32 papers
Auto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer
Jingjie Ning, Xiaochuan Li, Shanshan Zhong +2
Auto Research uses language-model agents to propose, implement, and evaluate machine-learning changes in a closed loop, but is usually judged by its terminal pipeline. A terminal s…
EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation
Nan Wang, Zhiyi Xia, Yiming Li +7
Quantitative microstructural characterization is fundamental to materials science, and electron micrographs (EMs) provide indispensable high-resolution insights. However, progress…
Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements
Jingjie Ning, Xiaochuan Li, Ji Zeng +2
Closed-loop Auto Research extends automated machine learning from fixed-dataset fitting to changing the research workflow, with language-model agents editing representations and mo…
Multi-Alignment Contrastive Learning for Enzyme--Reaction Retrieval
Gengmo Zhou, Feng Yu, Wenda Wang +4
Identifying enzymes that catalyze target biochemical reactions is a key step in computational enzyme discovery and biocatalyst design. Recent representation-learning methods formul…
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Yutang Ge, Guojiang Zhao, Sihang Li +7
Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tu…
MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining
Haoyu Dong, Pengkun Zhang, Mingzhe Lu +2
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machin…