Video Models Reason Early: Exploiting Plan Commitment for Maze Solving

2026-03-31

AI Summary

This study addresses the limited understanding of internal reasoning mechanisms in existing video diffusion models, particularly their planning capabilities in task-oriented scenarios such as maze solving. Using 2D mazes as a testbed, the authors discover that these models commit to high-level path planning early in the denoising process—a phenomenon termed “early planning commitment”—and that task difficulty is primarily governed by path length rather than obstacle density. To enhance long-horizon reasoning, they propose the ChEaP method, which leverages chained multi-segment generation. Experiments demonstrate that ChEaP improves solution accuracy on long-horizon maze tasks from 7% to 67% and achieves a 2.5× overall performance gain on both Frozen Lake and VR-Bench benchmarks.

Abstract

Video diffusion models exhibit emergent reasoning capabilities like solving mazes and puzzles, yet little is understood about how they reason during generation. We take a first step towards understanding this and study the internal planning dynamics of video models using 2D maze solving as a controlled testbed. Our investigations reveal two findings. Our first finding is early plan commitment: video diffusion models commit to a high-level motion plan within the first few denoising steps, after which further denoising alters visual details but not the underlying trajectory. Our second finding is that path length, not obstacle density, is the dominant predictor of maze difficulty, with a sharp failure threshold at 12 steps. This means video models can only reason over long mazes by chaining together multiple sequential generations. To demonstrate the practical benefits of our findings, we introduce Chaining with Early Planning, or ChEaP, which only spends compute on seeds with promising early plans and chains them together to tackle complex mazes. This improves accuracy from 7% to 67% on long-horizon mazes and by 2.5x overall on hard tasks in Frozen Lake and VR-Bench across Wan2.2-14B and HunyuanVideo-1.5. Our analysis reveals that current video models possess deeper reasoning capabilities than previously recognized, which can be elicited more reliably with better inference-time scaling.

Problem

• video diffusion models
• reasoning
• maze solving
• plan commitment
• planning dynamics

Innovation

• early plan commitment
• video diffusion models
• maze solving
• reasoning dynamics
• inference-time scaling