Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering
This paper introduces HASTE, a hierarchical multi-agent system that organizes cross-competition knowledge into three scope tiers (global, domain, competition-specific), with LLM-driven abstraction between tiers. On the MLE-Bench Lite benchmark, HASTE achieves a 77.3% medal rate using Claude Sonnet 4.6 at 12h per competition. Warm starts use 52% fewer refinement iterations, suggesting that better knowledge organization can partly substitute for model strength and compute budget.
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[Submitted on 29 Jun 2026]
Title:Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering
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Abstract:ML engineering agents waste compute rediscovering known techniques because every competition is a cold start. We present HASTE, a hierarchical multi-agent system that organizes cross-competition knowledge into three scope tiers (global, domain, and competition-specific), each coupled to a matching agent level. An orchestrator coordinates domain specialists and promotes learning between tiers via LLM-driven abstraction. A controlled ablation provides evidence for scoped loading: holding a 159-skill inventory constant across 8 competitions, tiered loading achieves a 100% medal rate while flat loading reaches only 62.5%, the same medal rate as loading no skills, and consumes 2x the output tokens. On the full MLE-Bench Lite benchmark (22 Kaggle competitions), HASTE reaches a medal rate of 77.3% using Claude Sonnet 4.6 at 12h per competition. In a cold-start run, the system begins with no accumulated skills. In warm-start runs, it reloads skills learned from earlier competitions, using only global and domain-level skills for transfer across competitions. Warm starts use 52% fewer refinement iterations, and the fraction of proposed changes kept by the agent rises from 42% at low inventory to 85% once 50+ skills are available. These results suggest that better knowledge organization can partly substitute for model strength and compute budget in ML-engineering agents.
Comments: 19 pages. Accepted at the ICML 2026 Workshop on Deep Learning for Code (DL4C)
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2606.30911 [cs.AI]
(or arXiv:2606.30911v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30911
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yongbin Kim [view email] [v1] Mon, 29 Jun 2026 20:59:14 UTC (76 KB)
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