[2511.19413] UniGame: Turning a Unified Multimodal Model Into Its Own Adversary

Computer Science > Machine Learning arXiv:2511.19413 (cs) [Submitted on 24 Nov 2025 (v1), last revised 30 Mar 2026 (this version, v3)] Title:UniGame: Turning a Unified Multimodal Model Into Its Own Adversary Authors:Zhaolong Su, Wang Lu, Hao Chen, Sharon Li, Jindong Wang View a PDF of the paper titled UniGame: Turning a Unified Multimodal Model Into Its Own Adversary, by Zhaolong Su and 4 other authors View PDF HTML (experimental) Abstract:Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental inconsistency: understanding favors compact embeddings, whereas generation favors reconstruction-rich representations. This structural trade-off produces misaligned decision boundaries, degraded cross-modal coherence, and heightened vulnerability under distributional and adversarial shifts. In this paper, we present UniGame, a self-adversarial post-training framework that directly targets the inconsistencies. By applying a lightweight perturber at the shared token interface, UniGame enables the generation branch to actively seek and challenge fragile understanding, turning the model itself into its own adversary. Experiments demonstrate that UniGame significantly improves the consistency (+4.6%). Moreover, it also achieves substantial improvements in understanding (+3.6%), generation (+0.02)on GenEval, out-of-distribution and adversarial robustness (+4.8% and +6.2% o...