WaveHumanize: Localized Text Humanization with Wavelet Energy

Text humanization aims to make LLM-generated writing more human-like while preserving its meaning. Achieving this with minimal changes to the original text requires identifying which passages to revise and choosing suitable replacements. We propose **WaveHumanize**, a training-free framework that uses a shared wavelet-energy signal to guide both decisions. A pretrained language model scores the tokens in a document based on its next-token predictions. Wavelet analysis converts these scores into an energy profile aligned with text positions. We use this profile to select short spans for rewriting, and a second language model generates candidate replacements. Each candidate is inserted into the document and rescored, and the replacement with the highest average energy over its span is retained. The procedure requires no feedback from the target detector. In the primary evaluation across fifteen domains, WaveHumanize achieves greater detection reduction with less textual change than the compared limited-edit baselines. At thresholds calibrated to a 1% false-positive rate, an average of 80.8% of initially detected texts evade detection after rewriting. Further evaluation shows transfer to several additional detector families. Code is available at https://anonymous.4open.science/r/WaveHumanize-ICLR-C6FE.