Infrastructure for Verifiable AI: Where Proof Costs Go and How Hardware Can Help

arXiv:2611.17042v1 Announce Type: new Abstract: Users of artificial intelligence services, including those powered by large language models and other transformer-based models, need assurance that inference and training follow the declared computation without exposing private models or data. Zero-knowledge proofs can provide this assurance, but proof generation remains expensive. Hardware acceleration research often reports gains for individual cryptographic operations, leaving their effect on the full cost of proving neural-network workloads, particularly large transformer models, unclear.