SkillSight, a training-free retrieval framework that calibrates shared background in both semantic and lexical spaces and achieves the best overall performance across three agent models and outperforms LLM Selection by up to 4.97 percentage points.
SkillReason-Bench is introduced, a large-scale cross-domain benchmark containing 3,729 queries and a retrieval corpus of 61,228 skills spanning nine domains and SkillRea- son is proposed, a two-stage framework that uses chain-of-thought rea- soning as training-time supervision for skill retrieval.
Donghong Jiang, Endian Lin, Luoping Cui et al.· 2 citations
Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agents on real-world tasks. Community-maintained skill libraries built around this interface are growing rapidly. However, this ecosystem remains deeply English-centric: ou...
Yi-Lun Liu, Shi-Min Tao, Ming-Gui He et al.· 0 citations
This work formalizes the task as structured prediction of (skill, level) pairs from free text and asks three questions: how accurately can text be mapped onto SFIA's closed vocabulary, which strategies reliably predict the level alongside the skill, and do agentic designs improve on simpler retrieval and prompting?
Ranuga Disansa, U. Samarasinghe, Lasith Gunawardena· 0 citations
This work proposes SkillDreamer, a novel framework that first infers the capabilities necessary for task execution, then imagines how to realize these capabilities by generating pseudo skills, and finally leverages such prospective information to bridge the gap between objective-oriented task queries and execution-orie...
Shuo Liu, Yu-Tong Yang, Haonan Xiao et al.· 3 citations
seek, Self-Evaluative Exploration for Knowledge Retrieval, a training-free framework that addresses this limitation through iterative corpus interaction at test time through iterative corpus interaction at test time.
While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations. Retrieval offers a practical way to reuse existing demonstrations for data-efficient a...
Haoran Hao, Shahram Najam Syed, Jeff G. Schneider et al.· 0 citations
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