5 citations · 7 across the 6 of their papers we have counts for
6 papers
ProofEvolve: Neuro-Symbolic Evolution for Formal Automated Theorem Proving
Wenqian Ye, Ziwei Guan, Eric Xie +6
Automated theorem proving offers a natural foundation for recursive self-improvement in scientific discovery. However, existing neural provers do not fully preserve this recursive…
ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction
Eric Xie, Wenqian Ye, Aidong Zhang
Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules. Whether such an output that appears creative is truly original and effective…
SlideBot: A Multi-Agent Framework for Generating Informative, Reliable, Multi-Modal Presentations
Eric Xie, Danielle Waterfield, Michael Kennedy +1
Large Language Models (LLMs) have shown immense potential in education, automating tasks like quiz generation and content summarization. However, generating effective presentation…
Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
Guangzhi Xiong, Eric Xie, Corey Williams +5
Large language models (LLMs) have shown significant potential in scientific disciplines such as biomedicine, particularly in hypothesis generation, where they can analyze vast lite…
Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models
Guangzhi Xiong, Eric Xie, Amir Hassan Shariatmadari +3
Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their abili…
IdeaBench: Benchmarking Large Language Models for Research Idea Generation
Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong +4
Large Language Models (LLMs) have transformed how people interact with artificial intelligence (AI) systems, achieving state-of-the-art results in various tasks, including scientif…