feature enhances interpretability, offering a clearer and more intuitive understanding of its reasoning process compared to Direct.
6 x 6 grid, as detailed in Table 15 in the Appendix.
(2) CoT relies heavily on textual description of the environ- ment. CoT performs well on MAZE and EHATIRR when the environment layout is accurately described through text. Captioning the environment layout simplifies the task into textual reasoning process, climinating the need for vi- sual references. However, shown in Table 1, when reason- ing is performed using only the agent's 's coordinates without explicit textual descriptions of the environment, CoT con- sistently underperforms the Direct baseline. This reliance is particularly evident in FROZENLAKE, where flawed predic- tions stem from inaccurate environment descriptions. These limitations constrain the generalization and reliability of CoT, particularly in complex environments.
MVoT achieves comparable or better performance than CoT with enhanced robustness. CoT achieves over 95% accuracy the MAZE and BINIO by explicitly describing the environment layout and agent location with textual coordinates. However, it performs worse than Di- rect baseline on FROZENAKKE. In contrast, MVoT demon- strates comparable performance on MAZE (92.95%) and MINIBEHAVIOR (95, 14%), while achieving a higher accu- racy of 85.60% on FROZENAKKE compared both Direct and COT.. This demonstrates better stability and robustness of MVoT than COT.
Meanwhile, MVoT doesn't 't have the ineffectiveness above.
(1) MVoT is more robust to environment complexity com- pared to COT. MVoT maintains stable performance across varying grid sizes within each task, as shown in Table 15 in the Appendix. In FROZENLAKE, even as the environ- ment becomes more complex with larger grid sizes and increased number of 'holes', as shown in Table 7, MVoT consistently achieves over 83% task performance. In con- trast, CoT shows significant decline in performance as environmental complexity rises, highlighting the robustness of MVoT in handling more challenging scenarios.
Although CoT desmonstrates strong results on MAZE and MINIBEHAVIOR, it shows vulnerabilities and limitations: (I) CoT is sensitive to environment complexity. CoT under- performs on FROZENLAKE, where the environment is more complex due to the presence of additional key entities (e.g.. holes), compared to the other tasks. Error analysis reveals that 90.80% of CoT's mistakes FROZENLAKE stem from inaccurate coordinate descriptions of holes in the environ- ment. An illustration of this type of еттог is presented in Figure 9 in the Appendix. In contrast, no such errors are ob- served in MAZE and MINIBEHIVVIOR. Furthermore, CoT's performance on FROZENLAKE deteriorates as grid size in- creases, dropping from 0.9401 on 3 grid 0.3911 on
(2) MVT demonstrates better interpretability than CoT. Rather than solely relying on textual descriptions, MVoT elicits reasoning process by visualizations, effectively miti- gating potential emrrr introduced by inaccurate text-based captions for complex environments. Moreover, visual thought provides a more direct and interpretable way to track the reasoning process compared to aligning textual coordinates within images. This calls for the need of incor- porating multimodal reasoning, leveraging both textual and other modalities, such as vision, rather than reasoning solely
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Multimodal Houalization-of-Thuglh
Table Performance upper bounds achieved by combining predic- tions from CoT and MVoT across three tasks.
bound performance reaches nearly 100% accuracy on Maze and MINIBEHAVIOR, and 92% accuracy on FROZENLAKE, These findings suggest that MVoT complements CoT by offering an alternative reasoning strategy, enabling the en- semble of approaches to further enhance performance.
MAZE
MINIBEHAVIOR
FROZENLAKE
CoT
0.9792
0,9812
0.6148
MVoT
0.9295
0,9514
0.8560
Upperbound
0.9984
1.0000
0.9246
In summary, MVoT demonstrates its effectiveness in per- formance, better generalization than CoT in eliciting the reasoning state during spatial reasoning.
in text as the primary modality.
Learning from interleaved multimodal rationales for better and grounded reasoning. Data-wise, our findings suggest that incorporating interleaved training data, even without generating visualizations, improves reasoning per- formance. Method-wise, MVoT achieves higher and more consistent improvements across all tasks. Compared be- tween Direct and Interleaved, we observe that the inclu- sion of interleaved visualizations during training, despite not contributing directly to the loss, improves performance on MAZE and MINIBEHAVIOR by over 10%. This em- pirically indicates that these interleaved images helps the model in leveraging visual cues for reasoning during op- timization. However, despite the advantages of the Inter- leaved paradigm, we witness a drop in task performance on FROZENLAKE. We conjecture that this may be attributed to the complexity of the visual cues in FROZENLKKE for Interleaved. This highlights the unique challenges of the FROZENLAKE task and illustrates the limitations of the tra- ditional Interleaved training paradigm. In contrast, when interleaved multimodal rationales serve as supervision sig- nals for visual tokens as in MVOT, the model explicitly grounds its reasoning by generating visualizations, leading to improved performance across all tasks. These findings provide valuable insights into leveraging interleaved mul- timodal thoughts to enhance reasoning with MLLMs and emphasize the need for further research.
6. Discussions and Ablations on Visualization
During training, the model generates the next visual thought based on the previous golden image. On the other hand, MVOT recursively generate multimodal thoughts (texts and image visualizations) based on the previously generated thoughts, illustrating the difference between paradigms. We refer to these two approaches 'image editing' and 'MVoT' in the following discussion. Given that M operates through recursive generation, our focus in this section is primarily on discussing the visualization quality of MVoT.
Qualitative Analysis Figure 4 illustrate examples of cor- rect and incorrect generated images for FROZENLAKE, More generated visualization are shown in Figure 6 and 7 in Appendix D.2. We classify errors of visualization genera- tion into the following categories: (1) Wrong Visualization: The generated visualization is inaccurate. (2) Redundant Patterns: Unnecessary or irrelevant patterns are visualized outside the intended area for modification. Furthermore, in the FROZENLAKE task, we observe that generated image de- tails often blur pattern complexity increases compared to MAZE and MINIBEHAVIOR. Patterns, such as background details in FROZENI MVoT visualizations, often show minor inconsistencies between generated visual thoughts. Similar differences are also noted between original images and reconstructed images through tokenization and detok- enization. The variability frequently results in a loss or per- turbation of fine-grained details and highlights limitations in the expressiveness of MLLMs. These findings underscore the need for further research to improve the fidelity of image tokenization and generation in autoregressive MLLMs.
Equip Proprietary Models with MVOT In addition to use a single 7B open-sourced model to generate multimodal thought and conclude the answer, MVoT also provides flexi- bility of being used as plug-ins for other proprietary models including those accessible via APIs. We provide GPT-4o with the visual thoughts from fine-tuned MVoT model after GPT-4o generates the verbal thought. We witness an im- provement in performance by over 15% accuracy across all the tasks as shown in Table 1. We hope this work inspires further exploration and fosters advancements in multimodal agent reasoning with multimodal thoughts.
Quantitative Metries To evaluate the quality of generated visual rationales, we define automatic evaluation metrics based on the identified types of errors:
Visualization Accuracy (V-Acc.): Measures the accu- racy of visualizing the intended modifications in the grid corresponding to the next action.
MVoT Complements CoT in Reasoning. To investigate whether MVT and CoT share similar reasoning capabilities and fail on the same data, we combine their predictions and calculate the boued performance. In this setting, a data point is considered correct if either MVoT or CoT generates the correct prediction. Table shows the upper-
Visualization Pattern Redundancy (V-Red.): Assesses the presence of unintended visual patterns in regions outside the targeted area of modification.
Visualization Correctness Step (V-Steps): the aver-
age length of first k consecutive correct visualizations within an action sequence.
7

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Multimodal -aaaatiiooooooooogghe
Previous Image
Target Image
Visualization with Lp
Visualization without Lp
Image
Go down.
Correct Visualization
X Wrong Visualization
Blurred Details
Go left.
Correct Visualization
Redundant Visualization
Blurred Details
Figure 4. Qualitative analysis illustrating: 1) FROZENLAKE visualization quality by comparing systems trained with and without token discrepancy loss (Cn); and 2) differences reconstructed images using the image detokenizer tokenized inputs.
Table 3. Quantitative metries for MVoT visual thoughts with without token discrepancy loss Cp. Best results are highlighted in bold. Metrics with f indicate that higher values mean better performance and vice versa.
