Allora Intelligence Layer Playbook

- Shift to goal-focused ML (gML): set objectives, let @AlloraNetwork route models + contexts
- Learning Feedback Loop: on-chain performance tracking, network-wide updates each cycle
- Performance Forecasting: agents forecast their own accuracy before inference; context-aware weighting boosts relevant signals
- Coordinators, workers, evaluators, curators + reputation = rising aggregate accuracy
- Transparent provenance: trace how feeds evolve, audit decisions, tune incentives
- Consumers fund predictions; top performers earn more; low-signal agents get penalized
- Reliability under drift: accuracy maintained as data, models, and contributors change
- Pair with @NetworkNoya for decentralized compute to complete the agent stack

= self-improving, verifiable DeAI rails for agents, apps, and markets; mainnet unlocks dynamic inference markets + composable predictive feeds

Say gML, repeat $ALLO, watch collective intelligence compound