Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection
- Published
- Source
- arXiv
- Paper number
- 330
- Field
- LLMs / NLP
- arXiv ID
- 2606.06481
Key points
- The paper proposes OpAI-Bench, a benchmark that models the nine-stage progressive human-to-AI co-editing process.
- It evaluates AI-text detectors at four granularities: document, sentence, token, and span.
- It covers five representative AI editing operations across four domains.
- It finds non-monotonic patterns in which intermediate versions of mixed-authorship text are harder to detect than either fully human text or high-ratio AI text.
- AI detectability depends on the editing ratio, operation type, domain, and cumulative history.
Paper links
External research summaries. These are not HDATF publications or measured product results.