15 citations · 16 across the 4 of their papers we have counts for
7 papers
Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation
Jiashuo Sun, Pengcheng Jiang, Saizhuo Wang +13
Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise, even when the required evidence appears in the top-K results. A key reason is that retr…
Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning
Junlin Wu, Xianrui Zhong, Jiashuo Sun +4
Large language models (LLMs) have demonstrated remarkable advances in reasoning capabilities. However, their performance remains constrained by limited access to explicit and struc…
Think Twice: Branch-and-Rethink Reasoning Reward Model
Yizhu Jiao, Jiaqi Zeng, Julien Veron Vialard +3
Large language models (LLMs) increasingly rely on thinking models that externalize intermediate steps and allocate extra test-time compute, with think-twice strategies showing that…
A Survey on Retrieval And Structuring Augmented Generation with Large Language Models
Pengcheng Jiang, Siru Ouyang, Yizhu Jiao +3
Large Language Models (LLMs) have revolutionized natural language processing with their remarkable capabilities in text generation and reasoning. However, these models face critica…
Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning
Yin Fang, Qiao Jin, Guangzhi Xiong +6
Cell type annotation is a key task in analyzing the heterogeneity of single-cell RNA sequencing data. Although recent foundation models automate this process, they typically annota…
RAST: Reasoning Activation in LLMs via Small-model Transfer
Siru Ouyang, Xinyu Zhu, Zilin Xiao +3
Reinforcement learning (RL) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs), as evidenced by recent successes such as OpenAI…