Visualization Quality
Task Performance
V-Ace.f
V-Red.j
V-Steps
V-Ratio
Accuracyf
MAZE
MVoT with CD
0.9339
0.1010
8.4439
0.9449
0.9295
MVOT without Lp
0.6391
0.4931
5.6563
0.6930
0.7468
MINIBEHAVIOR
MV6T with Co
0.9681
0.0419
6.7532
0.9618
0.9514
MVoT without Co
0,7939
0.2633
5.2333
0.7793
0.7228
Visualization Correctness Ratio (V-Ratio): the average proportion of first consecutive correct visualizations across the action sequence.
the last column of Table 3, emphasizing the critical role of high-quality visualizations for better task outcomes. We also witness performance drop FROZENLAKE without token discrepancy loss from 0.8560 to 0. 0.7260. Empirical evidence suggests that incorporating visual embedding with Lp in addition to Le helps to bridge the gap between embeddings and improve the visual generation quality. This aligns with the findings by Tschannen et al. (2024) which noted similar limitations in embedding alignment for MLLMMs.
Due to the complexity of FROZENLAKE'S image details, which makes automatic evaluation challenging, we only report quantitative results of visualizing the agent position in MAZE and MINIBEHAVIOR.
Token discrepancy loss enhances accuracy and reduces redundancy in visualizations. Table 3 shows that MVoT, enhanced with token discrepancy loss (Cp), produces highly accurate visualizations with minimal pattern redundancy. Even in recursive generation scenarios, MVoT with Lo achieves an average of 95% correct and consecutive visual- izations during reasoning. In contrast, the absence of Co significantly degrades the generation quality: without LD. MVoT frequently generates redundant patterns and fails to accurately capture state transitions. These results align with findings from the image-editing scenario, as illustrated in Figure 10, which tracks quantitative metrics for MAZE at various epochs. Furthermore, poor visualization qual- ity negatively impacts task performance, highlighted in
7. Conclusion
We introduced Multimodal Visualization of Thought (MVoT). a novel reasoning framework that clicit reason- ing process with multimodal thoughts using multimodal native generative models. MVoT outperformed textual rea- soning baselines across variety of tasks, meanwhile demon- strating better robustness to state complexity and offering enhanced interpretability. To ensure the generation of high- quality visualizations, we proposed the token discrepancy loss, addressing embedding misalignment in autoregressive MLLMs. This helps to alleviate the issues of redundant
8

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Multimodal Visualizationof-Thouue
patterns and inaccurate visual thought generation, leading to better task performance with MVOT. Furthermore, the complementary strengths of MVoT and Chain-of-Thought (CoT), as evidenced by their combined upper-bound per- formance, highlight the promise of hybrid multimodal rea- soning approaches. This work underscores the value of multimodal cues and paves the way for future research into reasoning thoughts of hybrid modalities in complex tasks.
styles: Validation studies in 3800 individuals. Group brain technical report, 2:1-20, 2006.
Chameleon Team. Chameleon: Mixed-modee early-fusion foundation models. arXiv preprint arXiv:2405.099 200.40000088888 /8, 2024.
Chen, B., Xu, Z., Kirmani, S., Ichter, B., Sadigh, D., Guibas, L., and Xia, F. SpatialvIm: Endowing vision-language models with spatial reasoning capabilities. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14455-14465, 2024.
Impact Statement
This paper aims to advance the field of multimodal reasoning and machine learning. While our work has potential societal implications, the specific datasets used this study not present any immediate concerns of this work that require explicit attention here.
Chen, X., Wu, Z., Liu, X., Pan, Z., Liu, W., Xie, Z., Yu, X., and Ruan, C. Janus-pro: Unified multimodal un- derstanding and generation with data and model scaling, 2025.
Cheng, A.-C., Yin, H., Fu, Y., Guo, Q. Yang, R., Kautz, J., Wang, X., and Liu, S. Spatialrgpt: Grounded spa- tial reasoning in vision language model. arXiv preprint arXiv:2406.0158. 2024.
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Multimodal Visualization-of-Touur
A. Additional Related Work
In addition to Section 2, we provide additional related work for the comprehensive understanding of this work.
Video Generation World Model World models aim to predict the future for better understanding of the world, while video generation world models focus on the generation of videos to simulate and comprehend real-world dynamics (Zhu al., 2024). Recent developments in generative models have markedly enhanced the capacity to capture physics and motion (Blattmann et al., 2023a;b; Esser et al., 2023; Zeng et al., 2024). The Sora model (Brooks et al., 2024) has demonstrated exceptional capabilities in this domain through its advanced generative techniques. World models extend beyond visual synthesis into numerous fields. In robotics, these models facilitate the simulation of physical interactions for diverse tasks (Zhen et al., 2024; Liu et al., 2024). They contribute to autonomous driving by predicting environmental changes (Wang et al., 2023; 2024b). Additionally, they function as effective simulators for data collection purposes (Yang et al., 2023a). Previous work typically generates outputs a single modality. In contrast, our proposed MVT introduces novel reasoning method supporting multi-modal generation. This enhanced capability enables superior understanding, reasoning, and simulation of complex scenarios.
B. Data
B.1. Dataset Collection
MAZE dataset is generated using the maze-datase framework (Ivanitskiy et al., 2023), leveraging an iterative depth- -irst search algorithm. Mazes of sizes 3, 4, 5, and 6 are created with varying random seeds. Navigation paths are generated, and we remove repetitive paths to prevent knowledge leakage between training and development sets. For each maze, potential destination candidates (three random coordinates) are selected alongside the true destination.
MINIBEHAVIOR dataset originates from the INSTALLING PRINTER simulation environment, where reinforcement learning (RL) agents are trained on grid environments of sizes 7 to 10 using stabbl-bbseell sequences that successfully complete the task are retained, ensuring no overlap with previously seen environments. For cases involving repeated action sequences or previously seen environments, the simulator layout is modified with specified probabilities: 40% chance of altering the printer or table coordinates respectively, 20% chance of randomly removing either the printer or table. The modified environments are re-evaluated using the previous action sequences to validate the outcomes.
(Raffin et al., 2021). Action
FROZENLAKE dataset based on Gym environments (Brockman, 2016) and scripts from Wu et al. (2024a). Trajectories collected from RL agents selecting actions with maximum expected rewards using Q-table. Successful action sequences are retained if they are not seen in this environment, For unsuccessful sequences, cases where the agent falls into hole have a 50% probability of being saved as-is or 50% with appended random actions. If the agent neither falls into hole nor reaches the destination, the sequence is saved in accordance to Section 4. To increase the variance of the action sequences, we also randomly sample the paths when learning the Q-table following similar practice as above.
B.2. Dataset Statistics
The statistics of the datasets are illustrated in Table 4. We also provide additional details about the datasets used in this work.
MAZE
Entities: The starting point is marked with red dot, with potential destination candidates labeled as A, B, C. and D.
Actions: Go up. down, left, or right. All action sequences are valid, ensuring collisions with walls and that every
action is executable.
Visual Pattern: Abstract sketch illustrating the maze layout and navigation path. The agent's path is visualized incrementally with red arrows, preserving all previous movements.
MINIBEHAVIOR
Entities:
The agent's current location represented by a red triangle, with a printer symbol and a brown-colored table.
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Multimodal Visualization-f--Thovee
Table 4. Statistics of the collected datasets, covering varying levels of complexity in actions and patterns.
Task
MAZE
MINIBEHAVIOR
FROZENL
Grid Sizes
3-6
5-8
3-6
Entity Types
Entities Numbers
5
7.16
Action Length
9.11
7.83
6.56
Action Types
Pattern Details
X
/
Train Set Size
5007
6400
6846
Test Set Size
1255
1604
1664
Table 5.
Dataset statistical distributions of options.
D
Total
Train
1187
1269
1305
1246
5007
MAZE
Dev
323
326
293
313
1255
Train
3321
1092
1456
531
6400
MINIBEHAVIOR
Dev
834
297
349
124
1604
Train 3043
2377
1426
6846
FROZENLAKE
Dev
735
580
349
1664
Go up, down, left, or right; pick up; drop; toggle. The pick up action removes the printer from the map if next to the agent, while the drop action places the printer on the table if the agent is carrying it and next to the table. Visual Pattern: Abstract sketch of the environment layout. Unlike MAZE, only the agent's 's current position is visualized on the map, and whether the agent carries the printer described in text.
Actions:
FROZENLAKE
Entities:
The agent's current location depicted as an elf wearing green hat, gift and multiple holes on the frozen
lake.
Actions:
Go up, down, left, or right.
