# LeafSkill preprint reports higher agent task completion with fewer model calls

_Published Monday, October 26, 2026 at 6:06 AM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers report in a preprint that LeafSkill, a system that reuses executable actions across tasks, improved an AI agent's completion rate on 200 Robotouille tasks. It completed 137 tasks versus 121 for ReCAP, or 68.5% versus 60.5%.

LeafSkill retrieves related experience, adapts action templates to available objects and permitted actions, and uses a 1.7B selector to choose an action or return the subtask to the original agent. On the 106 tasks both systems completed, it reduced Qwen3.5-27B tokens by 14.42% and calls by 14.08%. Those resource savings apply to jointly completed tasks, rather than the entire test set.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2610.32412)

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Canonical: https://techandbusiness.org/newswire/pgrO0-G8ua-gYNE49QFqVW
Published: 2026-10-26T10:06:03.119Z
Story chronology: 2026-10-26T04:00:00.000Z
Retrieved: 2026-10-26T12:07:48.118Z
Publisher: Tech & Business (techandbusiness.org)