14 papers
Scaling medical imaging report generation with multimodal reinforcement learning
Qianchu Liu, Sheng Zhang, Guanghui Qin +11
Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit major competency gaps in multimodal unde…
OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning
Timothy Ossowski, Sheng Zhang, Qianchu Liu +5
High-quality and carefully curated data is a cornerstone of training medical large language models, as it directly impacts both generalization and robustness to unseen clinical tas…
Be My Eyes: Extending Large Language Models to New Modalities Through Multi-Agent Collaboration
James Y. Huang, Sheng Zhang, Qianchu Liu +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in challenging, knowledge-intensive reasoning tasks. However, extending LLMs to perceive and reason over a ne…
ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation
Qin Liu, Jacob Dineen, Yuxi Huang +4
Benchmarks are central to measuring the capabilities of large language models and guiding model development, yet widespread data leakage from pretraining corpora undermines their v…
Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale
Cliff Wong, Sam Preston, Qianchu Liu +22
A significant fraction of real-world patient information resides in unstructured clinical text. Medical abstraction extracts and normalizes key structured attributes from free-text…
Exploring Scaling Laws for EHR Foundation Models
Sheng Zhang, Qin Liu, Naoto Usuyama +3
The emergence of scaling laws has profoundly shaped the development of large language models (LLMs), enabling predictable performance gains through systematic increases in model si…