No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models

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

arXiv · 2510.03978

No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models

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

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Abstract

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 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.

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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. 2025-10-04. No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models. https://arxiv.org/abs/2510.03978

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