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PL-HCL: Detecting Cross-Layer Misalignment in LLM Agent Skills

Jul 14, 2026

Researchers introduce Progressive Loading-Aware Hierarchical Contrastive Learning (PL-HCL), a framework for detecting inconsistencies between the descriptions and actual behaviors of large language model (LLM) agent skills. Evaluated on a corpus of over 264,000 open-source skills and a human-verified challenge set, PL-HCL raises Macro-F1 scores from around 0.45 (for unadapted baselines) to 0.87–0.89 across different LLM backbones. This demonstrates a substantial improvement in identifying misaligned agent skills.

Why it matters: As open-source skill marketplaces expand, PL-HCL provides an effective tool to help users and operators screen for agent skills that may not behave as advertised, improving trust and safety.

Full story at: arXiv Cryptography and Security ↗

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