Score, Subnet 44 on Bittensor, has achieved a groundbreaking feat with the launch of Turbo Vision, a redesigned validation and incentive mechanism. This redefines how intelligence is measured, verified, and rewarded across the Bittensor network.
The update introduces a twin-competition system built to scale production workloads while continuing to push the limits of Vision AI. Every model evaluation is now signed, public, and verifiable, setting a new transparency standard for decentralized AI development.
The Public Challenge: Open Competition on Chutes
Score is opening a public Vision AI competition on Chutes, dedicating 10% of the subnet emissions to it. The experiment’s first version focuses on football footage, testing model capability in tracking players, labeling teams and roles, and mapping pitch keypoints. But the system itself is taxonomy-agnostic, meaning it can evolve beyond sports into other visual domains.
Running directly on the mainnet is a deliberate choice. The team believes real incentives create real progress. Unlike testnets, mainnet competition drives genuine performance, innovation, and resilience under real-world conditions.
A Smarter, Fairer Incentive System
The new TurboVision Incentive Mechanism replaces the subnet’s previous evaluation approach with a system that’s structured, transparent, and far harder to exploit.
Here’s what’s new:
a. Structured evaluation: Validators create a “pseudo-ground-truth” using top vision-language models to assess miners’ predictions.
b. Holistic scoring: Models are graded on accuracy, tracking, and geometry consistency – not just bounding boxes.
c. On-chain verification: Results are finalized through consensus, with each evaluation cryptographically signed and publicly visible.
d. Scalability: Cached pseudo-ground-truths allow for large-scale parallel processing.
e. Future-ready: Upcoming updates will add event detection and latency-based performance bonuses.
This means TurboVision rewards not just correct answers, but quality of reasoning models that track smoothly, maintain object identities, and show genuine visual understanding.
Built-In Security and Integrity
Working with the Chutes team, Score Vision introduced new integrity safeguards to make every competition tamper-evident.
Each TurboVision deployment is verified against a template, ensuring miners can’t manipulate workloads or hide proprietary code. Every model runs in a locked, integrity-checked environment with verified commands.
This closes the gap between open-source transparency and enterprise-grade accountability, a balance crucial for future subnet adoption.
Looking Ahead: The Private Track on Targon and Chutes’ TEE
While the public competition powers open innovation, a private track is already in the works for enterprise customers.
Built on Targon and Chutes’ trusted execution environments (TEEs), this track will let partners bring proprietary datasets into the subnet, with miners competing on both quality and speed. All results will be verified securely inside TEEs.
Over time, this private track will become the backbone for production-grade validation, while the public track continues as an open innovation lab.
Two Repositories, One Mission
For now, Subnet-44 will operate two repositories:
a. The existing repo will support active partnerships and customer projects.
b. The new TurboVision repo will remain in “graduation mode” until it matches or surpasses the previous system’s quality.
Once that milestone is reached, the subnet will fully migrate to TurboVision and retire the legacy system.
Why It Matters
This launch is more than just a technical upgrade; it’s a statement of transparency, proof, and leadership. By opening its validation process to public scrutiny, Score Vision demonstrates that open AI can also be enterprise-grade.
The team compares this approach to the balance between Linux and Red Hat:
a. The public track is open, collaborative, and transparent.
b. The private track builds on that foundation, monetized through trust and reliability.
Together, they form a model for how decentralized AI can scale responsibly, open for innovation, yet robust enough for enterprise use.
The Bottom Line
Subnet-44’s TurboVision launch marks a decisive evolution in decentralized AI: a system that proves real intelligence can be measured, rewarded, and publicly tested.By combining open competition, verifiable proof, and enterprise reliability, Score Vision is building not just better models, but a fairer, more accountable future for Vision AI.
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