BrainSteer: Probing and Reducing the Neural Evidence Utilization Gap in Single-Trial fMRI Time-Series-to-Image Reconstruction

Does equivalence under a trained neural readout imply equivalence for generative reconstruction? We introduce BrainSteer, a diagnostic and closed-loop steering framework on frozen DynaDiff. BrainSteer decomposes six-TR evidence into a retained component visible to the trained temporal readout and an Exact-Null component annihilated by it, then injects TRAIN-centered evidence through independent zero-preserving, state-conditioned paths. It adds 947,264 trainable parameters (0.947M). On Shared1000, four-subject PixCorr improves from 0.21766 to 0.24590 and SSIM from 0.30864 to 0.35637; non-development relative gains are 12.51% and 14.32%. On frozen VAL64, CORRECT exceeds SHUFFLED structurally in every subject, and five paired ablations favor Full on both structural metrics. Changing Exact-Null identity changes reconstruction while preserving the frozen readout. Time-Only retains about 0.5% of Full structural specificity; the rollout-initial hidden summary preserves most specificity but remains below Full feedback. A 20-state audit finds lower target predicted-$x_0$ error under matched evidence throughout. Matched Exact-Null evidence reduces target-relative error, but neither null-only nor coherent retained/null donor transfer reliably recovers donor identity under fixed recipient Official conditioning. Perceptual/semantic metrics do not improve uniformly. For this studied readout, readout-equivalent evidence need not be reconstruction-equivalent when an independent zero-preserving evidence path is available; its residual utility is context-dependent.