Visual Pattern: Comic-style illustrations with detailed depictions of the elf, background, and holes. Similar to MINIBEHAVIOR, the visualization is not incremental, focusing only on the current state.
Table 5 illustrates the distribution of correct choices of the train and dev split for each task. The distribution of different
grid sizes in train and dev split for each task is listed in Table 6. Specifically, Table shows how environmental complexity evolves with larger grid sizes in FROZENLAKE.
C. Experiments
C.1. Hyper-Parameters
Table 8 and show the hyper-parameters for training MVoT and doing inference with GPT-4o.
All models were trained on MI300X GPUs. Table provides the details of GPU configurations and hyperparameters for various experimental settings. For GPT-4o, we utilized the 2024-07-01 version hosted the Azure platform, with inference parameters outlined in Table 9.
During M training, as visual thoughts are recursively generated during inference, we applied input augmentation to improve visualization robustness and mitigate noise introduced by image reconstruction during tokenization and detokeniza- tion, as illustrated in Figure This augmentation applies tokenization and detokemization over the input image for multiple times, with the iteration count randomly determined between and 10.
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Multimodal Sxuullizatonn-ofTTooree
Table 6. Dataset statistical distributions of grid sizes.
MAZE
Grid Size
3
4
5
Train
1481
1722
1823
1820
Dev
334
418
462
450
MINIBEHAVIOR
Grid Size
8
Train
1600
1600
1600
1600
Dev
401
401
401
401
FROZENLAKE
Grid Size
3
4
5
Train
308
1080
1397
2222
Dev
78
271
350
556
Table 7. Average number of key entities in FROZENLAKE with different grid sizes.
Grid Size
4
6
Train
4.7097
5.7166
7.4723
9.5049
Dev
4.6737
5.4689
7.4589
10.2267
Total
4.7030
5.6682
7.4696
9.648
Table 8. Hyper-parameters of fine-tune fine-tuning Anole 7B for different system variants.
Direct
CoT
Interleaved
MVoT
Random Seed
42
42
Epoccs
40
40
40
40
Learning Rate
0.0002
0.0002
0.0002
0.0002
Train Batch Size
4
4
Val Batch Size
16
16
8
Grad Accumulation
4
2
GPUs
8
32
32
Table 9. Hyper-parameters for GPT-4o
Temperature
Max Tokens
Top P
Frequency Penalty
Presence Penalty
Stop
GPT-4o
800
None
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Multimodal V'sualization-ff-Thuug
C.2. Prompting Templates
Table 10, 11, 12 shows examples of prompting templates and responses for each tasks with different system variants, Table 13 illustrate the prompting template for GPT-4o with MAZE and Table 14 shows an example for GPT-4o with MVoT on MINIBEHAVIOR. The other two tasks follow similar patterns.
C.3. Reproducibility
We will release the code and the datasets at URL- URL-ANOOMOOUS upon acceptance for reproducibility purposes.
D. Results
D.1. Task Performance
Table 15 presents the detailed task performance metrics across varying grid sizes. For FROZENLAKE, we observe a noticeable decline performance for both CoT and GPT-4o as the grid size increases, reflecting the growing complexity of the environment. This drop in performance highlights the limitations of these models in handling more intricate spatial reasoning tasks as the state complexity expands.
In contrast, MVoT demonstrates consistent performance across all grid sizes and tasks, underscoring its robustness to environmental complexity. Unlike CoT--based approaches, which struggle to generalize effectively in larger and more complex settings, MVoT'ss ability to integrate verbal and visual thoughts allows it to maintain stability and accuracy. The underlying intuition is that, even in more complex environments, MVoT focuses solely on the intended areas of the image without modifying irrelevant parts, thereby maintaining a consistent level of reasoning difficulty for the model. These results emphasize MVoT's resilience and adaptability, making a better choice for tasks involving complex reasoning in dynamic environments.
D.2. Visualizations
Table 16 presents fine-grained visualization metrics for MAZE and MINIBEHAVIOR across varying grid sizes. The consistent performance across grid sizes underscores the robustness of the visualizations. Compared to MVoT models trained without token discrepancy loss, those incorporating token discrepancy loss (CD) achieve better visualization accuracy and reduced redundancy, demonstrating the effectiveness of Ln in enhancing visualization quality.
Figures 6 and 7 provide examples of visualizations generated for MAZE and MINIBEHAAVIOR. These examples clearly illustrate the improvement in visualization quality achieved by introducing token discrepancy loss.
Figure 10 illustrates the quantitative metrics for MAZE at various epochs in image-editing setting. With token discrepancy loss, the visualizations are more accurate and less redundant in the end, while converging faster well.
Image Tokenization in Autoregressive MILMSs To understand why token dis- crepancy loss LD helps to improve the quality of generated visual thoughts, we examine the two sets of embeddings introduced in autoregressive MLLMs: token embeddings for language modeling and visual embeddings for image tokenization. These embeddings originate from distinct systems because the visual codebook, image tokenizer, and detokenizer are trained separately from the autoregressive de- coder, which creates a potential discrepancy between the two embedding spaces. To investigate their alignment, we compare the two embedding sets from Chameleon (Chameleon Team, 2024), LVM (Bai et al., 2024) and LlamaGen (Sun et al., 2024a) by calculating the average overlap ratio of the top-k similar tokens based on cosine similarity. Results in Figure reveal that, on average, only one token overlaps among the top 10 similar tokens, and approximately 20% of tokens overlap among the top 50 similar tokens. To further illustrate this discrepancy, we replace the image tokens with their most similar tokens in the token embeddings and visual embeddings, reconstructing the images (Figure 8 in Appendix D.2). After 10 iterations of tokenization and detokenization, the reconstructed images based on token embeddings exhibit significant differences from the originals, such as altered colors and distorted symbols (e.g., the
(image Wcbb=e8921 Lmmape
Uamaa (Image
6-10
K-20
K-50
Figure 5. Average percentage of top-k overlapping tokens.
16

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Multimodal Visualization-of-Thuee Thouggh
Table I0. Example of input and output for Direct reasoning. Italic text is the expected response.
Direct
MAZE
Task: Maze Navigation Simulation
Determine the final destination (A, B, or D) from the starting point (red point) following the action sequence. The definitions of the actions are as below.
Go up/left/doww/rrihht move one grid space in the absolute up/left/down/right direction.
Full Action Sequence: Go down. Go left. Go left. Go up. Go up.
Initial maze: <image>>
Response: The answer
MINIBEHAVIOR
Task: Mini-Behavior Installing the Printer
Determine whether the agent (red triangle) can pick up the printer (printer symbol) on the floor and place it on the table (brown area) and toggle it on. If not, identify the failure reason. The definitions of the actions are as below. Go up/left/down/riihtt move one grid space in the absolute up/left/down/right direction.
& Pick up: pick up the printer from the any of the grid next to the agent. If there is no printer next to the agent, the action fails.
Drop: drop the printer to the table that is next to the agent. If there is table next the agent, the action fails.
Toggle: toggle the printer that is the table and next to the agent.
Return A, B, C or D.
Full Action Sequence: Go up. Go up. Go right. Go down. Go down. Pick up. Go right. Drop. Toggle.
A. Action Success.
B. Action Failed: Drop Error.
C. Action Failed: Pick Up Error.
D. Missing Key Objects.
Initial State: <image>
Response: The answer is A.
FROZENLAKE
Task: FrozenLake
Determine whether the agent (elf character) can safely reach the gift following the action sequence without falling into the holes. If not, identify the failure reason. The definitions of the actions below.
Go up/leit/downrright move one grid space in the absolute up/left/ddon/rriht direction. Return A, B or C.
Full Action Sequence: Go right. Go up.
A. Action Success.
B. Action Failed: Fall into the Hole.
C. Action Failed: Agent Safe but Fail Reach Destination.
Initial State: <image>
Response: The answer is A.
17

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Multimodal Visualization-o-Thourg
Table Example input and output CoT with environment layout. Italic text expected response.
CoT with Environment Layout
MAZE
Task: Maze Navigation Simulation
Determine the final destination (A, B, or D) from the starting point (red point) following the action sequence. The definitions of the actions are as below.
e Go up/lett/doww/rrght move grid space in the absolute up/left//dow//right direction.
Full Action Sequence: Go down. Go left. Go left. Go up. Go up.
