A general approach to progressive intelligence. - Joshua T. Vogelstein; Hayden S. Helm; Ronak D. Mehta; Jayanta Dey; Weiwei Yang 004; Bryan Tower; Will LeVine; Jonathan Larson; Christopher M. White; Carey E. Priebe (CoRR, 2020)

Out-of-distribution Detection Using Kernel Density Polytopes. - Jayanta Dey; Ashwin De Silva; Will LeVine; Jong M. Shin; Haoyin Xu; Ali Geisa; Tiffany Chu; Leyla Isik; Joshua T. Vogelstein (CoRR, 2022)

Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models. - Will LeVine; Benjamin Pikus; Pranav Raj; Fernando Amat Gil (CoRR, 2023)

A Baseline Analysis of Reward Models' Ability To Accurately Analyze Foundation Models Under Distribution Shift. - Benjamin Pikus; Will LeVine; Tony Chen; Sean Hendryx (CoRR, 2023)

Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy. - Will LeVine; Benjamin Pikus; Jacob Phillips; Berk Norman; Fernando Amat Gil; Sean Hendryx (CoRR, 2024)

Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking. - Will LeVine; Bijan Varjavand (CoRR, 2025)

Simple Lifelong Learning Machines. - Joshua T. Vogelstein; Jayanta Dey; Hayden S. Helm; Will LeVine; Ronak D. Mehta; Tyler M. Tomita; Haoyin Xu; Ali Geisa; Qingyang Wang 002; Gido M. van de Ven; Chenyu Gao; Weiwei Yang 004; et al. (IEEE Trans. Pattern Anal. Mach. Intell., 2025)