A model-independent framework converts mild low-degree testing advantages into conditional computational lower bounds for recovery tasks, recovering prior results for planted submatrix and SBM while providing new evidence for detection-recovery gaps in angular synchronization and multi-layer models.
Low- degree lower bounds via almost orthonormal bases
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Algorithmic Contiguity from Low-Degree Heuristic II: Predicting Detection-Recovery Gaps
A model-independent framework converts mild low-degree testing advantages into conditional computational lower bounds for recovery tasks, recovering prior results for planted submatrix and SBM while providing new evidence for detection-recovery gaps in angular synchronization and multi-layer models.