Cryptographic proofs now allow users to verify that a specific model produced a given output without re-running the computation. This solves the "black box" problem where providers swap expensive models for cheaper ones. ZK-ML researchers are scaling these proofs to larger networks. Practitioners can now audit model integrity and ensure strict compliance with specific weights.