RASO (Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation)
RASO (Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation) released — a framework that optimizes agent skills by using existing skill repositories as base knowledge, rather than relying only on expensive agent rollouts. It runs in two stages: RASI builds initial skills grounded in existing knowledge without rollouts, and RASU retrieves external knowledge, guided by execution results, to improve skills step by step. Says it consistently beat baselines without retrieval-augmented initialization and updates across 4 agent benchmarks and 2 models. 16 pages. arXiv 2609.38024.
Why it matters
An approach that retrieves and adapts skills from repositories instead of learning them anew for each harness, turning skills from outputs tied to a model and harness into transferable assets. Read together with ClawScan's tightened skill vetting the same week, the premise that skills are goods in circulation is firming up from both sides.
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2609.38024Added in Week 41, 2026 · See that week