Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking

2026-07-06

AI Summary

This work addresses the fundamental trade-off in large language model watermarking between detection power and semantic distortion, as well as the reliance of existing approaches on heuristic hyperparameter tuning. The authors propose a power-calibrated statistical framework that, for the first time, establishes an explicit theoretical relationship among watermark hyperparameters, detection efficacy, and semantic fidelity. By formulating watermark design as a constrained optimization problem, the study shifts the paradigm from heuristic parameter selection to principled optimization. Leveraging a logit-based watermarking mechanism integrated with statistical power analysis and constrained optimization, the framework consistently identifies Pareto-optimal solutions across multiple language models and datasets, significantly outperforming current heuristic strategies.

Abstract

Logit-based watermarking is a widely used mechanism for identifying LLM generated content, yet its effectiveness is governed by a fundamental trade-off between detectability and semantic distortion. Existing analyses provide limited guidance for principled hyperparameter selection, leaving practical deployments reliant on heuristic tuning. In this work, we develop a power-calibrated statistical framework that establishes explicit quantitative relationships between watermark hyperparameters, detection power, and distortion. This characterization transforms watermark design into a guided optimization problem. Building on these results, we derive practical parameter selection procedures that achieve optimal tradeoffs under constraints. Extensive experiments across multiple language models and datasets validate the theory and demonstrate that the proposed framework consistently identifies Pareto-optimal points.

Problem

• LLM watermarking
• detectability
• semantic distortion
• hyperparameter selection
• heuristic tuning

Innovation

• power-calibrated watermarking
• logit-based watermarking
• detection power
• semantic distortion
• hyperparameter optimization