Readout Fidelity Bottlenecks Self-Verification in Unified Multimodal Models

Unified multimodal models combine image generation and visual understanding, allowing them to evaluate and select among their own generations without an external verifier. Yet self-verified Best-of-N recovers only a small fraction of the quality available in candidate pools. We identify readout fidelity as an independent bottleneck: useful candidate preferences can already exist in verifier evidence but are compressed by discrete Yes/No decoding. Across seven models, discrete readout produces extensive score collisions and can even underperform random selection despite substantial oracle headroom. We model discrete verification as finite observations of an underlying Yes/No preference, with a formulation agnostic to how the verifier evidence is produced. In the direct-answer setting, repeated stochastic observations converge to the pre-decoding logit margin, while full stochastic decoding closely tracks the predicted finite-observation behavior. We use Continuous Best-of-N (C-BoN) as a controlled intervention that directly exposes this limiting preference while keeping the remaining selection pipeline fixed. Across seven models and four benchmarks, C-BoN improves all 28 settings without additional models, training, generation, or verifier calls, and enables stronger Best-of-N scaling. Larger continuous margins also yield more reliable ordering within discrete ties, while recovered ordering tracks downstream gains. These results establish readout fidelity as an information bottleneck in self-verification and show that verifier evidence can contain richer preferences than discrete verdicts reveal.