LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems
AI 摘要
LIFE框架通过结合Agent技术、新型内存系统等组件，提升HPC系统能源效率和自适应能力。
主要贡献
- 提出LIFE框架，一种节能的持续学习Agentic AI框架
- 结合Agentic Context Engineering、新型内存系统和信息格学习
- 应用于HPC运维，解决延迟峰值检测和缓解问题
方法论
LIFE框架采用Agent中心系统，通过Orchestrator协调，结合Agentic Context Engineering、内存系统和信息格学习，实现自适应网络管理。
原文摘要
The rapid advancement of AI has changed the character of HPC usage such as dimensioning, provisioning, and execution. Not only has energy demand been amplified, but existing rudimentary continual learning capabilities limit ability of AI to effectively manage HPCs. This paper reviews emerging directions beyond monolithic transformers, emphasizing agentic AI and brain inspired architectures as complementary paths toward sustainable, adaptive systems. We propose LIFE, a reasoning and Learning framework that is Incremental, Flexible, and Energy efficient that is implemented as an agent centric system rather than a single monolithic model. LIFE uniquely combines four components to realize self evolving network management and operations in HPCs. The components are an orchestrator, Agentic Context Engineering, a novel memory system, and information lattice learning. LIFE can also generalize to enable a variety of orthogonal use cases. We ground LIFE in a specific closed loop HPC operations example for detecting and mitigating latency spikes experienced by critical micro services running on a Kubernetes like cluster.