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Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:08
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers
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.
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