Initial maze: <image>
Response: Destination Coordinates: A Coordinate: I0, OJ. B Coordinate: [2, 0}. C Coordinate: [0, 1J. D Coordinate: [1, 11. Initial Agent Coordinate: [2, 1]. Go down. Agent Coordinate: (2, 2/. Go left. Agent Coordinate:
[1, 2J. Go left. Agent Coordinate: [0, 2). Go up. Agent Coorlinate: [0, 1]. Go up. Agent Coonlinate: [0, 0J. Action sequence finished. The answer A.
MINIBEHAVIOR
Task: Mini-Behavioe Installing the Printer
Determine whether the agent (red triangle) can pick up the printer (printer symbol) on the floor and place it on the table (brown area) and toggle it on. If not, identify the failure reason. The definitions of the actions are as below.
* Go up/leit//oow//iihhtt move one grid space in the absolute up/lcft/dowm/right direction.
@ Pick up: pick up the printer from the any of the grid next to the agent. If there is no printer next to the agent, the action fails.
Drop: drop the printer to the table that is next to the agent. If there is table next the agent, the action fails.
Toggle: toggle the printer that is the table and next to the agent.
Return A, B, C or D.
Full Action Sequence: Go up. Go up. Go right. Go down. Go down. Pick up. Go right. Drop. Toggle.
A. Action Success.
B. Action Failed: Drop Error.
C. Action Failed: Pick Up Error.
D. Missing Key Objects.
Initial State: <image><Crryinn objects: None.
Response: Initial Agent Coordinate: [2. 7J. Initial Environment Layout: Printer Initial Coordinate: [3, 8]. Table
Coordinates: [[5, 61, [5. 7], [6, 6]. [6, 71, [7, 6). [7, 71]. Go up. Agent Coordinate: [2, 6J. Carrying objects: None.
Go up. Agent Coordinate: [2, 5]. Carrying objects: None. Go right. Agent Coordinate: [3, 5J. Carrying objects:
None. Go down. Agent Coorlinate: [3, 6]. Carrying objects: None. down. Agent Coordinate: [3, 7J. Carrying
objects: None. Pick up. Agent Coordinate: [3, 7]. Carrying objects: printer O. Go right. Agent Coordinate: [4. 7].
Carrying objects: printer O. Drop. Agent Coordinate: [4, 71. Carrying objects: None. Toggle. Agent Coordinate:
[4, 7]. Carrying objects: None. Action sequence stopped. The answer is A.
FROZENLAKE
Task: FrozenLake
Determine whether the agent (elf character) can safely reach the gift following the action sequence without falling into the holes. If not, identify the failure reason. The definitions of the actions are as below.
& Go up/left/down/right move one grid space in the absolute up/left/down/right direction. Return A, B or C.
Full Action Sequence: Go right. Go up.
A. Action Success.
B. Action Failed: Fall into the Hole.
C. Action Failed: Agent Safe but Fail to Reach Destination.
Initial State: <image>
Response: Initial Agent Coordinate: [1, 2]. Initial Environment Layout: Holes Coordinate: 1[I, 0J, 10, 1]]. Gift
Coordinate: [2, 1]. Go right. Agent Coordinate: [2, 2]. Go up. Agent Coordinate: [2. 1]. Action sequence stopped.
The answer is A.
18

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Multimodal Visualization-of-Thore
Table 12. Example of imput and output for CoT without environment layout. Itaiic text is the expected response.
CoT without Environment Layout
MAZE
Task: Maze Navigation Simulation
Determine the final destination (A, B. or D) from the starting point (red point) following the action sequence. The definitions of the actions are as below.
Go up/left/down/right move one grid space in the absolute up/leit/downrright direction.
Full Action Sequence: Go down. Go left. Go left. Go up. Go up.
Initial maze: <image?>
Response: Initial Agent Coordinate: [2, I]. Go down. Agent Coordinate: [2, 2]. left. Agent Coordinate: [1, 2].
Go left. Agent Coordinate: [0, 2]. Go up. Agent Coordinate: [0, 1]. Go up. Agent Coordinate: [0, 0J. Action sequence finished. The answer is A.
MINIBEHAVIOR
Task: Mini-Behavior Installing the Printer
Determine whether the agent (red triangle) can pick up the printer (printer symbol) on the floor and place it on the table (brown area) and toggle it on. If not, identify the failure reason. The definitions of the actions are as below.
* Go up//eft/doow/rrihht move one grid space in the absolute up/left/dowm/right direction.
Pick up: pick up the printer from the any of the grid next to the agent. If there is printer next to the agent, the action fails.
Drop: drop the printer to the table that is next to the agent. If there is no table next to the agent, the action fails.
Toggle: toggle the printer that is on the table and next to the agent.
Return A, B, C or D.
Full Action Sequence: Go up. Go up. Go right. Go down. Go down. Pick up. Go right. Drop. Toggle.
A. Action Success.
B. Action Failed: Drop Error.
C. Action Failed: Pick Up Error.
D. Missing Key Objects.
Initial State: Himage>Carryinn objects: None.
Response: Initial Agent Coordinate: [2, 7J. Go up. Agent Coordinate: [2, 6J. Carrying objects: None. Go up.
Agent Coordinate: [2, 5J. Carrying objects: None. Go right. Agent Coordinate: [3, 5J. Carrying objects: None.
down. Agent Coordinate: [3, 6]. Carrying objects: None. Go down. Agent Coordinate: [3, 7J. Carrying objects:
None. Pick up. Agent Coordinate: [3, 7]. Carrying objects: printer Go right. Agent Coordinate: [4, 7J. Carrying objects: printer-O. -0. Drop. Agent Coordinate: [4, 7]. Carrying objects: None. Toggle. Agent Coordinate: [4, 7].
Carrying objects: None. Action sequence stopped. The answer is A.
FROZENLAKE
Task: FrozenLake
Determine whether the agent (elf character) can safely reach the gift following the action sequence without falling into the holes. If not, identify the failure reason. The definitions of the actions below.
Go up/left/down/right move one grid space in the absolute up/left/down/right direction.
Return A. B or C.
Full Action Sequence: Go right. Go up.
A. Action Success.
B. Action Failed: Fall into the Hole.
C. Action Failed: Agent Safe but Fail to Reach Destination.
Initial State: <image>
Response: Initial Agent Coordinate: [1, 2]. Go right. Agent Coordinate: [2, 2). Go up. Agent Coordinate: [2, 11.
Action sequence stopped. The answer is A.
19

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Multimodal Visualization-o--Toggg
Table 13. Prompting template for GPT-4o zero-shot direct and CoT inference, with MAZE, example.
GPTT-4o on MAZE
GPT-4o zero-shot inference
Task:
Maze Navigation Simulation
Determine the final destination (A, B, or D) from the starting point (red point) following the action sequence. The definitions of the actions are as below.
& Go up/left/doww/right move one grid space in the absolute up/lcit/down/rriht direction.
Full Action Sequence: Go down. Go left. Go left. Go up. Go up.
Initial maze: <image>>
Conclude your final answer between <ANSWER>and </ANSWER
GPT-40 zero-shot CoT
Task:
Maze Navigation Simulation
Determine the final destination (A, B. or D) from the starting point (red point) following the action sequence. The definitions of the actions are as below.
Go up/lett/down/rrihht move one grid space in the absolute up/left/down/right direction.
Full Action Sequence: Go down. Go left. Go left. Go up. Go up.
Initial maze: <image>>
Let's think step by step. Conclude your final answer between ANSWER </ANSWER
elf's appearance). This contrast highlights the misalignment between the two embedding systems. By introducing token discrepancy loss, we hope to bridge this gap, enabling better alignment between visual and token embeddings, thus allowing the model to generate higher-quality visual rationale.
Figure provides an example where CoT fails on FROZENLAKE while MVoT succeeds. This example clearly and intuitively demonstrates that CoT is highly sensitive to environmental complexity and generates incorrect coordinate descriptions of holes when dealing with FROZENLAKE. In contrast, MVoT successfully avoids this issue by leveraging the visualization of thought during reasoning.
E. Limitation
MVoT unifies the verbal and visual thoughts to elicit the reasoning process through image visualizations. However, we observe that the generated visualizations often attempt reconstruct iask-inrelevant details, such as the background patterns in FROZENLAKF, while overlooking the intended alterations in the visualizations. This can be mitigated by incorporating the guidance techniques in image generation with diffusion models (Sadat et al., 2024), which propose a direction for future improvement. Additionally, explicitly generating visualizations introduces computational overhead during inference. To address this, we advocate for further research into compact image representations using fewer tokens (Choudhury et al., 2024; Yu et al., 2024), therehy reducing the computational cost of visualization generation.
