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cs.LG2026
TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development
Jiarui Yan, Weiwei Sun, Sijie Li +2
Large language models write correct code for isolated problems but remain far weaker at autonomous machine-learning development, where an agent must revise data pipelines, models,…
cs.LG2026
Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection
Sijie Li, Shanda Li, Haowei Lin +3
Scaling laws are used to plan multi-million-dollar training runs, but fitting those laws can itself cost millions. In modern large-scale workflows, assembling a sufficiently inform…