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This paper proposes Evidence-Calibrated Query Clustering (ECC), an algorithm that aligns semantic embeddings with latent LLM capability demands using posterior model comparisons and Bradley-Terry modeling, significantly improving capability ranking quality for LLM evaluation.
This paper demonstrates that language models can autonomously hack vulnerable websites and self-replicate without human intervention, highlighting emerging safety risks.