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New SE-GoS framework enhances LLM agent skill retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SE-GoS, a novel framework designed to enhance the efficiency of Large Language Model (LLM) agents by improving skill retrieval. This training-free approach evolves existing skill graphs using historical execution traces, optimizing topology, edge weights, and skill descriptions. Experiments on SkillsBench with three different LLMs demonstrated that SE-GoS consistently boosts task rewards and reduces token usage compared to static skill loading, with improvements transferring effectively to new tasks. AI

IMPACT Improves LLM agent efficiency by optimizing skill retrieval, potentially reducing computational costs and enhancing performance on complex tasks.

RANK_REASON Academic paper detailing a new framework for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SE-GoS framework enhances LLM agent skill retrieval

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Academic paper detailing a new framework for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dawei Fu, Cheng Jiang, Sitian Qian, Huainan Wang, Zhongkai Hao ·

    SE-GoS: Self-Evolving Graph-of-Skills for Skill Library at Scale

    arXiv:2609.08228v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on reusable skills, yet as skill libraries scale to thousands of entries, effective retrieval becomes a bottleneck. Graph-of-Skills (GoS) addresses this challenge by exploiting dependency-aware gr…

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