The Aurelius team has published a detailed roadmap outlining how its alignment research will translate into protocol infrastructure and ecosystem growth.

The roadmap follows the subnet’s whitepaper published on Feb. 19, which introduced Aurelius’ core thesis that alignment should emerge from multi-agent experience rather than be imposed through post-training constraints.

Together, the documents provide the clearest view yet of how the team intends to move from early protocol validation toward a fully decentralized alignment system.

Positioning Around Experiential Alignment

Within the Bittensor ecosystem, Aurelius is positioning itself around the production of alignment-focused training data and supporting infrastructure rather than traditional model inference tasks.

The design centers on a competitive loop in which miners propose new environments and world model improvements while validators execute and score those contributions before admitting high-quality outputs into the corpus. Over time, the system is intended to compound as stronger environments generate richer interaction data and more capable models produce increasingly complex scenarios.

At the core of the approach are what Aurelius calls “aenes,” described in the whitepaper as atomic units of moral reasoning produced through multi-agent interaction under real consequences. The protocol aims to accumulate these episodes into a growing corpus used to train models toward what the team refers to as experiential alignment.

According to the whitepaper, this framework is designed to differ from approaches such as RLHF that optimize for behavioral compliance using centrally defined reward functions. The newly published roadmap outlines, across four phases, how the subnet plans to build the infrastructure, datasets, and commercial surface area around that thesis.

Phase I: Observe

The first phase focuses on core protocol validation and incentive design.

According to the roadmap, key milestones include:

• Aurelius v0 (already live on Subnet 37)
• Incentive mechanism design covering reward curves and penalty structures
• Prototype Aurelius v1 development
• Pareto frontier refinement across alignment quality, throughput, and cost
• Testnet deployment to evaluate adversarial robustness

This stage is intended to establish the foundation of the alignment scoring pipeline and validate that the economic design holds under competitive conditions.

Phase II: Reveal

The second phase shifts toward market entry and early commercialization.

Planned components include:

• Aurelius v1 release
• Work token focused on configuration contributions
• Merkle tree construction for the corpus
• Fine-tuning models on the generated data
• Publication of fine-tuning results
• First open source dataset release
• Sales portal launch

The roadmap also outlines infrastructure aimed at external users, including API access to the corpus, staking tiers for access, benchmarking evaluations, and token-gated certifications.

This phase marks the subnet’s intended transition from internal validation toward external adoption.

Phase III: Teach

The third phase focuses on scaling the ecosystem and expanding model capabilities.

Targets include:

• Aurelius v2
• Work token expansion to world model contributions
• Fine-tuning on larger models
• Regular open source dataset cadence
• Expanded staking tiers for premium access
• Peer review incentives
• Benchmarking dashboard
• Subnet partnerships

The roadmap frames this stage as the point where the system begins compounding through broader ecosystem participation.

Phase IV: Align

The final phase outlines a long-term vision of full decentralization and autonomous operation.

Planned elements include:

• An evolutionary flywheel
• Corpus expansion
• Domain-specific datasets
• Premium dataset access
• Licensing of fine-tuned models
• Stake-to-deploy model infrastructure
• Frontier model alignment efforts
• The Tribunate governance structure
• An alignment oracle
• On-chain governance

Part of a Broader SN37 Push

Execution across the early phases will determine whether Subnet 37 can translate its alignment thesis into measurable improvements in training data quality and model behavior. If the team can demonstrate that its multi-agent framework produces durable gains in model reasoning, Aurelius could emerge as one of the more technically distinct alignment efforts within the Bittensor ecosystem.

For now, the roadmap establishes the direction. Progress across upcoming releases will provide the clearest indication of how quickly that vision moves from research to production infrastructure.

We look forward to covering what comes next.

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