2 citations · 2 across the 5 of their papers we have counts for
10 papers
PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
James Burgess, Jan N. Hansen, Duo Peng +5
Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinf…
The Impact of Image Resolution on Biomedical Multimodal Large Language Models
Liangyu Chen, James Burgess, Jeffrey J Nirschl +2
Imaging technologies are fundamental to biomedical research and modern medicine, requiring analysis of high-resolution images across various modalities. While multimodal large lang…
No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models
Min Woo Sun, Alejandro Lozano, Javier Gamazo Tejero +8
Embedding vision-language models (VLMs) are typically pretrained with short text windows (<77 tokens), which forces the truncation of long-format captions. Yet, the distribution of…
Can Large Language Models Match the Conclusions of Systematic Reviews?
Christopher Polzak, Alejandro Lozano, Min Woo Sun +4
Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based…
A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI
Alejandro Lozano, Min Woo Sun, James Burgess +16
Despite the excitement behind biomedical artificial intelligence (AI), access to high-quality, diverse, and large-scale data - the foundation for modern AI systems - is still a bot…
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research
James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez +20
Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (…