As a professor in the Department of Electrical and Computer Engineering at Princeton University, Pramod Viswanath's research spans information theory, cryptography, and learning theory—these seemingly abstract mathematical foundations are the theoretical backbone for @SentientAGI to build verifiable, attributable, and controllable open AI models. Defining OML ————————————————————————— Ownership, control, and alignment mechanisms for open models. This is a fundamental framework for rethinking how AI models should be owned, used, and monetized. Traditional open-source models are either completely open and lose control, or closed and monopolized, stifling innovation. OML offers a third path through model fingerprinting technology: models can be openly downloaded for local use while maintaining traceability and monetization capabilities for remote use, ensuring that models always adhere to the values and safety standards of their creators. The technical breakthrough of OML 1.0 ————————————————————————— He led the development of OML 1.0, achieving groundbreaking progress in large-scale, practical model attribution verification. The specific mechanism works as follows: when a model owner wants to distribute model M to a new AI user, the protocol first converts it into the OML format by fine-tuning it with unique secret fingerprint pairs. Each model copy embeds multiple secret fingerprint pairs, creating a traceable link between the model and the specific requester. This protects innovators' intellectual property while enabling decentralized AI economic incentive alignment. ————————————————————————— When AI models truly become ownable, controllable, and alignable digital assets, Professor Pramod's theoretical foundations and engineering practices are redefining the rules of the entire open-source AI ecosystem. He represents @SentientAGI's core capability of combining deep academic research with cutting-edge technological applications.