23 citations · 35 across the 10 of their papers we have counts for
12 papers
Heterogeneous Agentic Coevolution
Wenqian Ye, Ziwei Guan, Zi Yang +14
Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-specific applications rely on spe…
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…
MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics
Xinyu Liu, Zixuan Xie, Amir Moeini +5
While the ecosystem of Lean and Mathlib has enjoyed celebrated success in formal mathematical reasoning with the help of large language models (LLMs), the absence of many folklore…
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…
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
Huimin Xu, Seungjun Yi, Terence Lim +9
Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable insights in healthcare but is resource-i…
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…
Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions
Guangzhi Xiong, Qiao Jin, Xiao Wang +3
The emergent abilities of large language models (LLMs) have demonstrated great potential in solving medical questions. They can possess considerable medical knowledge, but may stil…
A Self-explaining Neural Architecture for Generalizable Concept Learning
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
With the wide proliferation of Deep Neural Networks in high-stake applications, there is a growing demand for explainability behind their decision-making process. Concept learning…
Benchmarking Retrieval-Augmented Generation for Medicine
Guangzhi Xiong, Qiao Jin, Zhiyong Lu +1
While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinatio…