Predicting Generation Failures from Intermediate Representations

Generative models based on diffusion or flow matching can produce undesirable, low-quality, or invalid outputs, which we broadly refer to as generation failures. Detecting such failures before the generation is complete can help identify bad samples early and avoid spending additional computation on samples that will eventually be discarded. In this work, we study whether generation failures can be forecast from intermediate representations of the generative model, and how early such signals become useful. We find that simple reconstruction-based models, including vanilla autoencoders and Block Sparse Featurizers (BSF), expose markers of eventual generation failure through their reconstruction residuals and feature usage patterns. On La-Proteina, a flow matching based protein generative model, these signals become predictive very early (first quarter) in generation and outperform the sequence-based baselines at the same stage. For low-entropy failures, we further find that the representation-level signal appears before the same failure becomes visible in the model's predicted sequence. We see a similar trend in image generation, where the reconstruction-based detectors perform comparably to or better than existing detectors on several datasets while 15-45% of the generation process still remains. Together, these results suggest that information about eventual generation failure can become accessible inside the model well before the final sample is produced, and in some cases before the failure is expressed in the generated output. More broadly, the recurrence of this behavior across protein and image generation suggests that reconstructing intermediate representations can serve as a simple, general tool for studying when generation failures become predictable.