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Mixed-authorship documents can be harder to detect than purely human or AI-generated texts, challenging existing assumptions about AI-text detection.
Language agents can achieve more diverse and effective self-reflection by encoding cross-sample reflection patterns directly into model parameters, leading to significant performance gains in reasoning tasks.
