37 citations · 38 across the 17 of their papers we have counts for
6 papers · 1 filter
Rethinking Scientific Discovery in the Agentic Era
Yining Zheng, Yuxin Wang, Jiahao Lu +27
Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature gro…
AI Can Learn Scientific Taste
Jingqi Tong, Mingzhe Li, Hangcheng Li +20
Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with the potential for long-term sci…
Multi-hop Reasoning via Early Knowledge Alignment
Yuxin Wang, Shicheng Fang, Bo Wang +4
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for Large Language Models (LLMs) to address knowledge-intensive queries requiring domain-specific or up-to-d…
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
Yuan Li, Qi Luo, Xiaonan Li +7
Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and mitigate hallucination. However, dense retri…
Text Information Aggregation with Centrality Attention
Jingjing Gong, Hang Yan, Yining Zheng +2
A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves aggregation process of combining the representations o…
Heterogeneous Graph Neural Networks for Extractive Document Summarization
Danqing Wang, Pengfei Liu, Yining Zheng +2
As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the grap…