Outputs

Repositories, papers, and demos will be linked here as the work gets underway.

Background reading

• Models That Prove Their Own Correctness — Noga Amit, Shafi Goldwasser, Orr Paradise, Guy N. Rothblum · NeurIPS 2025. Introduces self-proving models, which learn to produce checkable proofs alongside their answers.
• zkLLM: Zero Knowledge Proofs for Large Language Models — Haochen Sun, Jason Li, Hongyang Zhang · ACM CCS 2024. Specialised proofs for attention and tensor operations: an example of exploiting the structure of AI computations instead of generic circuits.

Team: Nir Bitansky (NYU); Yuval Ishai (Technion); Ron Rothblum (Succinct/Technion); Sarah Meiklejohn (UCL/Google)

Outputs

Repositories, papers, and demos will be linked here as the work gets underway.

Background reading

• One Server for the Price of Two: Simple and Fast Single-Server Private Information Retrieval — Alexandra Henzinger, Matthew M. Hong, Henry Corrigan-Gibbs, Sarah Meiklejohn, Vinod Vaikuntanathan · USENIX Security 2023. SimplePIR: practical private retrieval, a building block for private access to shared data.
• Private Web Search with Tiptoe — Alexandra Henzinger, Emma Dauterman, Henry Corrigan-Gibbs, Nickolai Zeldovich · SOSP 2023. Private search over semantic embeddings: the closest existing system to concealing queries and embeddings in retrieval.