AIHOT 于 2026-08-17 收录了“GenRouter：面向智能体图像生成的统一工作流路由框架”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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[2608.16721] GenRouter: Unified Workflow Routing for Agentic Image Generation
Computer Science > Computer Vision and Pattern Recognition
arXiv:2608.16721 (cs)
-
[Submitted on 17 Aug 2026]
Title:GenRouter: Unified Workflow Routing for Agentic Image Generation
Authors:Harold Haodong Chen, Zhiyu Hou, Wen-Jie Shu, Weilin Ruan, Yingjie Xu, Litao Guo, Ying-Cong Chen
View a PDF of the paper titled GenRouter: Unified Workflow Routing for Agentic Image Generation, by Harold Haodong Chen and 6 other authors
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Abstract:The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the community's focus toward fulfilling increasingly intricate user requests. While recent agentic image generation workflows enhance static inference with advanced capabilities like external knowledge retrieval and iterative reasoning, they mostly operate in isolated silos with fixed ``one-size-fits-all" topologies. This inevitably leads to severe compute-mismatch, where simple queries are forced through computationally heavy pipelines. To bridge this gap, we present GenRouter, the first unified workflow routing framework for agentic image generation. We first formulate GenCanvas, standardizing diverse agentic pipelines into a universal set of foundational primitives and executable templates. Operating over this unified space, GenRouter adaptively routes heterogeneous prompts to their optimal workflows via (i) demand profiling, (ii) experience matching, and (iii) Pareto filtering. Extensive experiments across diverse benchmarks demonstrate that GenRouter achieves superior visual alignment while reducing execution costs by over 95% and latency by 65% compared to heavyweight static pipelines. Furthermore, the system continuously self-evolves via accumulated experience, enabling robust zero-shot generalization that boosts performance and halves computational overhead.
Comments:
Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2608.16721 [cs.CV]
(or
arXiv:2608.16721v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.16721
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haodong Chen [view email]
[v1]
Mon, 17 Aug 2026 15:36:45 UTC (32,045 KB)
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arXiv:2608.16721 (cs)
-
[Submitted on 17 Aug 2026]
Title:GenRouter: Unified Workflow Routing for Agentic Image Generation
Authors:Harold Haodong Chen, Zhiyu Hou, Wen-Jie Shu, Weilin Ruan, Yingjie Xu, Litao Guo, Ying-Cong Chen
View a PDF of the paper titled GenRouter: Unified Workflow Routing for Agentic Image Generation, by Harold Haodong Chen and 6 other authors
View PDF
HTML (experimental)
Abstract:The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the community's focus toward fulfilling increasingly intricate user requests. While recent agentic image generation workflows enhance static inference with advanced capabilities like external knowledge retrieval and iterative reasoning, they mostly operate in isolated silos with fixed ``one-size-fits-all" topologies. This inevitably leads to severe compute-mismatch, where simple queries are forced through computationally heavy pipelines. To bridge this gap, we present GenRouter, the first unified workflow routing framework for agentic image generation. We first formulate GenCanvas, standardizing diverse agentic pipelines into a universal set of foundational primitives and executable templates. Operating over this unified space, GenRouter adaptively routes heterogeneous prompts to their optimal workflows via (i) demand profiling, (ii) experience matching, and (iii) Pareto filtering. Extensive experiments across diverse benchmarks demonstrate that GenRouter achieves superior visual alignment while reducing execution costs by over 95% and latency by 65% compared to heavyweight static pipelines. Furthermore, the system continuously self-evolves via accumulated experience, enabling robust zero-shot generalization that boosts performance and halves computational overhead.
Comments:
Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2608.16721 [cs.CV]
(or
arXiv:2608.16721v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.16721
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haodong Chen [view email]
[v1]
Mon, 17 Aug 2026 15:36:45 UTC (32,045 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled GenRouter: Unified Workflow Routing for Agentic Image Generation, by Harold Haodong Chen and 6 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.CV
< prev
|
next >
new
|
recent
| 2026-08
Change to browse by:
cs
References & Citations
- NASA ADS
- Google Scholar
- Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
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Bibliographic and Citation Tools
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About arXivLabs
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AIHOT 摘要
GenRouter 提出首个面向智能体图像生成的统一工作流路由框架，将多样化智能体管线标准化为通用原语与可执行模板，并通过需求分析、经验匹配和 Pareto 过滤将异构提示词自适应路由至最优工作流。实验显示，相比重型静态管线，GenRouter 在保持视觉对齐的同时将执行成本降低超 95%、延迟降低 65%，并借助累积经验实现零样本泛化，性能提升且计算开销减半。
为什么值得关注
把异构智能体图像流程抽象成统一原语与模板，按需求画像、经验匹配和帕累托过滤做路由，为简单请求被迫走重型管线的问题提供了可迁移的调度思路。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：把异构智能体图像流程抽象成统一原语与模板，按需求画像、经验匹配和帕累托过滤做路由，为简单请求被迫走重型管线的问题提供了可迁移的调度思路。
- AIHOT 评分：36；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。