AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning, tool use, and verification. Scaling such agentic science remains difficult because workflows are hard to observe and reproduce, many scientific tools and laboratory systems are not agent-ready, and execution trac...
Lin-Feng Zhang, Si-Heng Chen, Yu-Zhu Cai et al.· AI Plus· 0 citations
BioDataLab evaluates the capability of autonomous agents to transform raw, heterogeneous biological resources into structured, analysis-ready databases, and underscores that while LLMs are proficient in downstream reasoning, autonomous upstream curation remains a formidable frontier.
Jiaxian Yan, Xi Fang, Jintao Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
High-quality biological databases are the bedrock of data-driven scientific discovery. However, the construction of these resources remains a labor-intensive bottleneck, particularly for emerging research frontiers where structured data is non-existent. While LLM-based agents have catalyzed progress in downstream scien...
Jiaxian Yan, Xi Fang, Jintao Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
Uni-XAS is presented, a unified benchmark and learning framework that reframes bidirectional XAS modeling as a cross-modal alignment and conditional generation problem, and introduces Permutation-Rectified Flow Matching, which integrates type-wise optimal transport into a continuous generative flow to provide a princip...
Suyang Zhong, Yuhao Zhao, Boying Huang et al.· 0 citations
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