LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems

TL;DR

LIFE is an energy-efficient, agent-centric continual learning framework designed for high-performance computing systems, combining novel components for adaptive, sustainable AI management.

Contribution

It introduces a unique, modular framework with four components that enable self-evolving network management in HPCs, emphasizing energy efficiency and adaptability.

Findings

LIFE demonstrates effective latency spike detection and mitigation in HPC microservices.

The framework generalizes to various use cases beyond the initial example.

LIFE's components enable continuous, energy-efficient system adaptation.

Abstract

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.