13 citations · 17 across the 10 of their papers we have counts for
11 papers
Idea Search: Guiding Tree Search with Ideas to Explore Diverse Scientific Methods
Xuefei Julie Wang, Hao Cui, Michael P. Brenner +1
Tree Search-based test-time scaling of LLMs is a powerful tool for automated scientific coding. However, pure Tree Search sometimes struggles with systematic exploration, becoming…
Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search
Sarah Martinson, Michael P. Brenner, Martyna Plomecka +3
Probabilistic forecasting of infectious diseases is crucial for public health monitoring and preparedness but has historically relied on labor-intensive manual model curation by ex…
Optimized Three-Dimensional Photovoltaic Structures with LLM guided Tree Search
Michael P. Brenner, Lizzie Dorfman, John C. Platt
We present a case study for how AI coding systems can be used to generate novel scientific hypotheses. We combine a generic coding agent (Google's AntiGravity) with an LLM-driven t…
Probabilistic Seasonal Streamflow Forecasting Across California's Sierra Nevada Watersheds with Agentic AI
Ignacio Lopez-Gomez, Michael P. Brenner, Tapio Schneider
Accurate seasonal runoff forecasts are critical for managing California's reservoirs and water supply for millions of its residents. Winter snow accumulation provides a strong sour…
Expert Evaluation of LLM World Models: A High- Superconductivity Case Study
Haoyu Guo, Maria Tikhanovskaya, Paul Raccuglia +20
Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comp…
An AI system to help scientists write expert-level empirical software
Eser Aygün, Anastasiya Belyaeva, Gheorghe Comanici +39
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments\cite{hannay2009how}. To address this, we…
FEABench: Evaluating Language Models on Multiphysics Reasoning Ability
Nayantara Mudur, Hao Cui, Subhashini Venugopalan +3
Building precise simulations of the real world and invoking numerical solvers to answer quantitative problems is an essential requirement in engineering and science. We present FEA…
Towards AI-assisted Academic Writing
Daniel J. Liebling, Malcolm Kane, Madeleine Grunde-Mclaughlin +3
We present components of an AI-assisted academic writing system including citation recommendation and introduction writing. The system recommends citations by considering the user'…
CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning
Hao Cui, Zahra Shamsi, Gowoon Cheon +31
Scientific problem-solving involves synthesizing information while applying expert knowledge. We introduce CURIE, a scientific long-Context Understanding,Reasoning and Information…
Quantum Many-Body Physics Calculations with Large Language Models
Haining Pan, Nayantara Mudur, Will Taranto +5
Large language models (LLMs) have demonstrated an unprecedented ability to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrat…
Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments
Shanqing Cai, Subhashini Venugopalan, Katie Seaver +13
Finding ways to accelerate text input for individuals with profound motor impairments has been a long-standing area of research. Closing the speed gap for augmentative and alternat…