Token rankings from language models are unique and NP-hard to forge, providing the first polynomially unforgeable model signature.
Are You Getting What You Pay For? Auditing Model Substitution in
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A method infers conservative lower bounds on LLM parameter counts from next-token accuracy profiles on popular texts using pairwise tests and PCA-based scaling-law estimation.
Proposes referential security as a paradigm for AI evaluations that reframes model identity as verifiable to support reproducible audits and regulatory decisions despite system changes.
citing papers explorer
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Token Rankings are Unforgeable Language Model Signatures
Token rankings from language models are unique and NP-hard to forge, providing the first polynomially unforgeable model signature.
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Inferring the Size of Large Language Models From Popular Text Memorization
A method infers conservative lower bounds on LLM parameter counts from next-token accuracy profiles on popular texts using pairwise tests and PCA-based scaling-law estimation.
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Referential Security as a New Paradigm for AI Evaluations
Proposes referential security as a paradigm for AI evaluations that reframes model identity as verifiable to support reproducible audits and regulatory decisions despite system changes.