Research & Insights

Explore our latest research, analysis, and insights into decentralized machine learning and staking mechanisms.

Technical Analysis
TaoPill

The Controversy of Bittensor's Future with EVM Compatibility

Bittensor's current architecture is intentionally simple: subnet owners create incentive mechanisms that direct validators and miners toward specific goals. While this design is powerful for its intended purpose of producing commodities, the network lacks the programmability of traditional Layer-1s – meaning no smart contracts or complex onchain applications. But with EVM support on the horizon, Bittensor is poised to dramatically expand its capabilities beyond its current scope.

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Governance
TaoPill

Dynamic TAO: Bringing Bittensor out of the Dark Age

Today, $4.2 million worth of TAO emissions will be minted. If the current price holds, over the next year, $1.5 billion of TAO will flow to subnets and their contributors. The critical decisions on how these emissions are allocated are made by a group of the largest validators, essentially resembling a DAO. And let's be real - anyone who's been in crypto for more than 48 hours knows that DAOs are pretty ineffective at accomplishing large, long-term tasks.

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Research
Documentation

Dynamism: Decentralizing the Future of AI and Supercommodities

Before the introduction of Dynamic TAO, Bittensor implemented its governance mechanism through a centralized protocol known as the Root Network. The Root Network placed governance powers in the hands of the top 64 validators—those holding the greatest amount of delegated TAO—to decide how TAO emissions should be distributed across all the subnets in the network.

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Technical Analysis
Twitter

Weight copying in Bittensor and the proposed solutions

Within Bittensor there is a fundamental flaw in Yuma consensus. Validators can replicate the weight assignments of other validators or the consensus weights without performing their own independent evaluations of miners. Validators earn rewards based on how closely their submitted weights align with the consensus.

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Research
TaoPill

Fast Inference, Easy Fine-tuning, Reliable Serverless Compute

If you’ve been around Bittensor, you’ve likely seen a lot of innovation—but much of it has felt experimental, almost like ongoing R&D with yet-to-be-realized potential. Now, things are shifting. At a recent Novelty Search, some of the core ecosystem contributors brought forward a trio of tangible upgrades that push Bittensor beyond the prototype phase and into a more practical, developer-friendly era. By delivering real-world tools—faster inference through NineteenAI, accessible training with Gradients, and serverless compute via Chutes— Bittensor participants are showing that Bittensor isn’t just a hub for ideas, but a growing marketplace of capabilities ready to be put to use.

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Research
Medium

Intelligent Predictive Validation and The New Frontier of Yuma Consensus

As the Bittensor ecosystem continues to evolve, so do the strategies employed to mitigate the pervasive issue of weight copying. The OpenTensor Foundation (OTF) has iterated on the Commit Reveal mechanism, culminating in Commit Reveal 3.0 (CR3). This latest version aims to simplify implementation while enhancing effectiveness in deterring weight copying. However, as with any adaptive system, the arms race between mitigation strategies and malicious actors continues, necessitating further innovations.

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