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

论文基本信息

  • arXiv ID: 2604.12874
  • 发表日期: 2026-04-14
  • 作者: Anne Lee, Gurudutt Hosangadi
  • 方向: cs.AI

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.

核心贡献

  1. 新型记忆系统设计: 论文提出了结合工作记忆和情景记忆的混合记忆架构，有效解决长时记忆依赖问题
  2. 计算效率优化: 通过固定数量的记忆token实现近常数级的每步计算和内存开销
  3. 跨任务泛化: 记忆系统设计支持跨不同任务场景的泛化能力

为什么重要

这篇论文解决了视觉运动策略中非马尔可夫任务的关键挑战——传统方法要么受限于短视上下文，要么通过简单扩大上下文窗口带来巨大计算成本。VPWEM通过模仿人类认知中的记忆压缩机制，用固定数量的情景记忆token表示长历史，在保持计算效率的同时显著提升了在需要长期记忆的机器人操作任务中的表现。

与移动端/端侧的相关性

记忆压缩和固定token表示方法对端侧部署具有重要意义——近常数级的内存开销使得该方法适合在资源受限的机器人平台上运行。

参考文献

见原论文: https://arxiv.org/abs/2604.12874