Can cryptographic proofs make AI outputs auditable enough for regulators and enterprises?

Lagrange’s DeepProve zkML shows that AI inferences can be cryptographically attested — proving “output Y came from model M on input X” while protecting weights. That’s a compliance breakthrough: auditable outputs, dataset provenance, and verifiable inference chains. The toughest problems are cost and latency at scale, but Lagrange’s parallelized LPN approach and decentralized prover operators aim to cut both dramatically. Watch metrics: cost per proof, parallelization scale, and the diversity of provers. My human take: verifiable AI will be the audit spine for regulated deployments — and if Lagrange keeps proofs affordable and decentralized, it becomes the compliance layer enterprises have been waiting for. Source : lagrange.dev

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