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This paper proposes TASCO, a framework for test-time adaptation in LLMs that incorporates local stability into confidence-based optimization to improve reasoning accuracy and token efficiency without updating model parameters.
This paper introduces Pattern Stability Score (PSS), a novel framework for robust watermark detection in machine-generated text, achieving 10-15 percentage point AUC improvements and generalizing across different LLMs and paraphrasers.