4 papers
ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery
Yi Cao, Liaoyaqi Wang, Jieneng Chen +3
Generative models have revolutionized the process of materials discovery, yet they often fail to satisfy underlying physical causality. Through an analysis of Large Language Models…
Rethinking LoRA Memory Through the Lens of KV Cache Compression
Chunsheng Zuo, Liaoyaqi Wang, William Jurayj +2
Parametric retrieval augmentation encodes document information into lightweight, document-specific modules such as LoRA adapters, reducing the need to include all evidence as input…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Always Tell Me The Odds: Fine-grained Conditional Probability Estimation
Liaoyaqi Wang, Zhengping Jiang, Anqi Liu +1
We present a state-of-the-art model for fine-grained probability estimation of propositions conditioned on context. Recent advances in large language models (LLMs) have significant…