Shuo Yang

One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Ziyue Peng, et al.

Experience Augmented Policy Optimization for LLM Reasoning

Jinda Lu, Kexin Huang, Junkang Wu, et al.

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals

Shuo Yang, Jinda Lu, Chiyu Ma, et al.

Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations

Bochao Liu, Zhipeng Qian, Yang Zhao, et al.

OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory

Jinze Li, Yang Zhang, Xin Yang, et al.

FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization

Chiyu Ma, Shuo Yang, Kexin Huang, et al.

CRAFT: Aligning Diffusion Models with Fine-Tuning Is Easier Than You Think

Zening Sun, Zhengpeng Xie, Lichen Bai, et al.

CRAFT is a new method for improving AI image generation models to better match what humans prefer, using far less data and computing power than existing approaches. Instead of needing thousands of expensive human-labeled examples, CRAFT can work with just 100 samples by first filtering them for quality and then fine-tuning the model, while being 11-220 times faster than competing methods. The researchers also mathematically proved that this simpler approach is actually equivalent to a more complex reinforcement learning method, making it both practical and theoretically sound.