Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning (2025)

Abstract: While chains-of-thought (CoT) have advanced complex reasoning in multimodal large language models (MLLMs), existing methods remain confined to text or static visual domains, often faltering in dynamic spatial reasoning tasks. To bridge this gap, we present GRASSLAND, a novel maze navigation benchmark designed to evaluate dynamic spatial reasoning. Our experiments show that augmenting textual reasoning chains with dynamic visual drafts, overlaid on input images, significantly outperforms conventional approaches, offering new insights into spatial reasoning in evolving environments. To generalize this capability, we propose D2R (Dynamic Draft-Augmented Reasoning), a training-free framework that seamlessly integrates textual CoT with corresponding visual drafts into MLLMs. Extensive evaluations demonstrate that D2R consistently enhances performance across diverse tasks, establishing a robust baseline for dynamic spatial reasoning without requiring model fine-tuning.

## introduction humans often exhibit effective behavioral strategies that inspire multimodal large language models (mllms) @xcite @xcite to tackle complex tasks, particularly in the realm of multimodal reasoning. in such tasks, humans commonly create drafts to support step-by-step thinking when processing visual information that integrates text and imagery. this drafting approach is especially beneficial for extracting insights from dynamic images, where chronological, incremental reasoning is highly effective. niques, exemplified by methods such as tot @xcite and icot @xcite ), but they lack mechanisms for draft creation based on input images. while these frameworks achieve strong results on textual and static visual tasks @xcite @xcite , they often suffer from loss of rich visual information and diminished spatial awareness-factors critical for dynamic multimodal spatial reasoning. since dynamic spatial reasoning plays a pivotal role in many real-world applications, it is important to investigate how well existing mod-els perform in this domain. ## dynamic multimodal spatial reasoning to further evaluate the performance of the existing mllms on the dynamic spatial reasoning task, we propose grassland, a dynamic maze navigation scenario for the dynamic spatial reasoning task. as shown in figure 3 , it simulates a classical pixel grid world w with a start point p s and destination point p e . also, parts of the environment grids contain obstacles('the walls')p o , dynamic traps ('the lava')p l , and stationary traps ('the water') p w . the model is required to determine the next action or state based on the given prompt and scenario. ## methodology although the draft cot can obtain great performance gains, it rely on image generation capabilities not universally available across all the mllms. to broaden its applications, we propose the dynamic draft augmented reasoning framework (d2r), a training-free framework to generate intermediate thoughts on both textual thoughts and visual drafts. d2r extends the reasoning space from a signal language domain l to multiple domains l ∪ v ∪ t , where v represents the visual domain and t represents the chronology domain. it enables models to reason in dynamic visual information by splitting it into steps and marking drafts over the input images in each step. by combining textual thoughts with corresponding drafts, this novel reasoning paradigm offers a more intuitive and accurate method with enhanced ability to collaborate on details between these two modalities.