想了解智能体在机器学习开发中的局限性？TraceML分析了134场Kaggle竞赛中的人类和智能体工作轨迹，揭示了智能体在数据工作、验证、模型更改和集成等方面的不足，值得一读。
TraceML通过分析人类和智能体在134场Kaggle竞赛中的工作轨迹，揭示了智能体在自主机器学习开发中的局限性。研究发现，智能体在数据工作、验证、模型更改和集成等方面表现不如人类，且无法像人类一样灵活地调整策略。通过分析人类实践中的短期规划提示，TraceML将智能体的行为引导向人类模式，提高了分数，但努力模式仍保持智能体形状。相关数据集、方案、标签器和提取管道已发布在Hugging Face上。
TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development
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 alternate data work, validation, model changes, and ensembling, and return to approaches they had set aside. Each agent scaffold instead collapses into a narrow loop: Codex spends its steps re-weighting ensembles and tuning submissions, MLEvolve mutates its model in place, and neither pivots at the human rate nor reopens abandoned work. A short planning prompt distilled from human practice moves the behaviors it names toward the human profile and lifts scores, but the effort profile stays agent-shaped: instruction closes only the part of the gap that reduces to instructions. We release the corpus, the schema, the labelers, and the extraction pipeline at https://huggingface.co/datasets/jerryyan/TraceML.