TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development
Score
76首次提供版本级人机轨迹对比数据，揭示过程性差异，超越传统结果基准的信息增量显著。
实验设计严谨，数据规模大，标注schema清晰，分析有据且结论具有可验证性。
为Agent开发者和AutoML研究者提供可直接使用的数据集与诊断方法，有助于改进智能体规划能力。
当前Agent自主完成复杂任务的能力备受关注，该研究切中热点，时效性强。
来自arXiv预印本，虽未经同行评审，但方法透明、数据开源，信源可信度较高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
Introduces a novel dataset and methodology quantifying human-agent gaps from process traces rather than final outcomes, with significant implications for agent alignment and automated ML.
AI commentary
「What makes this paper worth reading is that it turns the human-agent gap from scores into behavioral trajectories, letting us see exactly how agents get stuck in narrow loops over long tasks. For engineers working on agent reasoning, planning, or AutoML, this open-source dataset may be more valuable than a pile of benchmarks. Pair it with human trajectories and prompt design becomes far more grounded.」
Quick overview
Large language models write correct code for isolated problems but remain far weaker at autonomous machine-learning development. Outcome-based benchmarks record the gap but not its cause. TraceML pairs human and agent work under a version-level schema: 4,465 human Kaggle trajectories across 134 competitions, plus 430 paired human and 207 agent trajectories from seven competitions worked by two agent scaffolds. Every code version carries score, timestamp, and labeled action, intent, edit size, and score effect. Experts alternate strategies and revisit abandoned approaches, while agents collapse into narrow loops. A short planning prompt distilled from human practice moves behaviors closer to humans but leaves the effort profile agent-shaped.
- TraceML includes 4,465 human Kaggle trajectories and 207 agent trajectories across 134 competitions, with every version labeled for score, timestamp, action, and intent.
- Experts flexibly switch among data work, validation, model changes, and ensembling, and revisit abandoned approaches, while both agent scaffolds collapse into narrow loops.
- A short planning prompt distilled from human practice improves scores but leaves the effort profile agent-shaped, showing instruction closes only part of the gap.
- The corpus, schema, labelers, and extraction pipeline are publicly released for further research.
Full text
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