Non-spatial generality of early plan commitment and horizon limitations

Investigate whether the phenomena of early plan commitment—where a video diffusion model commits to a high-level plan within the first few denoising steps—and generation-horizon limitations—where success drops sharply for trajectories exceeding the model’s effective planning window—also manifest in non-spatial reasoning modalities beyond 2D maze solving.

Background

The paper analyzes how video diffusion models solve 2D mazes and finds that they commit to a high-level motion plan within the first few denoising steps (early plan commitment), with later steps mainly refining visual details. It also shows a sharp horizon limitation: success collapses for paths longer than about 12 steps, indicating an effective planning window per generation.

These findings are established in spatial, visually grounded tasks (Frozen Lake and VR-Bench). Whether similar early-commitment dynamics and horizon constraints arise in non-spatial reasoning modalities remains unverified, motivating a broader investigation across different forms of reasoning.