UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision
While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals. To validate the restoration of multimodal coherence, we introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop. Extensive experiments demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves SOTA performance on TIIF(73.8), DPG(86.8), CompBench(88.5), and UniCycle while further delivering substantial gains of +5.0 on WISE and +6.5 on OneIG. These results highlight that our method significantly enhances T2I generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence.
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Ruiyan Han
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2026-01-07T13:21:53.416Z
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Ruiyan Han
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Zhen Fang
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2026-01-07T13:13:51.100Z
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Zhen Fang
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XinYu Sun
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Yuchen Ma
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Ziheng Wang
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Yu Zeng
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2026-01-07T09:25:35.308Z
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Yu Zeng
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Zehui Chen
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2026-01-07T13:22:13.382Z
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Zehui Chen
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Lin Chen
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2026-01-07T09:25:37.096Z
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Lin Chen
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Lin-Chen
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Wenxuan Huang
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2026-01-07T13:37:57.684Z
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Wenxuan Huang
Osilly
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Osilly
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Wei-Jie Xu
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Yi Cao
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Feng Zhao
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision
While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals. To validate the restoration of multimodal coherence, we introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop. Extensive experiments demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves SOTA performance on TIIF(73.8), DPG(86.8), CompBench(88.5), and UniCycle while further delivering substantial gains of +5.0 on WISE and +6.5 on OneIG. These results highlight that our method significantly enhances T2I generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence.
UniCorn, a self-improvement framework for unified multimodal models, addresses generation gaps through self-play and cognitive pattern reconstruction, achieving state-of-the-art results in text-to-image generation.
Unified Multimodal Models Conduction Aphasia UniCorn self-improvement framework self-play cognitive pattern reconstruction Text to Image cycle-consistency UniCycle T2I generation