15 citations · 27 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
Siru Ouyang, Jun Yan, I-Hung Hsu +14
With the growing adoption of large language model agents in persistent real-world roles, they naturally encounter continuous streams of tasks. A key limitation, however, is their f…
FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models
Xuan Liu, Siru Ouyang, Xianrui Zhong +2
Large language models (LLMs) have gained significant attention in chemistry. However, most existing datasets center on molecular-level property prediction and overlook the role of…
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…
Benchmarking Retrieval-Augmented Generation for Chemistry
Xianrui Zhong, Bowen Jin, Siru Ouyang +5
Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that…
Multimodal Search in Chemical Documents and Reactions
Ayush Kumar Shah, Abhisek Dey, Leo Luo +9
We present a multimodal search tool that facilitates retrieval of chemical reactions, molecular structures, and associated text from scientific literature. Queries may combine mole…