LIFE框架让AI在高性能计算中持续学习并节能运行，自动应对延迟突增问题。
LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems
- 用代理架构替代单体模型，实现可增量、灵活的AI系统
- 通过记忆与信息网络协同，自动识别并缓解关键服务延迟
- 适合需要长期自适应运维的高性能计算场景
AI的快速发展改变了高性能计算（HPC）的资源配置与执行方式，能源需求激增，而现有简单的持续学习能力限制了AI对HPC的有效管理。本文探讨超越单一Transformer模型的新方向，强调代理型AI与类脑架构作为实现可持续、自适应系统的互补路径。我们提出LIFE框架——一种以推理和学习为核心的增量式、灵活且节能的代理型系统。LIFE通过四个核心组件实现：编排器、代理上下文工程、新型记忆系统与信息晶格学习，支持HPC中的自我演化网络管理。该框架可在闭环场景中用于检测并缓解基于Kubernetes集群的关键微服务延迟突增问题，并具备扩展至多种异构应用场景的能力。
原文摘要 · Abstract (English)
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.
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