READER: Reasoning-Enhanced AI-Generated Text Detection

• URL: http://arxiv.org/abs/2605.25281v2
• Date: Tue, 26 May 2026 16:00:26 GMT
• Title: READER: Reasoning-Enhanced AI-Generated Text Detection
• Abstract summary: We present READER, a reasoning-enhanced AI text detector that outputs both a human/AI label and a structured rationale describing the evidence for its decision.<n>A key component of our approach is READ, a curated supervision set of rationales and verdicts.<n> READER consistently outperforms existing detectors as well as prompted, high-capacity LLM baselines.
• Score: 9.295680825103183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have made it increasingly difficult to distinguish human-written text from AI-generated content. Many existing detectors train supervised neural classifiers that achieve strong in-distribution performance but are often opaque and can degrade substantially under distribution shift. We present READER, a reasoning-enhanced AI text detector that outputs both a human/AI label and a structured rationale describing the evidence for its decision. A key component of our approach is READ, a curated supervision set of rationales and verdicts. We fine-tune an LLM on READ to build READER, which reasons before detecting at inference time. Despite having only 1.5B parameters, READER consistently outperforms existing detectors as well as prompted, high-capacity LLM baselines (GPT-5.2, Gemini-3-Pro, and DeepSeek-V3.2), which are 100 to 1000 times larger in scale.

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