Key Features of zkPyTorch - zkPyTorch enables verifiable and data-protective AI without compromising performance [1] - Model weights remain local, ensuring they are never exposed or transmitted [2] - Cryptographic proofs guarantee that results are derived from Gemma-3 [2] - zkPyTorch is fully compatible with existing PyTorch workflows [2]

Enter zkML + Polyhedra:• Cryptographic proofs that the exact driving model in use is the one that passed validation.• Transparent updates — every new version can be verified against what was promised.• Privacy preserved — no need to reveal raw weights, just the proof of compliance. ...

Enter zkML + Polyhedra:• Cryptographic proofs that the exact driving model in use is the one that passed validation.• Transparent updates — every new version can be verified against what was promised.• Privacy preserved — no need to reveal raw weights, just the proof of compliance. ...

zkML (Zero-Knowledge Machine Learning) Benefits - Cryptographic proofs ensure model behavior aligns with declared safety rules, enhancing transparency and accountability [1] - Eliminates black-box trust by providing transparency without exposing sensitive data or inner logic [1] - Enables auditing of updates to confirm deployed version matches promised specifications [1] Polyhedra's Role - Integrates with zkML to provide cryptographic proofs [1] - Facilitates transparency without data exposure [1] - Supports verifiable model updates [1]