TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915891445235712 |
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| author | Xu, Yu Yan, Hongbin Cao, Juan Cheng, Yiji Hang, Tiankai He, Runze Yin, Zijin Zhang, Shiyi Zhang, Yuxin Li, Jintao Wang, Chunyu Lu, Qinglin Lee, Tong-Yee Tang, Fan |
| author_facet | Xu, Yu Yan, Hongbin Cao, Juan Cheng, Yiji Hang, Tiankai He, Runze Yin, Zijin Zhang, Shiyi Zhang, Yuxin Li, Jintao Wang, Chunyu Lu, Qinglin Lee, Tong-Yee Tang, Fan |
| contents | Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts Xu, Yu Yan, Hongbin Cao, Juan Cheng, Yiji Hang, Tiankai He, Runze Yin, Zijin Zhang, Shiyi Zhang, Yuxin Li, Jintao Wang, Chunyu Lu, Qinglin Lee, Tong-Yee Tang, Fan Computer Vision and Pattern Recognition Artificial Intelligence Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations. |
| title | TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.08881 |