Papers by Yanwei Huang
Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers (2026.acl-demo)
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| Challenge: | Existing post-training pipelines that generate QA pairs require costly expert annotation and synthetic data that drops evidence structure. |
| Approach: | They propose a system that converts raw biomedical papers into evidence-enriched training sets and a domain-specialized VLM. |
| Outcome: | Ryze synthesizes QA pairs with complete supporting evidence, reduces layout and OCR errors . the system outperforms the base model on LAB-Bench and surpasses GPT-5.2 by +3.8%. |
PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data (2026.findings-acl)
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| Challenge: | Existing datasets with verifiable answers are limited in reliability, diversity, and scalability . a new approach to generate verifikatable data at scale is needed to improve models' performance . |
| Approach: | They propose a formal framework for synthesizing verifiable data at scale using a novel DSL-driven approach. |
| Outcome: | The proposed framework improves performance on a wide range of puzzles and logic benchmarks. |