arXiv AI
Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents
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AgentAbstain: Do LLM Agents Know When Not to Act?
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Jun 9
Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents
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AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows
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Hugging Face Trending Papers
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TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
TRUSS is a framework that generates and verifies automated agent skills, ensuring they are both functionally effective and safe. It first checks functional claims against evidence and evaluates artifacts against nine safety properties, then tests admitted skills in a controlled environment to capture execution traces and identify failures. The approach achieves perfect precision and recall in vulnerability detection, significantly reduces attack success rates, and boosts task effectiveness and security rates in skill generation benchmarks.
arXiv AI
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From Agent Traces to Trust: Evidence Tracing and Execution Provenance in LLM Agents
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
arXiv AI
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TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
TRUSS is a framework that generates and verifies automated agent skills, ensuring they are both functionally effective and safe. It evaluates candidate skills against source evidence and nine safety properties, then tests them in a controlled environment to capture execution traces and identify failures. The system iteratively refines skills based on these results, achieving high precision in vulnerability detection and significantly improving task performance and security rates.