arXiv AI By Alexander Nemecek, Vipin Chaudhary, Erman Ayday

Watermarks Without Verification: AI Text Watermarking After the EU AI Act

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The paper discusses the EU AI Act’s requirement for generative AI providers to embed detectable watermarks in their outputs, noting that Anthropic’s Claude models and Google’s Gemini use SynthID‑Text by default. It critiques the lack of verifiability of claims about watermark quality, privacy, and robustness, and evaluates the open‑source SynthID‑Text implementation on two open‑weight models, finding minimal impact on prose and modest correctness loss on code. The authors argue that the real governance issue is the inability to verify these assertions and outline necessary steps—such as output release, configuration disclosure, accredited audits, shared evaluation protocols, and interoperable detection—to address the gaps.

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arXiv Computation and Language
Sep 3

LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization

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