78 citations · 108 across the 12 of their papers we have counts for
13 papers
Structured Sparse Memory for Recurrent Reasoning
Zixuan Zhao, Samuel Wheeler, Neil Getty +3
Recurrent models trained from scratch have recently become competitive on ARC-style reasoning tasks, but the usual framing around small recurrent backbones overlooks two important…
OmniRAS: Standardizing Foundation Model Training and Evaluation in Robot-Assisted Surgery
Leonardo Borgioli, Neil Getty, Wenli Xiu +8
Few foundation models exist for robot-assisted surgery, partly because large robotic-surgery video corpora are difficult to assemble and existing models are evaluated mostly on lap…
BioAlchemy: Distilling Biological Literature into Reasoning-Ready Reinforcement Learning Training Data
Brian Hsu, Ozan Gökdemir, Carlo Siebenschuh +7
Despite the large corpus of biology training text, the impact of reasoning models on biological research generally lags behind math and coding. In this work, we show that biology q…
Adapting General-Purpose Foundation Models for X-ray Ptychography in Low-Data Regimes
Robinson Umeike, Neil Getty, Yin Xiangyu +1
The automation of workflows in advanced microscopy is a key goal where foundation models like Language Models (LLMs) and Vision-Language Models (VLMs) show great potential. However…
Automated MCQA Benchmarking at Scale: Evaluating Reasoning Traces as Retrieval Sources for Domain Adaptation of Small Language Models
Ozan Gokdemir, Neil Getty, Robert Underwood +5
As scientific knowledge grows at an unprecedented pace, evaluation benchmarks must evolve to reflect new discoveries and ensure language models are tested on current, diverse liter…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…