Jun Liu

From Rollouts to Recipes: Self-Contained Post-Training for LLMs

Yifei Li, Lingling Zhang, Muye Huang, et al.

Beyond the Stability-Exploration Dilemma: Environmental Regularization for LLM Policy Optimization

Xianlei Zhou, Xiangdi Meng, Yu He, et al.

I Seek You in Videos: Identity-Conditioned Queries for Person-Centric Video Reasoning

Shibo Gao, Chongxiao Wang, Chenglong Huang, et al.

Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments

Xianhui Meng, Zirui Song, Yuchen Zhang, et al.

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications

Oxygen AIIC, Chan Long, Chao Liu, et al.

MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditions

Xiaofei Hui, Haoxuan Qu, Hossein Rahmani, et al.

Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems

Shihao Qi, Jie Ma, Rui Xing, et al.

Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees

Amartya Mukherjee, Maxwell Fitzsimmons, David C. Del Rey Fernández, et al.

This paper solves a major problem with physics-informed neural networks (PINNs)—we don't know how accurate their solutions actually are. The researchers proved that if a neural network's residual errors (how well it satisfies the equations) are small, then its actual solution must be close to the true answer, providing the first rigorous guarantees for these AI-based equation solvers.