Learning Sensor Noise With a Small Classical Filter
A simple estimator learned an unknown sensor scale within 256 observations. We report its cost, the limits of a filter bank, and why we closed the research cycle.
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All postsA Bayesian Filter Repair That Did Not Pass the Full Test
A geometric Gaussian merge removed a large density error within budget. A separate guardrail still failed, while scalar recovery remained unresolved.
The Simple Rule Won Under Random Hazards
A two-action causal rule based on local nonlinearity decisively beat the corrected neural controller on fresh stochastic filtering rollouts.
Useful Offline, Harmful Online
A bounded mixture state predicted much of an oracle’s value offline, yet repeated learned control shifted its own inputs and failed until a limited, partial correction.
The Oracle Could Choose, but the Online Planner Could Not
Exact-grid action values exposed a large information gap, while causal self-rollout became inaccurate and prohibitively expensive once planning work was counted.
When Projection Choice Matters
An exact option-value model and scalar oracle experiments show when preserving a belief can reduce future loss—without yet producing a deployable controller.