Path consistency for latent world models
A self-supervised mechanism that penalizes disagreement between horizon-conditioned direct prediction and autoregressive rollout, aimed at reducing compound error in latent rollouts. Designed as an importable component rather than a standalone architecture.
- Status
- In progress
- Topics
- world models, self-supervised learning
Acoustic early warning, and where it breaks
A Random Forest classifier on MFCC features with local inference, reaching strong held-out metrics that then failed on real recordings while misflagging ambient noise. The generalization gap, not the accuracy number, is the finding.
- Status
- 2026
- Topics
- audio ML, sim-to-real, embedded