READER: Reasoning-Enhanced AI-Generated Text Detection
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Computer Science > Computation and Language
Title:READER: Reasoning-Enhanced AI-Generated Text Detection
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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.25281 [cs.CL] |
| (or arXiv:2605.25281v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25281
arXiv-issued DOI via DataCite (pending registration)
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