HASTE shows how to organize skills for ML agents
The material on HASTE reports that, across eight competitions, hierarchical skill loading achieved a 100% medal rate, compared with 62.5% for flat loading.
Source: HASTE organiza skills reutilizáveis para agentes de engenharia de ML (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on July 5, 2026, the material describes HASTE, a hierarchical multi-agent system that organizes skills at global, domain, and competition-specific levels. In an ablation with 159 skills across eight competitions, tiered loading achieved a 100% medal rate, compared with 62.5% for flat loading. The results indicate that how accumulated skills are organized and loaded can affect performance and token use.
A company operating internal agents could test a similar structure: separate shared instructions and tools from those specific to each domain or task, set loading criteria, and monitor performance, token use, and errors. A controlled evaluation on representative tasks can help determine whether the hierarchy improves outcomes in the company’s context.
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Wendelmaques can assess an agent architecture and its use of skills, define an evaluation suited to the company’s tasks, and propose a scoped implementation, including monitoring and operation.
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