CARV Announces Open-Source For Its D.A.T.A. Framework

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The leading AI chain ecosystem for data sovereignty, CARV has today announced that its D.A.T.A. (Data Authentication, Trust, and Attestation) Framework has gone open-source, marking a notable milestone in its decentralized AI journey.

While CARV is relentlessly pursuing democratization of AI data access, its decision to open-source its D.A.T.A. framework will facilitate this process by providing AI agents with verifiable access to on-chain and off-chain data. 

Per the announcement, the D.A.T.A. Framework is now fully available on GitHub. Developers worldwide can now build, refine, and scale AI agents with trustless, structured, and privacy-preserving data.

Notably, CARV’s open-source D.A.T.A. Framework offers developers the ability to build AI agents that don’t just react but also reason, adapt, and optimize interactions in real time using on-chain and off-chain data.

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This development also promises to bring DeepSeek’s chain-of-thought processing into web3 by allowing AI agents to perform multi-step logical reasoning before taking actions.

CARV’s D.A.T.A. Framework also enables multi-stage verification of AI decisions while empowering AI agents with economic self-awareness. The Framework aims to foster decentralized knowledge networks.

Furthermore, the Open-Source D.A.T.A. Framework boasts multiple features, positioning itself as the foundation for decentralized intelligence. Some of its key features include real-time on- and off-chain data access, cross-chain verification identity mapping, economic self-awareness and monetization, and privacy-preserving AI computation with TEE.

CARV’s D.A.T.A. 2.0 is going open-source, and the platform provides developers, researchers, and enterprises with the opportunity to contribute to the evolution of AI reasoning in Web3.

Impressively, they can now leverage token-driven intelligence to create AI-driven economies and push the boundaries of privacy-preserving AI execution, thereby fostering economic self-awareness among AI agents.