{"ID":2860707,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03978","arxiv_id":"2510.03978","title":"No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models","abstract":"Embedding vision-language models (VLMs) are typically pretrained with short text windows (\u003c77 tokens), which forces the truncation of long-format captions. Yet, the distribution of biomedical captions from large-scale open source literature reveals that a huge portion of captions far exceed 77 tokens. To this end, we investigate the impact of pretraining on long-format biomedical captions by extending the context length of text encoders in VLMs. We find that longer context (thus, enabling additional supervision provided in long-format captions) correlates with better retrieval and classification performance. Given this finding, we introduce BIOMEDICA-LongCAP, a dataset of 1M image-caption pairs enriched with context-aware descriptions from full-text articles, providing longer and additional textual supervision. Using BIOMEDICA-LongCAP, we train BMC-LongCLIP, a long-context biomedical VLM with a text encoder supporting windows of up to 512 tokens. Our model extends context capacity by 6.6x, reducing token waste from 55% to just 2.2%. On long-caption retrieval benchmarks, BMC-LongCLIP achieves up to +30% absolute gains in Recall@1 and +2% average improvements in classification, while also converging faster than short-context. Our results demonstrate that long-context modeling is a promising direction for advancing biomedical VLMs.","short_abstract":"Embedding vision-language models (VLMs) are typically pretrained with short text windows (\u003c77 tokens), which forces the truncation of long-format captions. Yet, the distribution of biomedical captions from large-scale open source literature reveals that a huge portion of captions far exceed 77 tokens. To this end, we i...","url_abs":"https://arxiv.org/abs/2510.03978","url_pdf":"https://arxiv.org/pdf/2510.03978v1","authors":"[\"Min Woo Sun\",\"Alejandro Lozano\",\"Javier Gamazo Tejero\",\"Vishwesh Nath\",\"Xiao Xiao Sun\",\"James Burgess\",\"Yuhui Zhang\",\"Kun Yuan\",\"Robert Tibshirani\",\"Sean Huver\",\"Serena Yeung-Levy\"]","published":"2025-10-04T23:38:18Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}