$E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference
arXiv:2605.27428v1 Announce Type: new Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time, and it is non-stationary due to user-driven semantic events, background load, and device churn. Consequently, a resource manager that is tuned offline under a fixed regime can become brittle and expensive to maintain. This paper presents $E^3$-Agent, an executable and evolving agent for edge artificial intelligence generated content (AIGC) resource management. $E^3$-Agent separates a fast-path r
The proliferation of generative AI models on edge devices necessitates more dynamic and adaptive resource management solutions.
This development addresses critical performance and maintenance challenges for deploying generative AI at the edge, making such deployments more robust and scalable.
Resource management for edge generative inference will shift from static, offline tuning to dynamic, evolving, and executable agents.
- · Edge AI providers
- · Device manufacturers
- · AI software developers
- · Industries using edge generative AI
- · Traditional static resource management systems
- · Companies relying on brittle edge AI deployments
Improved stability and efficiency of generative AI applications running on edge devices.
Accelerated adoption of more complex and autonomous generative AI capabilities in edge environments.
Reduced operational costs and increased reliability for large-scale distributed AI systems, fostering new edge-native AI services.
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Read at arXiv cs.LG