20

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Multimodal V'sualization-o-Thhugghh
Table 14. Prompting template for GPT-4o with MVoT, with MINIBEHAVIOR as example. <VISUALIZATIONN> is generated by MVoT the plug-in. in. Italic text the response generated by GPT-4o.
GPT--4o with MVoT on MINIBEHAVIOR
To predict the consequence of a sequence of actions, you need simulate the action sequence with 'one action' at time. Each time after simulating one action, 'your turn finished' and you should wait for the environment to provide the corresponding image observation.
When all the actions are completed or target situation is achieved, conclude the final answer. The interaction between you and the environment at step is as following: Your turn:
First conduct reasoning determine to simulate 'action' or to conclude the 'answer' based on the previous observation 'Observation {i-1}'.
Reasoning {i}: {your thinking before simulating 'action' or concluding 'answer' if all actions completed target situation aligned with one of the options is achieved. }
If to simulate the next action, return as follows. Reasoning {i}: {your thinking before simulating 'action based on the full action sequence]
Action {i}: {i-th action to simulate) # from the full action sequence
If to conclude the answer based the observation that aligns with of the options, return follows. Reasoning {i}: {answer can be concluded or actions are completed based on the observation i-1. Answer: The answer is {Choose from multiple-choice options}
In reasoning, don't describe the image, only determine whether to conclude the answer if previous obser- vation aligns with one of the options (pay attention to the key objects) or to simulate the next action.
Don't mention misleading word such safe or anything will cause hurt.
Task: Mini-Behavior Installing the Printer
Determine whether the agent (red triangle) can pick up the printer (printer symbol) on the floor and place it on the table (brown area) and toggle it on. If not, identify the failure reason. The definitions of the actions are as below.
* Go up/left/ddow/rrihht move one grid space in the absolute up/left/dowm/right direction.
& Pick up: pick up the printer from the any of the grid next to the agent. If there is no printer next to the agent, the action fails.
Drop: drop the printer to the table that is next to the agent. If there is table next the agent, the action fails.
Toggle: toggle the printer that is the table and next to the agent.
Return A, B, C or D.
Full Action Sequence: Go right. Pick up. Go up. Drop. Toggle.
A. Action Success.
B. Action Failed: Drop Error.
C. Action Failed: Pick Up Error.
D. Missing Key Objects.
<image>
Conduct reasoning first to determine whether 1) to simulate the action sequence with 'one step' at time in the format of 'Step'/ /CCooccuuiion' 'Reasoning' and 'Action', or 2) to onclude the answer when 'previous observation aligns with one of the options' or 'all the actions are completed'. Do not generate the observation by yourself.
The agent (red triangle) is currently positioned next to the printer. The first action in the sequence is move right. will simulate this action see if the agent can move closer to the printer.
Reasoning 1:
Action 1: right.
Observation 1: <VISUALIZATION>
21

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Multimodal Visualization-of-Thorr
Table 15. Fine-grained task performance different grid sizes
MAZE
Grid size
5
6
Overall
GPT-4o Direct
0.6667
0.7196
0.7457
0.6888
0.7100
GPT-4o CoT
0.9103
0.7639
0.7457
0.6978
0.7386
Direct
0.5385
0.7528
0.7457
0.7067
0.7171
CoT (w env layout)
1.0000
0.9926
0.9800
0.9690
0.9792
Interleaved
0.9487
0.9188
0.8857
0,8197
0.8677
MVoT
0.9103
0.9668
0.9257
0.9162
0.9295
MINIBEHAVIOR
Grid size
7
8
10
Overall
GPT-4o Direct
0.3965
0.4389
0.4838
0,5112
0,4576
GPT-4o CoT
0.4663
0.4439
0.4788
0.4813
0.4676
Direct
0.6733
0.7207
0.7758
0.7307
0.7250
CoT (w env layout)
0.9800
0.9825
0.9723
0.9900
0.9813
Interleaved
0.8279
0.8329
0.8615
0.8404
0.8406
MVoT
0.9651
0.9676
0.9528
0.9202
0.9514
FROZENLAKE
Grid size
3
5
6
Overall
GPT-4o Direct
0.6916
0.5215
0,4372
0.3933
0,4976
GPT-4o CoT
0.6018
0.5000
0.4113
0.3911
0.4664
Direct
0.8263
0.8038
0.7468
0.7533
0.7788
CoT (w env layout)
0.9401
0.7225
0.5000
0.3911
0.6148
Interleaved
0.7305
0.6818
0.6017
0.5956
0.6460
MVoT
0.8623
0.8397
0.8377
0.8876
0,8560
Table 16. Fine-grained task performance and visualization metrics on different grid sizes.
MAZE
5
MNoT Cp
MVoT Lp
MVoT LD
MVoT w/o Lp
MVoT w/ Lo
MV6T w/o
MVoT w/ ED
MVoT w/o
0.9103
0.7949
0.9668
0.7970
0.9257
0.7031
V-Ace.
0.9654
0,8447
0.9719
0.7858
0.9549
0.6786
0.9094
0.5609
0.0769
0.1923
0.0553
0,3284
0.0829
0.4200
0.1275
V-Steps
4.6538
4.0513
6.8930
5.4576
7.9771
5.5943
10.0346
6.0219
V-Ratio
0.9489
0.8314
0.9677
0.7897
0.9545
0.7195
0.9264
MINIBEHAVIOR
7
8
9
10
MNoT w/ Co
MVoT w/o Lu
MVoT w/ LD
MVoT w/o Lo
MVOT w/ Co
MVoT w/o Lo
MVoT w/ Co
MVoT w/o Co
Task Ace
0.9651
0.7606
0.9676
0.7980
0.9528
0.6850
0,9202
0.6425
V-Ace.
0.9781
0.7960
0.9694
0.8525
0.9791
0.7928
0.9479
0.7497
V-Red.
0.0349
0.2668
0.0399
0.1696
0.0367
0.2651
0.0898
0.3290
V-Steps
5.6708
4.3666
6.1
5.0374
7.6640
5.6089
8.6259
5.9663
V-Ratio
0.9693
0.7791
0.9608
0.8350
0.9786
0.7752
0.9313
0.7258
22

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Multimodal Shualization-o-Thhuuht
Previous Image
Action
Target Image
Visualization with Lp
Visualization without Lp
Perturbed Details
left
Blurred Patterns
X Wrong Visualization
Go down.
Redundant Visualization
B
X
Wrong Visualization
up.
Redundant Visualization
Go down.
X Redundant Visualization
B
C
A
up.
Redundant Visualization
Figure 6. Qualitative analysis of MAZE visualization quality from systems trained with and without token discrepancy loss (Cn) in image-editing setting.
23

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Multimodal Visualiztiionof-Thopgg
Previous Image
Action
Target Image
Visualization with hp
Visualization without Lp
Go down.
Perturbed Details
X Wrong Visualization Pick up action failed.
Pick up.
X
Wrong Visualizatiion
Go down.
Redundant Visualization
Go right.
X Redundant Visualization
Go left.
X Redundant Visualization
Figure Qualitative analysis of MINIBEHAVIOR visualization quality from systems trained with and without token discrepancy loss (Cp) in image-editing setting.
Replace with most similar image tokens
Tokenize & Detokenize 10 times
iiginallmmae
Token Embedding
Visual Embedding
Embedding
Figure Reconstruct the image with Anole by 1) replacing the image tokens with most similar tokens BPE visual embeddings: tokenizing and detokenizing the image after replacement for 10 times.
24

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Multimodal Visualization-of-Thoee
Chain-of-Thought (CoT)
Determine whether the agent can safely reach the gift following the action sequence without
Initial Agent Coordinate: [0. 4].
Holes Coordinate:
falling into the holes. not, identify the failure reason.
[1. 1]. 1]. [0. 2]. [2. [3, 2],
Coordinate: [0,
Go up. Agent Coordinate: [0.3].
Full Action Sequence: Go Go right. Go up.
Go right. Agent Agent Coordinate: [1, 3).
Go down. Go down. Go up.
Go up. Agent Coordinate: [1, 2].
Go down. Agent Coordinate; [1. 3].
Return or C. Action Success.
Go down. Agent Coordinate; [1,
Go up. Agent Coordinate: [1, 3].
Failed: Fall into Hole.
