SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse
arXiv cs.AIen
arXiv:2608.05204v1 Announce Type: new Abstract: LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workflows. As skills become marketplace artifacts, auditing their reuse is no longer the same problem as ordinary code clone detection. Existing detectors target single-modality source code or whole-package similarity, yet skill reuse evidence is distributed across authored text, implementation fragments, and operational structure. As a result, they can miss reuse that preserves only one part of a skill. We present SKILLTRACE, a multi-trace provenance auditing framework for
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Verktyg
- Forskning
- Agenter
Related AI news
- 不用關掉 ChatGPT,Adobe 一口氣整合 70 項自家工具進 AITechNews (TW) · August 7, 2026
- Chinese AI firms push Hong Kong data center leasingTech in Asia · August 7, 2026
- Backed by DeepSeek, Unitree IPO tests investor appetite for China’s AI robotics boomSCMP Tech · August 7, 2026
- The Ignition Index: Measuring Global Workspace Dynamics in Language ModelsarXiv cs.AI · August 7, 2026
- Otter: A Time-Aware, History-Conditioned Human Chess AIarXiv cs.AI · August 7, 2026
- Project2Task: Graph-Guided Project-Level Planning for Autonomous ResearcharXiv cs.AI · August 7, 2026