Latent Visual Diffusion Reasoning with Monte Carlo Tree Search

2026-06-26

AI Summary

This work addresses the limited interpretability of existing action quality assessment models, which often fail to reveal the visual reasoning processes underlying their judgments. To this end, the paper introduces, for the first time, a unified framework that integrates keypoint-guided Monte Carlo tree search into a latent visual diffusion model, enabling simultaneous high-accuracy skill evaluation and interpretable step-by-step visual reasoning. The proposed method produces clear reasoning trajectories that explicitly highlight the critical visual evidence supporting each prediction. Extensive experiments across four cross-domain datasets demonstrate that the model not only achieves competitive quantitative performance but also effectively visualizes its decision rationale, thereby substantially enhancing the transparency and trustworthiness of the assessment process.

Abstract

Analyzing fine-grained skill activities (e.g., sports, surgery) requires not only recognizing visual patterns but also performing step-by-step visual reasoning that leads to the final judgment. While recent advances in action quality assessment have achieved remarkable progress in evaluating performance, existing models remain black boxes, where they lack the ability to explicitly reveal the reasoning processes underlying their judgments. To address this limitation, we propose Latent Visual Diffusion Reasoning (LVDR), a novel framework that integrates keypoint-guided Monte Carlo Tree Search (MCTS) to model and visualize the latent visual reasoning process. LVDR not only produces more accurate skill assessments but also uncovers the critical visual reasoning sequences that contribute to the final evaluation. Extensive experiments across four datasets spanning diverse sports and surgical domains demonstrate that LVDR achieves competitive quantitative performance while providing interpretable visual reasoning trajectories leading to the final predictions. Source codes and models can be found through the following link: https://github.com/XiruiTeng/LVDR_Official.git.

Problem

• visual reasoning
• action quality assessment
• interpretability
• black-box models
• fine-grained skill activities

Innovation

• Latent Visual Diffusion Reasoning
• Monte Carlo Tree Search
• Visual Reasoning
• Action Quality Assessment
• Interpretable AI