Search for a command to run...
Abstract While Generative AI (GenAI) systems have revolutionized scalable content creation, they inherently fail to meet the strict Intellectual Property (IP) and brand safety requirements of AAA gaming studios and global Ad-Tech networks. In these industries, "similarity" is legally insufficient; deterministic identity preservation is mandatory. Building upon the Mnemosyne Layer-0 Protocol, this application paper introduces the Geometrical IP Lock (GIL) and the Cryptographic Attestation-as-a-Service model. By extracting canonical masks and depth envelopes from licensed 3D assets, the system establishes a zero-tolerance baseline. The Mnemosyne Core Inference Engine operates strictly as a closed-loop, fail-closed pipeline: any generated variation exhibiting geometric hallucination or brand drift is deterministically rejected. Verified frames are delivered with a cryptographically signed Evidence Pack, effectively monetizing "guaranteed compliance" and provides cryptographic evidence for standardized, data-driven dispute resolution against false-positive copyright claims. Introduction: The Illusion of Control and the Necessity of a "Toll Booth" Current open-weight models and GenAI platforms offer prompt-based steering, which creates an illusion of control. However, when generating User Acquisition (UA) assets for a global gaming IP, a 5% morphological drift in a character's armor constitutes a critical IP violation. Mnemosyne v2.0 transitions the industry paradigm from "best-effort generation" to "deterministic verification." To protect both the underlying proprietary AI workflows and the client's original 3D assets, the Mnemosyne Core operates as a secure, decoupled Black Box (API/VPC Gateway). Clients do not manage the volatile diffusion models; instead, they interface with the Mnemosyne Gate. The protocol establishes a transactional verification model: clients are only charged for frames that successfully pass the strict P0 invariants and receive a cryptographic seal of approval. License Notice: The theoretical paper and specifications are licensed under CC-BY-4.0. The associated software implementations and log schemas (provided via GitHub) are licensed under MIT.