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Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models — arXiv:2610.11163

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.11163
License (as archived): CC0-1.0
Archived: 2026-10-09T05:57:13.891Z
SHA-256 of the archived text: 5daa82c652ce8b4f63c14dd04362a4dfdca31d68e34e8d78547b59ccb8215352
Stable URL of this page: https://metraipolis.com/documento/dc_7171bf48856e

How to cite

Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models — arXiv:2610.11163 — arXiv (en), https://arxiv.org/abs/2610.11163 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7171bf48856e (Metraipolis archive dc_7171bf48856e, sha256 5daa82c652ce8b4f63c14dd04362a4dfdca31d68e34e8d78547b59ccb8215352)

BibTeX

@misc{dc7171bf48856e,
  title = {Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models — arXiv:2610.11163},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.11163},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7171bf48856e; sha256 5daa82c652ce8b4f63c14dd04362a4dfdca31d68e34e8d78547b59ccb8215352},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.11163. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Third-party challenge-response identity verification (TP-CRIV) enables an independent verifier to assess whether a claimant possesses a model identical to a remotely deployed model without directly accessing the reference model. However, for probabilistic AI models, repeated executions of the same query may produce different outputs and therefore different verification observations. This raises the question of how such stochastic evidence should be accumulated and how much evidence is required for reliable verification. In this work, we characterize statistical separability in TP-CRIV of probabilistic AI models. Specifically, we relate challenge-wise behavior of matching and non-matching provers to verification-level separability. The characterization explicitly describes how the numbers of independent challenges and repeated responses affect detection performance and enables the verification budget required for a target AUC to be estimated. We instantiate the proposed characterization for LLMs using open-ended challenges. The experiments demonstrate matching-non-matching separation, close agreement between theoretical and empirical AUCs, and consistent estimates of the minimum verification budgets. These results provide a statistical basis for relating probabilistic model behavior to verification-level separability and the evidence required for third-party verification.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.11163 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:47:01.515Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).