The growing prevalence of unauthorized model usage and misattribution has increased the need for reliable model provenance analysis. However, existing methods largely rely on heuristic fingerprint-matching rules that lack provable error control and often overlook the existence of multiple sources, leaving the reliability of their provenance claims unverified. In this work, we first formalize the model provenance problem with provable guarantees, requiring rigorous coverage of all true provenances at a prescribed confidence level. Then, we propose the Model Provenance Set (MPS), which employs a sequential test-and-exclusion procedure to adaptively construct a small set satisfying the guarantee. The key idea of MPS is to test the significance of provenance existence within a candidate pool, thereby establishing a provable asymptotic guarantee at a user-specific confidence level. Extensive experiments demonstrate that MPS effectively achieves target provenance coverage while strictly limiting the inclusion of unrelated models, and further reveal its potential for practical provenance analysis in attribution and auditing tasks.
Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models
IEM Kolkata（印度工程管理学院 Kolkata校区）；School of UEMK Kolkata（UEMK Kolkata学院）；Heritage Institute of Technology Kolkata（加尔各答遗产技术学院）；Indian Institute of Information Technology, Kalyani（卡利亚尼印度信息技术学院）
Provable Training Data Identification for Large Language Models
Department of Statistics and Data Science, Southern University of Science and Technology（统计与数据科学系，南方科技大学）；Shanghai Innovation Institute（上海创新研究院）；School of Computer Science, Shanghai Jiao Tong University（计算机科学学院，上海交通大学）
Capability Provenance in Language Models: A Case Study in Social Reasoning
Georgia Institute of Technology, College of Computing（佐治亚理工学院计算学院）；MATS Program（MATS项目）；EleutherAI；KAIST AI（韩国科学技术院人工智能学院）；Georgia Tech AI Safety Initiative（佐治亚理工学院人工智能安全倡议）
Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
Department of Statistics and Data Science, Chinese University of Hong Kong, Hong Kong（统计与数据科学系，香港中文大学）；Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA（工业与运营管理系，密歇根大学）
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
Harvard Law School（哈佛法学院）；MIT Center for Constructive Communication（麻省理工学院建设性传播中心）；MIT（麻省理工学院）；UC Irvine（加州大学尔湾分校）；Inria, Univ. Lille Center（法国国家信息与自动化研究所，里尔大学中心）；Contextual AI；ML Commons；Olin College（奥林学院）；Carnegie Mellon University（卡内基梅隆大学）；Tidelift；Cohere For AI