Action sequence stopped.
Action Failed: Agent Safe but Fail to Reach Destination.
The answer C. X
Verbal Thought
Multimodal Visualization-ol Thought (MVoT)
Go up.
Go right.
Go up.
Action sequence stopped.
With Visual Thought
The answer
Figure 9. An example where CoT fails FROZENLAKE while MVoT succeeds. Because of sensitivity to the environment, CoT generated incorrect coordinate descriptions of holes, leading to the wrong answer.
0.90
V-Acc. w/ Token Discrepency Loss
0.5
V-Acc. w/ Token Discrepency Loss
90
V-Red, w/o Token Discrepency Loss V-Red, w/o Token Discrepency Loss
V.Red
020
4000
34500
Training Steps
Figure 10. Visualization metrics for image editing on MAZE, evaluated using randomly sampled examples.
25


Reviews:
[{"review_id": "kM0q07B4VR", "reviewer": "Reviewer_5f3V", "summary": "This paper presents Multimodal VoT which integrate visual generation during MLLM’s reasoning process. The idea is straight forward and the motivation is inspired from the theory about how human reasoning in both verbal and non-verbal channels. In order to increase the image generation quality, the authors proposed token discrepancy loss. The proposed framework are further fine-tuned and evaluated on three visual spatial reasoning tasks. Compared to language-based reasoning, MVoT exhibits better robustness and performance.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows:\n\n**Benchmark selection**\n\n> All three benchmarks used in this paper is synthetic and under limited scale. \n\nOur use of grid-based benchmarks was intentional to ensure better controllability and to systematically evaluate various aspects of spatial reasoning—from pattern complexity to action complexity. In addition, grid-based benchmarks enable easier evaluation towards whether the generated visualization is correct in terms of the spatial transition between steps rather than focusing on the image pixel details. \n\n> With simple fine-tuning, the models’ performance on all the three benchmarks can be boosted to over 90%, which may implies the oversimplification of these benchmarks.\n\nWe emphasize that these benchmarks are not oversimplified. In fact, larger models consistently fail across all tasks, and even with fine-tuning, models only achieve up to 70% accuracy on FROZENLAKE. In contrast, our proposed MVoT surpasses 80% accuracy, highlighting the non-triviality of the benchmarks and the effectiveness of our approach.\n\n**Token discrepancy loss**\n\n> The proposed token discrepancy loss seems more like a trick to improve model’s image generation capability, but does not closely related to the reasoning process.\n\nWe believe that there may be some misunderstanding. Our design is grounded in the hypothesis that visualization quality is closely related to reasoning performance. Experimental results across all three tasks show that more accurate visualizations consistently improve reasoning performance.  Furthermore, our ablation studies indicate that integrating the token discrepancy loss yields higher-quality visualizations with fewer redundancies. By penalising deviations from ground-truth visual tokens, it effectively guides the model toward more faithful and visually semantically aligned generated visualizations, thereby improving reasoning performance. Notably, without the token discrepancy loss, the generated visualizations struggle to enhance reasoning, underscoring that it is not a mere “trick” but an essential component for our framework. Therefore, we emphasize the relevance and contribution of the token discrepancy loss to MVoT’s overall reasoning process.\n\n**Why LoRA instead of fully fine-tuning?**\n\nWe chose LoRA due to computational constraints as an approximation of full model fine-tuning. Full fine-tuning requires recording and updating gradients for all the model parameters, which is resource-intensive.\n\n**Can MVoT be built on top of other multimodal models with continuous visual encoder?**\n \nWe appreciate the insightful question regarding broader applicability. In principle, MVoT can be extended to other multimodal models that support interleaved text-image generation. However, many current MLLMs with continuous visual encoders are restricted to producing textual outputs only. We look forward to the development of architectures that support richer, interleaved multimodal outputs, which would further broaden the applicability of MVoT.\n\n**Language-only input output?**\n \nWe agree that it would be interesting to see how RL helps in textual spatial reasoning. However, our current work specifically targets multimodal spatial reasoning, and extending to language-only reasoning lies out of the present scope. We consider it a promising avenue for future exploration.\n\n**Evaluation of MVoT on real-world visual reasoning tasks and other abstract reasoning tasks.**\n \nWe agree that it would be interesting to see how MVoT performs on these tasks. However, due to the current lack of interleaved text-image reasoning datasets in those domains, we focused on tuning Anole with LoRA on three grid-based datasets. This constraint limits generalizability, but we hope our work offers foundational insights and inspires further studies in this direction.\n\nOnce again, we thank the reviewers for their constructive comments and for pointing us toward relevant recent work. We will reflect these considerations and include the suggested references in our camera-ready submission upon acceptance.", "claims_and_evidence": "Yes the claims in this paper are clear and well grounded.", "methods_and_evaluation_criteria": "The proposed methods make sense and the evaluation criteria also sounds good. One only deficit is the scope of the evaluation task are synthetic and under a limited scale.", "theoretical_claims": "This paper does not provide any theoretical proof.", "experimental_designs_or_analyses": "The experiments look good to me.", "relation_to_broader_scientific_literature": "This paper is closely related to the general multimodal and reasoning fields.", "essential_references_not_discussed": "I do not identify any important but missing references in this paper. But I suggest the authors to discuss some recent and highly related work on the spatial reasoning task:\n\n```bash\n[1] Ramakrishnan, Santhosh Kumar, et al. \"Does Spatial Cognition Emerge in Frontier Models?.\" arXiv preprint arXiv:2410.06468 (2024).\n[2] Yang, J., Yang, S., Gupta, A. W., Han, R., Fei-Fei, L., & Xie, S. (2024). Thinking in space: How multimodal large language models see, remember, and recall spaces. arXiv preprint arXiv:2412.14171.\n```", "other_strengths_and_weaknesses": "Strengths:\n\n- Despite straightforward, the idea to visualize each reasoning step (especially under multimodal scope) are really interesting and worth a lot of explorations.\n- Three benchmarks used in this paper is interesting, and MVoT gets competitive performance compared to other approaches.\n\nWeaknesses:\n\n- All three benchmarks used in this paper is synthetic and under limited scale.\n- With simple fine-tuning, the models’ performance on all the three benchmarks can be boosted to over 90%, which may implies the oversimplification of these benchmarks.\n- The proposed token discrepancy loss seems more like a trick to improve model’s image generation capability, but does not closely related to the reasoning process.", "other_comments_or_suggestions": "- It would be better if the authors can conduct the following experiments:\n    - Evaluate MVoT on real-world visual reasoning tasks\n    - Test MVoT on other abstract reasoning tasks like ARC challenge", "questions_for_authors": "- What’s the reason for using lora instead of fully fine-tuning?\n- This paper takes a multimodal-native pre-trained model as a foundation which tokenize both the image input and output to discrete token. However, most of available and competitive multimodal models are trained with a continuous visual encoder like CLIP. Can MVoT be built on top of these models?\n- RL-trained LLM show competitive reasoning capabilities. What’s the performance if benchmark reasoning LLM (like ChatGPT-o1) on three benchmarks with language only input output? (E.g., the input image can be represented as matrices or coordinates)", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UH83vVGADs", "reviewer": "Reviewer_yEfK", "summary": "This paper presents Multimodal Visualization-of-Thought (MVoT). This paradigm enables visual thinking in MLLMs by generating image visualizations of their reasoning traces. MVoT is motivated by human's cognition, having the ability to think both in words and images seamlessly.\n\nMVoT is developed based on Chameleon model, which natively unifies the text and image generation and understanding.\n\nIn MVoT, the model is trained to output a visualization after every intermediate verbal step, and a token descrepancy loss is introduced to enhance the quality of generated image.\n\nIn experiments, the author develop MVoT based on Anole-7B, a finetuned version of Chameleon model and train the model on three tasks.\n\nThrough experiments, the author shows the effectiveness of MVoT, by comparing the model with different finetuning strageties as well as GPT-4o.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows to get more support:\n\n**Experiment and task scope**\n \nOur use of grid-based benchmarks offers better controllability and systematic investigation across various aspects of spatial reasoning—from pattern complexity to action complexity. Grid-based benchmarks also enable easier evaluation towards whether the generated visualization is correct in terms of the spatial transition between steps rather than focusing on the image pixel details.