Forward-Only Learning Across Black-Box Interfaces: State Persistence and Pilot Recovery

A changing black-box interface can present different coordinates to a downstream learner even when every realized transformation is invertible. We study how interface-state persistence and observability affect forward-only local learning without gradient propagation across the interface. Matched fixed and resampled channels share the same state distribution and paired upstream representations, separating per-state properties from consistency across calls. Experiments cover vanilla Forward-Forward, channel-wise competitive learning, supervised block-local learning, and an external DeeperForward ResNet. Matched Rademacher comparisons show that resampling invertible, norm-preserving states can cause substantial accuracy degradation. We introduce shared pilots that pass through the same hidden state as the data, making structured interface transformations observable from forward outputs. For invertible diagonal states and signed permutations, pilot constructions enable exact representation recovery under noiseless observations without upstream credit. On frozen DeeperForward representations, a single shared pilot restores downstream accuracy from 13.73% to 76.90%, matching the identity condition in all five paired seeds. These results distinguish per-state information preservation from downstream usability and identify state persistence or observability as key interface properties for forward-only learning.