# DIVA: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement Source: [DIVA: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement]() Collection index: [llms.txt]() Data updated: 2026-05-27T05:00:13.467Z Authors: Renjie Lu, Xulong Zhang, Xiaoyang Qu, Shangfei Wang, Jianzong Wang Published: 2026-05-25T01:17:32.000Z ## Abstract Unified Multimodal models (UMMs) built on a single architecture have shown impressive performance in both understanding and generation. We identify a fundamental challenge that lies in inductive biases induced by distinct supervision signals: generation branch prefers high-fidelity, fine-grained representations capable of reconstruction, while the understanding favours semantically discriminative embeddings that remain invariant to task-irrelevant factors. Consequently, optimizing these complementary but non-equivalent objectives within a monolithic backbone leads to mutual impairment instead of enhancement. In this paper, we first analyze the root cause of this interference in unified backbones and reveal a complementary structure in their internal representations. Motivated by the observation, we propose DIVA, a self-improved post-training framework that transforms the representation divergence into interior synergy. By explicitly factorizing the visual representation into shared and unique components based on two complementary information flow, DIVA enables both the understanding and generation branches to achieve beneficial transferring while preserving the integrity of unique information from cross-flow interference via mutual information estimation. Despite its generality, our method consistently achieves improvements across visual understanding (+7.82%) and generation (+8.46%). The official code is available at: https://github.com/Jayyy-H/DIVA. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Editing on ImgEdit (Detailed Action Evaluation)](): Image Editing; ImgEdit; Overall Score: 3.35. ## Original sources and implementation [Original paper]() [PDF]()