\n\nWe agree that it would be interesting to see how MVoT performs on real-world reasoning tasks. However, due to the current lack of interleaved text-image reasoning datasets, it’s hard for us to adapt MVoT to real-world scenarios in this work. But we hope our paper as the first exploration of natively generating multimodal reasoning traces offers foundational insights and inspires further studies in this direction. \n\n**Visual tokens**\n \nWe generate 1024 visual tokens per visualization. Bounded by a 4096-token context limit, to manage this efficiently, when implementation, we employ a recursive generation strategy with a Markovian assumption: the visualization of the next step $v_{i+1}$ is derived based on the visualizations of the previous step $v_{i}$ and the initial step $v_{0}$. This assumption holds across all the benchmarks we used in this work since at each step the information of the visualization (with textual description for MiniBehavior) is complete. \n\nWhile this design is well-suited to current benchmarks, we acknowledge the opportunity to improve scalability—for instance, through more compact visual representations, as we stated in Appendix E Limitation. As the first work to explore generating native multimodal reasoning traces, MVoT opens up new possibilities, and we hope it will serve as a foundation for future advancements in more complex multimodal tasks.\n\n**Intuition of token discrepancy loss**\n \nToken discrepancy loss discourages the model to output visual tokens that deviate too much from the corresponding golden visual tokens. By penalising deviations from ground-truth visual tokens, it effectively guides the model toward more faithful and visually semantically aligned generated visualizations, thereby improving reasoning performance, as our ablation results confirm.\n\nWe thank the reviewer for acknowledging the potential broader impact of our work to a diverse set of newly released benchmarks. We will include relevant discussion and corresponding modifications in our camera-ready version based on the valuable suggestions from the reviewer upon acceptance.", "claims_and_evidence": "The authors claim that MVoT is competitive with other tasks, and more rubust and reliable than CoT finetuning.\nThought experiments in Section 5, the author support these claims by a clear set of experiments.", "methods_and_evaluation_criteria": "The authors use three proper public benchmarks to train and test the MVoT method, which makes sense.", "theoretical_claims": "There is no complex proofs for theoretical claim that need to be checked in this paper, it is an empirical study and all proposed methods are justified by experiments.", "experimental_designs_or_analyses": "Yes, please refer to paper summary.", "relation_to_broader_scientific_literature": "The method proposed by this paper is relevant to many new multimodal benchmarks, like EMMA [1]. And many other application such as robotics, interactive image editing.\nIt is potentially a new paradigm of multimodal reasoning, so the contribution is relevant to any benchmark or multimodal models.\n\n\n[1] Hao, Y., Gu, J., Wang, H. W., Li, L., Yang, Z., Wang, L., & Cheng, Y. (2025). Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark. arXiv preprint arXiv:2501.05444.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strengths:\n1. This paper propose a new multimodal reasoning paradigm, which is much more intuitive than traditional methods especially on multimodal interactive tasks.\n2. The experiment is well designed and many insights are provided when developing this method.\n\nWeakness:\n1. The experiment scope is narrow. Only three tasks are included, and the environments are not complex enough, with fixed and small set of actions and objects. So it is unclear whether such method can be effectively applied to the real world reasoning tasks.", "other_comments_or_suggestions": "No", "questions_for_authors": "1. How many visual tokens are use in each visualization? In Chameleon, each image is generated with 1024 tokens, I assume less number of tokens are used in each visualization? Based on this question, if this method is applied to more complex environment, do we need more tokens? I assume much more tokens are needed in more complex setting, if this is the case, the reasoning could be very inefficient (e.g. 1024 tokens for each step).\n2. I'm not very sure about the intuition of Token Discrepancy Loss. Is it encouraging the visual tokens to be more close to each other, or is it discouraging the model to output the visual tokens that deviate from the global embedding distribution?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QGckcGwaNJ", "reviewer": "Reviewer_wVwN", "summary": "This paper proposes a new multimodal reasoning paradigm — Multimodal Visualization-of-Thought (MVoT), which enables the model to \"think\" in both textual and visual spaces interleaved. The authors implement this by fine-tuning a Chameleon-like model, Anole-7B, to generate interleaved text and images. They collect data and fine-tune the model on three spatial reasoning tasks. Extensive experiments show that MVoT benefits spatial reasoning, outperforming CoT and direct prompting. The generated visual thoughts can also improve closed-source models.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows to get more support:\n\n**Visualization Consistency and Vulnerability**\n\nWe acknowledge the concern that unconstrained visualization could introduce inconsistencies, particularly if visual outputs do not precisely match the underlying reasoning steps. However, in our framework, visualization is not a standalone prediction but is tightly coupled with the reasoning chain. The generated visualization is conditioned on both the current reasoning step and the accumulated context. This is similar to textual rationales, which also lack hard constraints on internal consistency but are still useful in guiding reasoning. Importantly, MVoT does not generate full images from scratch; instead, it predicts spatial transitions between steps, which is more structured and less generative-intensive. This design significantly reduces the likelihood of arbitrary or unaligned visual outputs. \n\nFrom a performance perspective, while pure text-based reasoning can be effective in general tasks, prior work [1, 2] and our own experiments on FrozenLake show that it falls short in complex multimodal spatial reasoning. In contrast, MVoT demonstrates more robust performance by leveraging visual signals, suggesting that visualization acts as a regularizer rather than a vulnerability. \n\nLastly, we would like to clarify that concerns around the generalization ability are derived from the image generation model, which does not necessarily represent the drawback of MVoT as a reasoning method. \n\n**Error Accumulation in Visual Prediction**\n\nWe agree that cascading errors are a valid concern in any multi-step reasoning framework, especially in modalities like vision. However, it’s important to note that text-only reasoning is equally susceptible to error accumulation, especially when dealing with ambiguous or spatially grounded tasks. For example, our FrozenLake results show that when errors occur in describing environment layouts, Chain-of-Thought can even underperform the Direct baseline due to compounding misrepresentations.\n\nIn contrast, our results show that MVoT performs comparably or better than both Direct and CoT approaches, suggesting that the visual modality does not necessarily amplify errors, and may in fact help mitigate them by providing an interpretable, stepwise grounding of spatial transitions. \n\n**Out-of-Distribution (OOD) Generalization**\n\nWe appreciate your suggestion to evaluate MVoT in cross-task generalization settings. This is an important direction. The inter-task OOD performance of MVoT relies on image generation ability across scenarios. Currently, due to computational constraints, we fine-tune the model using LoRA on a limited dataset, which restricts its ability and makes it more challenging to generalize across tasks such as MAZE to MINIBEHAVIOR. \n\nHowever, we conducted preliminary experiments to test MVoT’s ability to generalize to OOD grid sizes. We found that the model can successfully generate up to 7 consecutive visualizations on larger grids, adapting its stepwise spatial transitions accordingly. That said, we also observed occasional redundancies and inaccuracies, indicating room for improvement.\n\nWe plan to explore MVoT’s OOD behavior more extensively as future work, and will include further discussions on this topic in the camera-ready version, should the paper be accepted.\n\n**More realistic spatial reasoning tasks**\n\nWe agree that it would be interesting to see how MVoT performs on realistic spatial reasoning tasks. However, due to the current lack of interleaved text-image reasoning datasets, it’s hard for us to adapt MVoT to these scenarios in this work. We believe our paper—being one of the first to explore native multimodal reasoning trace generation—lays important groundwork for future research, and we hope it inspires further development of models and datasets in this space.\n\nWe thank the reviewer for providing us with more recent references, which we would include in our discussion together with the comments above in our camera-ready version upon acceptance. \n\n```\nReference\n[1] Ramakrishnan, Santhosh Kumar, et al. \"Does Spatial Cognition Emerge in Frontier Models?.\" arXiv preprint arXiv:2410.06468 (2024).\n[2] Wang, Jiayu et al. “Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models.” The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. \n```", "claims_and_evidence": "The claims in this paper are supported either by references or by experiment results.\nThe experiment results are comprehensive and convictive.", "methods_and_evaluation_criteria": "The proposed fine-tuned Anole implementation is reasonable and clever for the task. The used benchmarks and metrics are technically sound for evaluating complex spatial reasoning abilities.", "theoretical_claims": "I have checked the correctness of the formula.", "experimental_designs_or_analyses": "I have reviewed the experimental design and analysis of the results.", "relation_to_broader_scientific_literature": "The paper provided a promising solution for multimodal reasoning and a great motivation to develop the unified multimodal model.", "essential_references_not_discussed": "I acknowledge the discussion of spatial reasoning papers like SpatialVLM and SpatialRGPT in this paper. It would be better to discuss some essential recent multimodal spatial reasoning fashions, for example:\n[A] Does Spatial Cognition Emerge in Frontier Models?