Persistent Tri-State Message Passing

תקציר מקורי באנגליתarXiv:2601.01207v2 Announce Type: replace Abstract: In stochastic message passing, an edge's sampled role changes the node states used to compute adaptive weights at later layers. Weight averaging therefore depends on whether edge roles persist across layers or are resampled at each layer. We study this dependence in Persistent Tri-State Message Passing (P3MP), which combines persistent additive, subtractive, and inactive roles with weights computed from each sample's endpoint states. For two layers, we separate target-state feedback from weight-source covariance and derive the condition under which local and shared weights reverse their preactivation ordering. Shared weights retain a $1/K$ fraction of the feedback for $K$ samples. The interaction is zero for a source-only scorer. Permutin