Latest Research Highlight

Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models

Taesung Kwon et al., NeurIPS 2026

Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models

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About BISPL

BISPL —the Bio Imaging, Signal Processing, and Machine Learning Lab at KAIST AI — is led by Prof. Jong Chul Ye, an IEEE Fellow. Our research spans theory & reinforcement Learning (RL) for diffusion/flow Models, LLMs & agentic AI, vision-language-action (VLA) Models, world models, computer vision, AI for science & healthcare, and medical imaging.

Across these areas, we seek to uncover the mathematical beauty and fundamental principles of modern AI and translate them into real-world impact. We ask not only how AI works, but why it works, developing new theories and algorithms from challenging practical problems. Building on deep expertise in biomedical imaging, signal processing, inverse problems, and machine learning, we turn real-world applications into sources of fundamental insight—and fundamental advances into transformative technologies.

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Most Recent Publications

  1. [J180] ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning
    Hyungjin Chung, Dohun Lee, Zihui Wu, Byung-Hoon Kim, Katherine L. Bouman, and Jong Chul Ye
    Medical Image Analysis (in press), 2026
  2. [C109] Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models
    Taesung Kwon, Jonghyun Park, Hyungjin Chung, and Jong Chul Ye
    In Conference on Neural Information Processing Systems ( NeurIPS ), 2026
  3. [C108] Accelerated Image Editing via Consistency-Aware Source Token Pruning
    Jeongsol Kim, Jong Chul Ye, Yufei Wang, and Jian Wang
    In Conference on Neural Information Processing Systems ( NeurIPS ), 2026
  4. [C107] MotionCFG: Boosting Motion Dynamics via Semantic Motion Sharpening
    Byungjun Kim, Soobin Um, and Jong Chul Ye
    In Conference on Neural Information Processing Systems ( NeurIPS ), 2026
  5. [C106] Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
    Jeongjae Lee, Jinho Chang, Jeongsol Kim, and Jong Chul Ye
    In Conference on Neural Information Processing Systems ( NeurIPS ), 2026
  6. [C105] CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation
    Seonghyun Jin, Youngmin Kim, Sunwoo Park, and Jong Chul Ye
    In Conference on Neural Information Processing Systems ( NeurIPS ), 2026