7 citations · 34 across the 16 of their papers we have counts for
14 papers · 1 filter
MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory and Fine-Grained Scientific Hypothesis Discovery
Hongran An, Zonglin Yang
Large language models (LLMs) show remarkable potential in scientific hypothesis discovery. However, existing approaches face two critical limitations: they treat divergent explorat…
MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification
MiroMind Team, S. Bai, L. Bing +39
We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends…
MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization
Xingxuan Li, Yao Xiao, Dianwen Ng +15
Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains,…
Harnessing Large Language Models for Scientific Novelty Detection
Yan Liu, Zonglin Yang, Soujanya Poria +2
In an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia. Despite potential, the lack of an appropriate benchmark dataset…
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search
Zonglin Yang, Wanhao Liu, Ben Gao +7
Large language models (LLMs) have shown promise in automating scientific hypothesis generation, yet existing approaches primarily yield coarse-grained hypotheses lacking critical m…
MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback
Wanhao Liu, Zonglin Yang, Jue Wang +7
Hypothesis ranking is vital for automated scientific discovery, especially in cost-intensive, throughput-limited natural science domains. Current methods focus on pre-experiment ra…
ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition
Yujie Liu, Zonglin Yang, Tong Xie +7
Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lac…
LLM4SR: A Survey on Large Language Models for Scientific Research
Ziming Luo, Zonglin Yang, Zexin Xu +2
In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of…
MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses
Zonglin Yang, Wanhao Liu, Ben Gao +6
Scientific discovery plays a pivotal role in advancing human society, and recent progress in large language models (LLMs) suggests their potential to accelerate this process. Howev…
A Survey on Semantic Processing Techniques
Rui Mao, Kai He, Xulang Zhang +4
Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of…
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Zonglin Yang, Xinya Du, Junxian Li +3
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past…
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
Zonglin Yang, Xinya Du, Rui Mao +2
Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses forma…
Language Models as Inductive Reasoners
Zonglin Yang, Li Dong, Xinya Du +5
Inductive reasoning is a core component of human intelligence. In the past research of inductive reasoning within computer science, formal language is used as representations of kn…
Improving Event Duration Prediction via Time-aware Pre-training
Zonglin Yang, Xinya Du, Alexander Rush +1
End-to-end models in NLP rarely encode external world knowledge about length of time. We introduce two effective models for duration prediction, which incorporate external knowledg…