TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
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.
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Yu Xu
claimed_verified
2026-01-19T09:24:33.659Z
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Yu Xu
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YUXU915
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Hongbin Yan
claimed_verified
2026-01-15T09:33:33.652Z
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Yana-Hangabina
Yana-Hangabina
user
Yana-Hangabina
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Juan Cao
6968839e0ac10a06522f6ad7
Yiji Cheng
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Tiankai Hang
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Runze He
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Zijin Yin
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Shiyi Zhang
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Yuxin Zhang
claimed_verified
2026-02-04T12:34:32.235Z
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Yuxin Zhang
ZHazel
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ZHazel
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Jintao Li
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Chunyu Wang
6968839e0ac10a06522f6adf
Qinglin Lu
6968839e0ac10a06522f6ae0
Tong-Yee Lee
6968839e0ac10a06522f6ae1
Fan Tang
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
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.
A novel framework injects semantic intent into Mixture-of-Experts routing for image generation and editing, resolving task interference through hierarchical task annotation and predictive alignment regularization.
Mixture-of-Experts diffusion transformers task interference gating networks semantic intent hierarchical task semantic annotation predictive alignment regularization task-specific specialization