CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models

The expanding capabilities of Large Language Models (LLMs) have enabled their application in increasingly diverse and sophisticated generation tasks Zhao et al. (2025), from acting as AI agents that produce structured data to solving complex scientific problems and writing functional code Chen et al. (2021); Guo et al. (2024). However, this proliferation of high-quality, machine-generated content poses formidable challenges for authenticity verification Burrus et al. (2024); A yoobi et al. (2024) and the prevention of misuse A yoobi et al. (2023); Dammu et al. (2024). Text watermarking, which embeds imperceptible statistical signals into generated text, has emerged as a promising solution for establishing content provenance Liu et al. (2024); Chen et al. (2023); Y oo et al. (2023). The dominant paradigm involves augmenting the model's output logits; a foundational method, for example, partitions the vocabulary into "green" and "red" lists and adds a positive bias to the logits of green-listed tokens to embed a detectable signature Kirchenbauer et al. (2023). Initial research quickly identified a primary limitation of this approach: its performance degrades significantly in low-entropy contexts, such as code generation, where modifying deterministic tokens can corrupt functional correctness. To address this, subsequent work has focused on entropy-aware adaptations. SWEET Lee et al. (2023) introduced a static entropy threshold, selectively applying the watermark only to high-entropy tokens to preserve low-entropy syntactic structures. Building on this, EWD Lu et al. (2024) refined the detection process by assigning weights to tokens proportional to their entropy, improving sensitivity without a hard threshold. While these methods marked important progress for single-domain tasks, they addressed only part of the problem.