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This content will become publicly available on March 15, 2026

Title: GaussMark: A Practical Approach for Structural Watermarking of Language Models
Award ID(s):
2031883
PAR ID:
10636020
Author(s) / Creator(s):
; ;
Publisher / Repository:
Forty-second International Conference on Machine Learning
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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