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Tung-Ling Li

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#natural language process... Preprint Oct 2026

Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text

We introduce Tokenization-Invariant Watermarking (TI-WM), a method that embeds a watermark during token generation but keys and scores it on canonicalized UTF-8 bytes. The detector does not invoke the tokenizer. Canonicalizer-preserving edits leave its score unchanged; other localized canonical-byte edits affect a boun...

Tung-Ling Li · 0 citations

#machine learning Preprint Sep 2026

Scale-QLoRA: Code-Invariant Adapter Merging for Native 4-bit Microscaling LLMs

Scale-QLoRA and merge-aware QAT-LoRA are both accuracy-lossless, so they claim no accuracy ordering between them; they differ structurally, in that QAT-LoRA re-derives the code plane through a quantizer while Scale-QLoRA preserves it exactly.

Tung-Ling Li, Jia-Le Huang, Lee-Chi Wang et al. · 1 citation