Bittensor Competition Proves Decentralized AI Alignment: Steering Model Thoughts Without Retraining
bittingthembits · x · 2026-08-08
A decentralized AI safety competition organized by Bittensor subnets Apex and Aurelius has successfully demonstrated the feasibility of altering a model's internal thoughts without retraining, using activation steering.
- Mechanism: Across 36 rounds, hundreds of miners developed modules for Gemma-3-12B to locate and manipulate specific concepts inside the model, changing its behavior without full retraining.
- Challenges: Some interventions were too aggressive and broke the model's coherence. The scoring mechanism was subsequently adjusted, leading miners to produce improved results.
- Next Steps: The underlying techniques, including sparse autoencoders, will be used to translate messy model internals into human-interpretable features, feeding directly into Aurelius's actual product, Proba.
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