Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection
arXiv:2606.06481v1 Announce Type: new Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing. However, existing AI-text detection benchmarks largely focus on final outputs and provide limited understanding of how AI authorship signals emerge, accumulate, or disappear throughout the revision process. We introduce OpAI-Bench, an operation-guided benchmark for studying progressive human-to-AI text transformation acro
The increasing sophistication and integration of AI writing assistants necessitate new methods for detecting AI authorship in co-edited documents, moving beyond simple final output analysis.
This development is crucial for maintaining academic integrity, ensuring transparency in content creation, and refining intellectual property frameworks in an age of human-AI collaboration.
The focus of AI-text detection shifts from binary classification of final outputs to a more nuanced, process-oriented understanding of how AI contributions evolve during revision.
- · AI-text detection companies
- · Academic institutions
- · Content integrity platforms
- · Essay mills
- · Undetectable AI content generators
Improved detection of AI-generated content in progressively human-AI edited texts.
Development of new tools and policies for attributing authorship in mixed-source documents.
A potential redefinition of originality and intellectual property in an era of seamless human-AI co-creation.
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Read at arXiv cs.CL