What CANAREON detects
ML training runs exhibit instability signatures before divergence — instability signatures emerge in training dynamics before loss spikes become visible in standard metrics. The system's ability to recover from perturbations decreases as training approaches instability. CANAREON detects this loss of training system resilience in advance, while intervention is still possible. Detection is self-validatable: no external ground-truth labels are required to identify instability onset.
Signal
Rising variance in training loss
Signal
Increasing gradient norm autocorrelation
Signal
Loss of training system resilience
Domain
Large language model pre-training
Domain
Deep learning fine-tuning runs
Domain
Reinforcement learning training loops
Validation Status
CompletedSynthetic training instability dataset generated and validated
CompletedBCI engine detects divergence precursors on synthetic runs
CompletedSelf-validation protocol established (no external labels required)
PendingValidation on real training runs in partnership with ML teams
PendingBenchmark against standard monitoring (loss curves, grad norm alerts)
Note: Self-validation identifies instability onset signatures without external labels. Independent ground-truth validation on real training runs is ongoing and required before production use.
Synthetic validation is complete. Real-run validation requires ML infrastructure partnership. Estimated timeline to production-grade results: 6–9 months.
Synthetic validation is complete. Real-run validation requires ML infrastructure partnership. Estimated timeline to production-grade results: 6–9 months.