\n[B] Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces", "other_strengths_and_weaknesses": "**Strengths**\nThe proposed new paradigm enables reasoning beyond text space, unlocking more potential abilities and applications of unified MLLMs. The idea is simple but intuitive. \n\n**Weakness**\n1. As there is no constraint that the text (action) can be consistent with visualization, there is a way that the visualization can make the \nreasoning process even more vulnerable. As shown in the analysis in Fig. 4, the visualization can modify the background or just not align with the action. What's worse, I think the image prediction is more vulnerable than text prediction, so the error accumulation in a long reasoning chain can be huge.\n\n2. All shown examples and experimental results are in-distribution. It would be more interesting to the generalization between different spatial reasoning tasks. For example, MLLM learns on the MAZE task, and evaluates on MINIBEHAVIOR and FROZENLAKE or other MAZE games. I suspect such MVoT would be vulnerable in OOD cases, since predicting visualization is easier to accumulate errors during reasoning, while all existing experimental results are all in-distribution. Besides, in the practice cases, the MLLMs always meet up with the OOD cases instead of in-distribution cases. I would like to see such a comparison between MVoT vs Direct and CoT.", "other_comments_or_suggestions": "Generally, I appreciate the idea of this paper. However, I think there are some missing pieces that can probably push this work to a better quality. I would like to raise my score if the author can address my concerns during the rebuttal, but I might also turn it down if the rebuttal is weak.", "questions_for_authors": "This paper reminds me of [C], which generates image visualizations to help robot control. The scenarios shown in this paper are mainly game engines, but it would be interesting to see whether this can work on more realistic spatial reasoning tasks, like SpatialVLM, SpatialRGPT and [B]. \n\n[B] Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces\n[C] Learning Universal Policies via Text-Guided Video Generation", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qxiJdlA5FK", "reviewer": "Reviewer_2g3q", "summary": "The paper introduces Multimodal Visualization-of-Thought (MVoT), a new reasoning paradigm designed to enhance the spatial reasoning capabilities of Multimodal Large Language Models. It improves the spatial reasoning ability over Chain-of-Thought (CoT) prompting by generating image visualizations of their reasoning traces, effectively allowing them to \"think\" in both words and images. Experiments on three datasets demonstrate the effectiveness of the proposed method.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of our work and your valuable suggestions. We would like to address your comments as follows to get more support:\n\n**Toy grid-world based experiments**\n\nOur use of grid-based benchmarks offers better controllability and systematic investigation across various aspects of spatial reasoning—from pattern complexity to action complexity. Grid-based benchmarks also enable easier evaluation towards whether the generated visualization is correct in terms of the spatial transition between steps rather than focusing on the image pixel details.\n\nWe agree that it would be interesting to see how MVoT performs on real-world reasoning tasks. However, due to the current lack of interleaved text-image reasoning datasets, it’s hard for us to adapt MVoT to real-world scenarios such as robotics or 3D reasoning in this work. But we hope our paper as the first exploration of natively generating multimodal reasoning traces offers foundational insights and inspires further studies in this direction.  \n\n**Zero-shot / out-of-distribution evaluations**\n \nWe appreciate your suggestion to evaluate MVoT in cross-task generalization settings. The inter-task OOD performance of MVoT relies on image generation ability. Currently, due to computational constraints, we fine-tune the model using LoRA on a limited dataset, which restricts its ability and makes it more challenging to generalize across tasks and scenarios.\n\nHowever, we conducted preliminary experiments to test MVoT’s ability to generalize to OOD grid sizes. We found that the model can successfully generate up to 7 consecutive visualizations on larger grids, adapting its stepwise spatial transitions accordingly. That said, we also observed occasional redundancies and inaccuracies, indicating room for improvement.\n\nWe plan to explore MVoT’s OOD behavior more extensively as future work, and will include further discussions on this topic in the camera-ready version, should the paper be accepted.\n\n**Failure cases of the proposed method**\n \nMost of the failure cases for MVoT are caused by generating incorrect visualizations during inference, including generating wrong or redundant visualizations with perturbed or blurred image details, as we illustrated in Section 6 and Appendix D.2. We will include  more analysis and discussion on failure cases in our camera-ready version upon acceptance. \n\n**Model choices**\n \nChameleon supports interleaved text-image generation, which meets the requirements of MVoT. In principle, MVoT can be extended to other multimodal models that support interleaved text-image generation. However, many current MLLMs are restricted to producing textual outputs only. We look forward to the development of architectures that support richer, interleaved multimodal outputs, which would further broaden the applicability of MVoT.\n\nWe thank the reviewer for appreciating the novelty and the constructive feedback on our work. We will reflect these considerations in our camera-ready submission upon acceptance.", "claims_and_evidence": "### Well-supported claims\n1. MVoT outperforms CoT in certain complex spatial reasoning tasks.\n2. MVoT provides better interpretability than CoT.\n\n### Weakly Supported Claims\nMVoT generalizes better than CoT: the paper does not sufficiently test out-of-domain generalization beyond the three controlled, grid-based environments. For example, robotics, navigation, and real-world images are not evaluated.", "methods_and_evaluation_criteria": "The proposed method and evaluation criteria make sense for the problem. Please also see weaknesses below.", "theoretical_claims": "No theoretical claim.", "experimental_designs_or_analyses": "- The three benchmarks (MAZE, MINIBEHAVIOR, FROZENLAKE) gradually increase in complexity, testing different aspects of spatial reasoning.\n- Ablation studies on token discrepancy loss show that removing LD worsens visual coherence and reasoning accuracy, validating its necessity.\n- The comparison against several baselines (Direct Prompting, CoT, GPT-4o, and Interleaved Training) demonstrate its strengths.\n- Beyond task accuracy dominates, the paper also introduces visualization accuracy, redundancy, and pattern correctness metrics, providing some insight into how well the model generates visual thought.", "relation_to_broader_scientific_literature": "This paper utilizes/fine-tunes a unified multimodal model Chameleon to enable interleaved multimodal output for chain-of-thought reasoning. It extends the textual CoT to the multimodal area.", "essential_references_not_discussed": "To my knowledge, necessary references are discussed.", "other_strengths_and_weaknesses": "### Strengths\n- It extends CoT to multimodal reasoning by integrating visual thought generation. First work to natively interleave image and text reasoning in MLLMs.\n- This paper introduces token discrepancy loss, which helps align text and image token spaces, improving coherence and quality of generated visuals.\n- The performance improvement on three benchmarks demonstrate the effectiveness of the proposed method.\n\n### Weaknesses\n- All tasks (Maze, MiniBehavior, FrozenLake) are toy grid-worlds based experiments. No experiments on real-world benchmarks such as images, 3D reasoning, or robotics.\n- The model has been fine-tuned on the train set of the benchmark. No zero-shot or out-of-distribution evaluations. It’s unclear if MVoT can handle new spatial reasoning problems beyond its training setup.", "other_comments_or_suggestions": "NA", "questions_for_authors": "- Can the authors discuss some specific failure cases of the proposed method?\n- Is there a specific reason for using Chameleon as the base model? Can the proposed method work for other unified vision-language models?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces Multimodal Visualization-of-Thought (MVoT), a novel multimodal reasoning paradigm that extends the chain-of-thought (CoT) concept by interleaving textual reasoning with visual outputs. Specifically, by introducing a token discrepancy loss, the authors demonstrate that MVoT can enhance complex spatial reasoning tasks. Experiments on three synthetic grid-based benchmarks (e.g., MAZE, MiniBehavior, FrozenLake) indicate that this new multimodal reasoning paradigm can achieve competitive performance.

Overall, all reviewers recognize the novelty of this paradigm and the solid experimental support provided. But meanwhile, they raise multiple major concerns, including: 1) all current evaluations are in-distribution, therefore it is unclear whether MVoT can generalize to more realistic or out-of-distribution scenarios; 2) its scalability to more complex scenes is a concern; 3) the intuition behind introducing the token discrepancy loss is not sufficiently clear, and its technical novelty is viewed as limited; and 4) it is unclear whether MVoT can generalize to other multimodal LLMs with a continuous visual encoder like CLIP. 

The authors' rebuttal is responsive and effectively addresses most of these concerns. As a result, all reviewers unanimously vote to accept the submission, and the AC concurs with this decision.
Decision: Accept (poster)