11 papers
Formally Solving Answer-Construction Problems in Lean
Jialiang Sun, Yuzhi Tang, Ao Li +2
Large language models (LLMs) have achieved remarkable progress in formal mathematical reasoning. Mathematical competition problems fall into two broad types: theorem-proving proble…
Integral Field Unit Spectroscopy with One Fiber
Zehao Peng, Biprateep Dey, Chris J. Maddison +1
Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost lim…
Causal Risk Minimization for High-Dimensional Treatments
Nikita Dhawan, Arnav Paruthi, Andrew Kim +3
Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movem…
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
Nikita Dhawan, Daniel Shen, Leonardo Cotta +1
Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis helps us determine how robust o…
MixMin: Finding Data Mixtures via Convex Minimization
Anvith Thudi, Evianne Rovers, Yangjun Ruan +2
Modern machine learning pipelines are increasingly combining and mixing data from diverse and disparate sources, e.g., pre-training large language models. Yet, finding the optimal…
BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
Adibvafa Fallahpour, Andrew Magnuson, Purav Gupta +8
Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel…