Imagine while Reasoning in Space: Multimodal Visualization-of-Thought (2501.07542v1)

Abstract: Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in LLMs and Multimodal LLMs (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.

• The paper introduces MVoT, a paradigm that interleaves text and image generation to dynamically visualize spatial reasoning.
• It proposes a novel token discrepancy loss to improve the coherence and fidelity of generated visualizations against textual descriptions.
• Experimental results demonstrate robust gains over traditional Chain-of-Thought methods in complex spatial manipulation tasks.

The paper "Imagine while Reasoning in Space: Multimodal Visualization-of-Thought" (2501.07542) introduces a reasoning paradigm termed Multimodal Visualization-of-Thought (MVoT) designed to enhance the capabilities of Multimodal LLMs (MLLMs) in complex spatial reasoning tasks. This approach draws inspiration from human cognition, where linguistic reasoning is often complemented by visual imagination. While Chain-of-Thought (CoT) prompting has significantly advanced reasoning in LLMs and MLLMs, its efficacy diminishes in scenarios demanding intricate spatial understanding and manipulation, areas where MVoT aims to provide substantial improvements.

The Multimodal Visualization-of-Thought (MVoT) Paradigm

MVoT extends standard autoregressive MLLMs by enabling them to generate intermediate image visualizations interleaved with textual reasoning steps. Instead of producing only a sequence of text tokens representing the thought process (as in CoT), an MLLM employing MVoT generates a multimodal sequence, dynamically visualizing the state described or inferred during reasoning.

Consider a spatial reasoning task, such as predicting the final configuration of objects after a series of manipulations. A standard CoT approach might output: Step 1: Move the blue cube left. Step 2: Rotate the red pyramid 90 degrees clockwise. Step 3: Place the blue cube on top of the green cylinder.

With MVoT, the MLLM would augment this textual trace with generated images: Step 1: Move the blue cube left. [Image depicting the scene after moving the blue cube] Step 2: Rotate the red pyramid 90 degrees clockwise. [Image depicting the scene after rotating the red pyramid] Step 3: Place the blue cube on top of the green cylinder. [Image depicting the final scene]

This process allows the model to ground its symbolic, textual reasoning in a concrete visual representation at intermediate stages. This visual grounding is hypothesized to mitigate errors common in purely linguistic spatial reasoning, where complex relationships and transformations are difficult to track accurately using text alone. The generation is typically autoregressive, where the prediction of the next token (be it text or image) depends on the sequence generated so far, including previous text and images.

Enhancing Visualization Quality: Token Discrepancy Loss

A crucial component introduced for training MLLMs to effectively utilize MVoT is the Token Discrepancy Loss. The quality of the generated visualizations is paramount; inaccurate or incoherent images would hinder rather than help the reasoning process. This novel loss function specifically targets the improvement of visual coherence (consistency between the generated image and the preceding textual description) and fidelity (accuracy of the visual representation).

While the exact formulation is not detailed in the abstract, the token discrepancy loss likely operates by penalizing deviations between the generated visual representation and a target representation implied by the textual reasoning context. Conceptually, during training, if $T_{img}$ represents the generated image tokens/features and $C_{text}$ represents the preceding textual context, the loss could be formulated as:

$L_{total} = L_{autoregressive} + \lambda L_{TD}$

Where $L_{autoregressive}$ is the standard cross-entropy loss for predicting the next token (text or image), and $L_{TD}$ is the token discrepancy loss. $L_{TD}$ might compare the generated visual features $f_{vis}(T_{img})$ with target features $g(C_{text})$ derived from the text:

$L_{TD} = D(f_{vis}(T_{img}), g(C_{text}))$

Here, $D$ represents a suitable distance metric (e.g., cosine distance, MSE) in a shared or projected feature space. The function $g$ could involve rendering a target image based on the text or extracting expected semantic/geometric features. Integrating $L_{TD}$ encourages the MLLM's generative component to produce images that faithfully reflect the state described in the accompanying text, thereby ensuring the visualizations are useful reasoning aids.

Experimental Validation and Performance

The efficacy of MVoT was evaluated on several dynamic spatial reasoning tasks. These tasks likely involved tracking object positions, orientations, and relationships over a sequence of actions or time steps. The experiments compared MVoT against standard CoT prompting within MLLMs.

The results indicate that MVoT achieves competitive performance across the board. More significantly, MVoT demonstrates robust and reliable improvements in the most challenging scenarios where CoT reasoning tends to fail. This suggests that the explicit generation of visual intermediate states provides a tangible benefit when the complexity of spatial transformations and interactions surpasses the capacity of purely textual reasoning chains. The abstract claims these improvements establish MVoT as a viable approach for complex reasoning tasks where visual thinking offers a complementary pathway to verbal reasoning.

Implementation Considerations

Implementing MVoT involves several practical considerations:

• Model Architecture: Requires an MLLM architecture capable of autoregressively generating interleaved sequences of text and images. Models like Flamingo, PALM-E, or newer architectures with fine-tuned generative capabilities for both modalities are potential starting points. The architecture must accommodate the injection of the token discrepancy loss during training, likely requiring modifications to the training procedure and potentially the visual encoding/decoding pathways.
• Training Data: Training may necessitate datasets containing not just the problem and final answer, but also ground-truth intermediate states (textual descriptions and corresponding images) for supervised learning of the visualization generation. Alternatively, if ground-truth images are unavailable, the token discrepancy loss might rely on self-consistency checks or comparisons against representations derived solely from the textual context, posing a greater training challenge.
• Inference Process: At inference time, the MLLM generates the reasoning trace step-by-step, producing text segments followed by corresponding image visualizations. This significantly increases computational cost compared to text-only CoT due to the overhead of generating potentially high-resolution images at multiple intermediate points. Latency will also increase substantially.

  function MVoT_Inference(problem_description): context = problem_description reasoning_trace = [] visualizations = [] current_state_text = "" while not reach_final_answer(current_state_text): # Generate next textual reasoning step text_step = model.generate_text(context) reasoning_trace.append(text_step) context += text_step current_state_text += text_step # Generate visualization for the current state image_visualization = model.generate_image(context) visualizations.append(image_visualization) context += image_to_token(image_visualization) # Add image representation to context # Generate final answer final_answer = model.generate_text(context) reasoning_trace.append(final_answer) return reasoning_trace, visualizations

• Visualization Granularity and Cost: A trade-off exists between the frequency of visualization generation and computational cost. Generating images after every minor reasoning step offers maximum grounding but incurs high overhead. Generating visualizations only at key milestones might be more efficient but risks error accumulation between visualizations.
• Evaluation Metrics: Evaluating MVoT systems requires assessing not only the correctness of the final answer but also the quality, fidelity, and relevance of the generated intermediate visualizations. This may involve automated metrics (e.g., image similarity to ground truth, CLIP score between image and text description) and/or human evaluation.
• Error Propagation: Errors in early visualizations could potentially mislead subsequent reasoning steps, highlighting the importance of the visualization quality ensured by mechanisms like the token discrepancy loss.

Conclusion

The Multimodal Visualization-of-Thought (MVoT) paradigm presented in (2501.07542) offers a novel approach to enhance spatial reasoning in MLLMs by integrating generated visualisations within the reasoning chain. The introduction of a token discrepancy loss aims to ensure the quality and relevance of these visualizations. Experimental results suggest MVoT provides significant advantages over traditional CoT, particularly for complex spatial tasks. While practical implementation entails challenges related to model architecture, training data, and computational cost, MVoT represents a potentially valuable direction for developing more robust and human-like reasoning capabilities in multimodal AI systems.