Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering
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 to...
| Authors | Yongbin Kim, Yashar Talebirad, Osmar R. Zaiane |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 1 July 2026 |
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