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CapabilityForge Next rewards traceable training-data improvements that measurably enhance the accuracy, reasoning, clarity, age appropriateness, and safety of learning-oriented AI through reproducible evaluation.
Neuron registration trends
Organizations building learning-oriented AI systems struggle to source or create high-quality training data that demonstrably improves model accuracy, reasoning, and safety. Existing datasets often lack verifiable lineage, may contain duplicates or evaluation leakage, and provide no measurable signal of whether a data patch actually improves downstream model performance. The cost and expertise required to evaluate data improvements under controlled conditions puts this work out of reach for most teams.
A decentralized incentive network can distribute the work of generating, verifying, and testing training-data patches across many contributors. When contributors are rewarded only for measurable improvements to model capability—not for dataset size or compute spent—they focus effort on quality and impact. Validators with domain expertise can check data rights, privacy, and contamination before patches are tested, reducing waste and risk.
CapabilityForge Next implements this as a structured pipeline: miners generate synthetic or appropriately licensed data patches with full provenance; validators verify data rights, privacy, lineage, and quality; qualified patches are trained on frozen baseline models under identical conditions; model outputs are evaluated against objective standards in mathematics and natural sciences; and rewards flow only to patches that measurably improve accuracy, reasoning, calibration, or safety.
Use cases were auto-generated from the GitHub repository and may not reflect the latest changes.
CapabilityForge Next (Subnet 119) is a Bittensor incentive network for discovering and verifying training-data improvements that enhance learning-oriented AI accuracy, reasoning quality, calibration, and safety. Miners generate synthetic or licensed data patches with provenance; validators verify data rights, privacy, and quality; patches are tested against frozen baselines under identical conditions; rewards reflect measurable capability improvements in mathematics, natural sciences, and other subjects with objective evaluation standards.
CapabilityForge Next operates as a multi-stage pipeline where miners generate training-data patches with documented provenance, validators perform comprehensive verification of data rights, privacy, lineage, and quality, then qualified patches are trained on frozen baseline models under controlled conditions. Model outputs are evaluated against objective standards, and rewards are distributed based on measured capability improvements.
The incentive mechanism follows a pipeline: GENERATE → VERIFY → TRAIN → EVALUATE → REWARD. Miners submit data patches with record-level provenance. Validators verify data rights, privacy, lineage, duplication, contamination, and evaluation leakage.
Qualified patches are tested to measure improvements in factual accuracy, reasoning quality, calibration, communication clarity, and safety. Rewards reflect actual capability gains rather than dataset size, compute expenditure, or unverified claims.
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository for the latest instructions.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository
CapabilityForge Next rewards traceable training-data improvements that measurably enhance the accuracy, reasoning, clarity, age appropriateness, and safety of learning-oriented AI through reproducible evaluation.
FAQs were auto-generated from the GitHub repository and may not reflect the latest changes.
Holder & total-staked metrics as of 2h ago (hourly snapshot)
