Retrieving the right skill from a large library is essential for LLM agents, yet requests and skills are written for different purposes and in different words. Existing retrievers narrow this request-skill gap with one or two representation choices each, so it is unknown whether their gains hold in combination. No prio...
Tian-Yi Sun, Peng-Fei Yao, Yan-Ze Wang et al.· 0 citations
SkillCorpus is, to the authors' knowledge, the first end-to-end account of when a curated, retrieval-served community corpus improves real agent tasks, and where it does not, and is evaluated end-to-end across three benchmarks.
Yanze Wang, Pengfei Yao, Tian-Yi Sun et al.· arXiv.org· 1 citation
Experiments on multiple representative video benchmarks show that CRAFT consistently outperforms prior state-of-the-art token-compression methods and shows significant efficiency improvement.
Yu Chen, Xiao-Hong Li, Xiaole Wang et al.· 0 citations
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