State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting
arXiv:2610.02248v1 Announce Type: new Abstract: Operational land surface forecasting systems built on Mamba-family Structured State Space Models absorb non-stationary confounding events (unrecorded irrigation booms, dam-operation shifts, sensor recalibrations) into their state-transition matrices,…
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- 2026-10-05 04:00 · arXiv cs.LG
State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting