arXiv:2606. 28331v1 Announce Type: cross Abstract: The widespread deployment of generative artificial intelligence (AI) models has raised serious concerns about the proliferation of AI-generated content.
By Andr\'es F\'abrega, Arkaprabha Bhattacharya, Miranda Christ, Sunoo Park
arXiv:2606. 00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced.
By Shubhashis Sengupta, Benjamin McCarty, Milind Savagaonkar, Rhine Andotra
arXiv:2608.21363v1 Announce Type: new
Abstract: A protocol is presented for recording the governance decisions of automated AI runtimes. When a runtime releases, blocks, defers, redacts, or escalates...
By Ali Toygar Abak
arXiv:2608. 12713v1 Announce Type: cross Abstract: Watermarking LLM-generated text is an important task for tracing its provenance.
By Xiaoyan Feng, Yanjun Zhang, He Zhang, Leo Yu Zhang, Shirui Pan
The paper presents a systematic framework for large language model (LLM) watermarking as a provenance tool in big data ecosystems. It categorizes existing watermarking methods along four deployment dimensions—insertion point, verification authority, operational state, and transformation threat model—and aligns them with the big data principles of Volume, Velocity, Variety, Veracity, and Value. The authors introduce a readiness framework that maps four key workloads—online generation, streaming detection, transformation pipelines, and ecosystem governance—to system-level requirements such as throughput, false-positive control, robustness, cross-domain reliability, governance, and downstream utility, while highlighting gaps between benchmark performance and real-world deployment readiness.
By Huy Phan, Kieu Dang, Ojaswi Dulal, Aiham AL Shukairi, Abby Shine, Chase Garner, Phung Lai
arXiv:2502. 02068v3 Announce Type: replace-cross Abstract: This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development.
By Ruisi Zhang, Neusha Javidnia, Nojan Sheybani, Farinaz Koushanfar