Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making

What the math gives to ML

The paper provides a principled way to compress recurrent belief representations according to decision relevance rather than posterior similarity or entropy. Its key transferable asset is a finite cover whose cells have uniformly small action-value diameter, together with a direct bound on the performance loss of a cell-constant policy. This suggests replacing arbitrary latent-state quantization in recurrent reinforcement learning and world models with noise-calibrated belief cells, using empirical or Lipschitz-certified estimates of value variation. The distinction between a fixed-observation Bayesian update and the predictive controlled belief-transition kernel is especially important: reliability should be assessed over possible next beliefs, not by assuming that every realized filter update is contractive.