💡 The paper introduces UniCorn, a self-improvement framework for unified multimodal models that addresses the generation gap in these models. The generation gap refers to the discrepancy between a model's ability to understand multimodal inputs and its ability to generate high-quality outputs. This gap is formalized as Conduction Aphasia, where models can accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis.

To address this issue, UniCorn proposes a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. The framework partitions a single unified multimodal model into three collaborative roles: Proposer, Solver, and Judge. The Proposer generates initial outputs, the Solver refines these outputs, and the Judge evaluates the quality of the refined outputs. Through self-play and cognitive pattern reconstruction, UniCorn generates high-quality interactions and distills latent understanding into explicit generative signals.

The authors introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop, to validate the restoration of multimodal coherence. The results demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves state-of-the-art performance on several benchmarks, including TIIF, DPG, CompBench, and UniCycle, and delivers substantial gains on WISE and OneIG.

The contributions of the paper are significant, as UniCorn enhances text-to-image generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence. The results highlight the effectiveness of the self-improvement framework in addressing the generation gap in unified multimodal models, and the potential of UniCorn to improve the performance of these models in various applications. Overall, the paper presents a novel approach to self-improving unified multimodal models, with significant implications for the development of more advanced and effective multimodal models.