arXiv:2605.25281v1 Announce Type: new 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 supervisio
The rapid advancement and widespread deployment of large language models have created an urgent need for robust methods to discern AI-generated content from human-written text.
The increasing sophistication of AI-generated text poses significant challenges to information integrity, academic honesty, cyber security, and the future of creative industries.
This development, READER, introduces a new paradigm for AI text detection by providing not only a label but also reasoning, enhancing transparency and potentially increasing resilience against adversarial attacks or distribution shifts.
- · Fact-checking organizations
- · Educational institutions
- · Cybersecurity firms
- · Content creators focused on authenticity
- · Malicious actors using AI for disinformation
- · Ghostwriting services reliant on undetectable AI
- · Platforms struggling to moderate AI-generated content
Improved detection capabilities will help mitigate some immediate risks associated with undetectable AI-generated content.
The demand for more robust and explainable AI detection tools will increase, driving further research and development in this domain.
The arms race between AI generation and detection could lead to a 'turing test' for text, where the most advanced AIs are those capable of consistently fooling the best detectors.
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