LIFE -- 一种面向前沿系统的高效高级持续学习智能体框架
LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems
AI总结：
本文提出LIFE框架，通过整合四个组件实现HPCs的自进化网络管理和操作，旨在提升AI在HPCs中的持续学习能力与能效。
摘要：
人工智能的快速发展改变了HPC使用的特性，如维度规划、资源配置和执行。不仅能源需求增加，现有基础的持续学习能力限制了AI有效管理HPCs的能力。本文回顾了超越单体Transformer的新兴方向，强调智能体AI和脑启发架构作为可持续、适应性系统的互补路径。我们提出了LIFE，一种增量、灵活且高效的推理和学习框架，以智能体为中心而非单一单体模型实现。LIFE独特地结合了四个组件，实现HPCs中的自进化网络管理和操作。组件包括调度器、智能体上下文工程、新型记忆系统和信息晶格学习。LIFE还能泛化以支持多种正交用例。我们基于特定闭环HPC操作示例，为检测和缓解在Kubernetes类集群上运行的关键微服务中的延迟尖峰问题而提出LIFE。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
