TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development
[Uczenie maszynowe] Autorzy: Jiarui Yan, Weiwei Sun, Sijie Li i 2 więcej
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, and validation over hours of feedback, and on most competitions still finishes below strong human competitors. Outcome-based benchmarks record this gap but not its cause, because they grade the final submission and discard the development process behind it. We introduce TraceML, which pairs human and agent work on the same competitions under one version-level schema: 4,465 human Kaggle trajectories across 134 competitions, seven of which are also worked by two agent scaffolds, giving 430 paired human and 207 agent trajectories. Every code version carries its score, its timestamp, and labels for the action taken, its intent, the edit size, and the score effect. Read this way, the gap becomes concrete. Experts a…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe