Test-time adaptation with semi-supervised learning leverages inference-time homogeneity to maintain AI text detection performance under adversarial humanization, new LLMs, and temporal drift.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
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An adversarial methodology generates a multilingual cross-platform dataset of paired human-AI social messages, and models trained on it outperform prior detectors on real-world out-of-distribution data.
Inverse Turing Bench evaluates LLMs on distinguishing human-human from human-AI dialogues, with GPTZero at 89.41%, Claude Opus-4.6 at 77.92%, and GPT-5.5 at 75.94% accuracy.
citing papers explorer
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Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
Test-time adaptation with semi-supervised learning leverages inference-time homogeneity to maintain AI text detection performance under adversarial humanization, new LLMs, and temporal drift.
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Adversarial Creation and Detection of AI-Generated Social Bot Content
An adversarial methodology generates a multilingual cross-platform dataset of paired human-AI social messages, and models trained on it outperform prior detectors on real-world out-of-distribution data.
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Inverse Turing Bench: Evaluating Language Models as Judges of Human vs. AI Dialogue
Inverse Turing Bench evaluates LLMs on distinguishing human-human from human-AI dialogues, with GPTZero at 89.41%, Claude Opus-4.6 at 77.92%, and GPT-5.5 at 75.94% accuracy.