TokenPrint: A Calibrated Token-Space Fingerprint for Language-Model Provenance
Establishing the provenance of a language model---including its base checkpoint and possible overlap in training distributions---is a governance challenge that metadata alone cannot resolve. We introduce a training-free fingerprint based on the top-$k$ vocabulary projections of late hidden states elicited by 250 fixed knowledge probes, compared using Jaccard overlap over decoded token strings. We evaluate the method on 32 open-weight models from nine families (0.6B--32B) with documented relationships. (1)~A \emph{similarity ladder} broadly follows model relatedness: independently trained models on identical data score 0.48 raw (0.35 vocabulary-corrected), followed by shared-base fine-tune...
| Authors | Yuqi Wu, Shengming Zhao, Jie Chen |
|---|---|
| Primary category | cs.AI |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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