Abstract

When a recurrent network iteratively refines its predictions, is the performance gain driven primarily by generic temporal integration, or does it depend on the specific geometry of the model's own prior output? To resolve this, we dissect a minimal 35-neuron recurrent network with controlled feedback interventions. Recurrent-specific gain is isolated by two contrasts that do not depend on the convergence-sensitive variable-noise residual: static repeated input, where any deterministic stateless aggregator yields zero gain by construction, and a 120k-parameter MNIST extension where the recurrent loop retains a small but reliable residual. Under variable noise a loop−ensemble residual is additionally present at low-to-moderate noise but is convergence-dependent (reported descriptively), with the no-feedback within-network ensemble matching or nominally exceeding the loop at high noise. Testing feedback-source dependence directly, we replace self-generated feedback with structurally valid output from an independently trained clone. This consistently degrades performance relative to self-feedback at all tested scales. At the minimal scale, clone feedback drops accuracy below the no-feedback baseline — a scale-bounded fake-mirror inversion (robust under static input; marginal under variable noise). This absolute-harm penalty is scale-dependent in both regimes: under variable noise it transitions to a net-positive but still suboptimal signal at the larger tested widths and on MNIST, while under static input it attenuates toward near-neutral at the larger widths; coordinate-shuffled feedback by contrast is harmful at every tested scale. Progressively stronger static aligners recover the clone-feedback loss essentially fully under static input but plateau near ~85% under variable noise, indicating that static open-loop mapping does not fully restore closed-loop compatibility across the aligner capacities we evaluated. A relative condition-number analysis does not separate the evaluated feedback-source conditions. Together, these results characterize feedback-geometry compatibility: an empirical relation between a recurrent receiver and the geometry of its self-generated feedback that is not captured by generic temporal integration alone.