Provable Model Provenance Set for Large Language Models

The rapid proliferation of customized large language models has intensified concerns over unauthorized model reuse, highlighting the need for reliable model provenance techniques for model auditing and tracking. However, existing work largely infers provenance relationships through heuristic fingerprint‑matching rules and lack of provably valid error control, leaving the credibility of provenance claims unverified. In this work, we formalize model provenance as a set‑valued inference problem and propose the Model Provenance Set (MPS), a novel principled method to adaptively construct a provenance set with rigorous coverage guarantee, i.e., covering the source models at a specified confidence level. Specifically, we introduce a novel equivalence test that yields a valid $p$-value under the null hypothesis that all candidates are equally close to the target model, and develop a test-and-exclusion procedure to sequentially select the most probable source candidate until the null is rejected. By establishing the sequential structure of the test, MPS constructs a subset of candidates with a provable coverage guarantee. Extensive experiments demonstrate that MPS can effectively identify all source models with the intended coverage while strictly avoiding the inclusion of unrelated models.