Abstract

This document introduces "Cortensor," a groundbreaking decentralized AI inference protocol designed to meet the escalating demand for a neutral, trustless platform that can efficiently host and deliver services from advanced AI models, including DALL-E and GPT-4. As these models become increasingly specialized and integrated into various industries, the need for a system that ensures efficient, reliable, and secure AI service provisioning is becoming more critical.

Cortensor proposes a novel architecture that distinctly segregates data handling, control mechanisms, and transaction processing, enhancing the system's reliability, scalability, and transparency. At the core of Cortensor is the implementation of a dual validation system: "Proof of Inference" (PoI) and "Proof of Useful Work" (PoUW).

• Proof of Inference (PoI): PoI measures the accuracy of AI inferencing by comparing the outputs from different nodes using an additional LLM model. This validation is achieved through the analysis of embedding vector distances, ensuring that the AI-generated outputs are consistent and reliable across the network.

• Proof of Useful Work (PoUW): PoUW goes a step further by using an additional LLM model to validate the correctness and usefulness of the AI inferencing results. This process focuses on ensuring that the generated outputs are not only accurate but also contextually relevant and applicable, adding an extra layer of quality assurance.

These systems fortify the platform against adversarial behaviors, such as inadequate AI service provision, payment defaults, and the illicit replication of AI models. Integrated within the transaction layer, this approach, combined with the economic layer, manages billing, metering, and incentives within a blockchain framework, fostering a secure and dependable environment.

Cortensor leverages the decentralized nature of blockchain technology to establish a marketplace/orchestrator layer, enabling effective communication and negotiation between AI service providers and consumers. This framework not only facilitates the dynamic establishment of service terms and pricing but also promotes the integration of emerging open-source LLMs, advocating for a liberated and uncensored AI service ecosystem. The platform supports a variety of open-source models, such as LLaMA, GPT-Neo, GPT-J, and BigScience’s BLOOM, encouraging unrestricted access and innovation akin to the evolution of Linux.

The trend towards community-driven models, now gaining acceptance for commercial use, underscores this approach. Additionally, the increasing formation of internal AI or ML teams within commercial entities and technology companies highlights the strategic importance of AI capabilities in driving innovation and competitive advantage. Market analysis predicts substantial growth in the LLM and AI agent market, driven by the widespread adoption of AI technologies across various sectors, including healthcare, finance, education, and entertainment.

Cortensor extends its functionality by enabling LLM inferencing nodes to offer memory services, supporting continuous conversations or the construction of complex, decentralized applications. These configurations and the associated data can be encoded into smart contracts and stored off-chain, allowing for cost-effective and flexible deployment of sophisticated AI applications.

In conclusion, Cortensor represents a significant advancement in AI service delivery, offering a decentralized alternative to traditional web2.0 services and setting a new standard for the future of AI model hosting and utilization. This interdisciplinary collaboration among prestigious institutions and innovative startups lays the foundation for a scalable, efficient, and secure decentralized AI inference platform that meets the growing and diverse needs of the global market.