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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.CL English(EN) · · [2 sources]

    SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents

    Researchers have developed SenFlow, a novel method for detecting AI-generated text in documents co-authored by humans and AI. Unlike previous approaches that analyze sentences in isolation, SenFlow models inter-sentence dependencies by treating detection as a structured prediction problem. The method was evaluated on MOSAIC, a new benchmark comprising 16,000 hybrid documents generated by DeepSeek V3.2 and Kimi K2, and achieved state-of-the-art performance. AI

    IMPACT This research could lead to more robust detection of AI-generated content, impacting content authenticity and academic integrity.