AureliusPrice

(SN37)

Aurelius is a specialized AI alignment subnet dedicated to identifying instances where AI models produce harmful, false, or unethical outputs. By acting as a safety layer, it transforms these misalignment findings into high-quality training datasets. This data is then used to fine-tune future AI models, ensuring they remain helpful, honest, and harmless while adhering to ethical human values.

The project is led by founder Austin McCaffrey. It was originally established as a fine-tuning subnet operated by the Macrocosmos team. It has since transitioned to the Aurelius team to focus exclusively on AI alignment, providing the critical data infrastructure needed to improve artificial intelligence safety and reliability across the network.

Miners operate as 'adversarial prompters' or red-teamers, tasked with finding ways to trick AI models into failing. Validators act as auditors who verify these findings. They score the miners' contributions against a specific set of rules known as the 'Constitution' to confirm if a legitimate alignment failure was discovered, ensuring only high-quality data is generated and rewarded.

The Tribunate is a unique governance feature of Aurelius that serves as a rules engine or living 'constitution.' It defines the rubric for safe AI behavior and evolves based on community and expert input. This system ensures that the standards for judging AI outputs remain current and effective in promoting ethical AI development across the decentralized stack.

The SN37 token, which operates as a dynamic TAO (dTAO) or Alpha token, represents a stake in the subnet’s output. It is utilized for governance within the Tribunate, rewards miners for successful red-teaming, and incentivizes validators for accurate scoring. Additionally, the token provides participants with access to premium, enterprise-grade alignment datasets produced by the network.

Aurelius is designed to be inter-subnet compatible, using models from other subnets as targets for red-teaming to create a feedback loop that strengthens the entire ecosystem. While producing enterprise-grade data, the project also focuses on publishing open benchmarks on public repositories to prove model safety improvements, serving as a decentralized watchdog for the artificial intelligence industry.