@inproceedings{huang-etal-2026-ryze,
title = "Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers",
author = "Huang, Yeqi and
Chen, Yue and
Ye, Yanwei and
Su, Guanhao and
Mai, Luo",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.73/",
pages = "743--749",
ISBN = "979-8-89176-392-0",
abstract = "General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.Existing post-training pipelines are bottlenecked by costly expert annotation and by synthetic data that drops this evidence structure.We present Ryze, a fully automated system that converts raw biomedical papers into an \textit{evidence-enriched} training set and a domain-specialized VLM.Ryze synthesizes QA pairs with complete supporting evidence (visual element, caption, extracted structure, and referring paragraphs), reduces layout and OCR errors via chart/table-aware extraction and LLM-based cleansing, and applies a two-stage post-training strategy combining supervised fine-tuning with reinforcement learning.Starting from Qwen3-VL-8B, Ryze produces BioVLM-8B at under {\$}200, achieving 48.0{\%} weighted accuracy on LAB-Bench{---}outperforming the base model by +12.6{\%} and surpassing GPT-5.2 by +3.8{\%}.We release Ryze as open source together with the trained BioVLM-8B model."
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<abstract>General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.Existing post-training pipelines are bottlenecked by costly expert annotation and by synthetic data that drops this evidence structure.We present Ryze, a fully automated system that converts raw biomedical papers into an evidence-enriched training set and a domain-specialized VLM.Ryze synthesizes QA pairs with complete supporting evidence (visual element, caption, extracted structure, and referring paragraphs), reduces layout and OCR errors via chart/table-aware extraction and LLM-based cleansing, and applies a two-stage post-training strategy combining supervised fine-tuning with reinforcement learning.Starting from Qwen3-VL-8B, Ryze produces BioVLM-8B at under $200, achieving 48.0% weighted accuracy on LAB-Bench—outperforming the base model by +12.6% and surpassing GPT-5.2 by +3.8%.We release Ryze as open source together with the trained BioVLM-8B model.</abstract>
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%0 Conference Proceedings
%T Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers
%A Huang, Yeqi
%A Chen, Yue
%A Ye, Yanwei
%A Su, Guanhao
%A Mai, Luo
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F huang-etal-2026-ryze
%X General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.Existing post-training pipelines are bottlenecked by costly expert annotation and by synthetic data that drops this evidence structure.We present Ryze, a fully automated system that converts raw biomedical papers into an evidence-enriched training set and a domain-specialized VLM.Ryze synthesizes QA pairs with complete supporting evidence (visual element, caption, extracted structure, and referring paragraphs), reduces layout and OCR errors via chart/table-aware extraction and LLM-based cleansing, and applies a two-stage post-training strategy combining supervised fine-tuning with reinforcement learning.Starting from Qwen3-VL-8B, Ryze produces BioVLM-8B at under $200, achieving 48.0% weighted accuracy on LAB-Bench—outperforming the base model by +12.6% and surpassing GPT-5.2 by +3.8%.We release Ryze as open source together with the trained BioVLM-8B model.
%U https://aclanthology.org/2026.acl-demo.73/
%P 743-749
Markdown (Informal)
[Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers](https://aclanthology.org/2026.acl-demo.73/) (Huang et al., ACL 2026)
ACL
- Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, and Luo Mai. 2026. Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 743–749, San Diego, California, United States. Association for Computational Linguistics.