Haotian Li

VITAL introduces a novel latent-space reasoning framework for medical MLLMs, utilizing visual-semantic dual supervision to enhance reasoning capabilities and provide crucial interpretability without sacrificing efficiency.

ReSkill is an RL-in-the-loop framework that reconciles skill creation and policy optimization by automatically creating, testing, and refining modular skills alongside the agent's policy learning, leading to superior generalization.

This paper proposes Yi, a system for efficient and effective in-place updates in large-scale vector indexing, achieving higher update and search throughput than state-of-the-art methods.

This paper analyzes large-scale human-LLM conversations to identify learning behaviors and their associated factors.

A neural network called DMSNet is proposed for multi-target sensing in multi-band integrated sensing and communication systems, improving accuracy, reducing errors, and decreasing runtime.

This paper introduces Skill Self-Play (Skill-SP), a co-evolutionary framework for LLM training that bridges the gap between structured verification and open-ended exploration.