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

• URL: http://arxiv.org/abs/2604.12874v1
• Date: Tue, 14 Apr 2026 15:23:36 GMT
• Title: LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems
• Abstract summary: This paper reviews emerging directions beyond monolithic transformers, emphasizing agentic AI and brain inspired architectures as complementary paths toward sustainable, adaptive systems.<n>We propose LIFE, a reasoning and Learning framework that is Incremental, Flexible, and Energy efficient.<n>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 like cluster.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Reward-Driven LLM Agent Workflows: Synthesizing POMDP Routing and Self-Correction for Autonomous Decision-Making [0.0]
  This paper addresses key technical challenges in current large language model (LLM) agent applications, including long-horizon planning, sparse reward attribution, and dynamic environmental interaction.<n>The proposed architecture is based on the synthesis of core AI paradigms: Visual, Language, Generative, Graph, Multimodal, Reinforcement, and Agent Intelligence.<n>Ultimately, the resulting architecture offers a practical, scalable reference framework for developing artificial intelligence technologies in complex, multi-step autonomous systems.
  arXiv  Detail & Related papers  (2026-07-19T02:51:41Z)
• Toward a Modular Architecture for Embedded AI Agent Systems at the Edge [0.0]
  This paper proposes a modular reference architecture for embedded agent systems.<n>We introduce a tiered design that decouples On-Device Agents and Cloud-Augmented Agents.<n>A key contribution is the integration of a cross-cutting Governance Layer.
  arXiv  Detail & Related papers  (2026-06-01T20:24:18Z)
• Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark [56.84575992325538]
  We introduce Ego-METAS: the first online Multimodal Energy-efficient Temporal Action benchmark.<n>Ego-METAS is a unified testbed of more than 100 hours of untrimmed egocentric video from EgoExo4D, CMU-MMAC, and CaptainCook4D.<n>We formulate an online temporal action segmentation task where models must dynamically select which sensors to activate at each timestep.<n>Our evaluations show that optimal routing is highly scenario-dependent, and that existing policy-learning methods, designed primarily for trimmed clips, struggle to adapt to continuous, untrimmed environments.
  arXiv  Detail & Related papers  (2026-05-29T17:36:10Z)
• Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey [64.20429885919701]
  We propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle.<n>We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference.<n>We identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems.
  arXiv  Detail & Related papers  (2026-04-09T06:13:59Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• Cognitive Platform Engineering for Autonomous Cloud Operations [0.14658400971135652]
  This paper introduces Cognitive Platform Engineering, a next-generation paradigm that integrates sensing, reasoning, and autonomous action directly into the platform lifecycle.<n>A prototype implementation built with Terraform, Open Policy Agent, and ML-based anomaly detection demonstrates improvements in mean time to resolution, resource efficiency, and compliance.
  arXiv  Detail & Related papers  (2026-01-24T18:17:49Z)
• Agentic AI for Mobile Network RAN Management and Optimization [0.0]
  Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs)<n>This paper contributes to ongoing research on Agentic AI in 5G and 6G networks by tracing its evolution from classical agents to Agentic AI.<n>Core design patterns-reflection, planning, tool use, and multi-agent collaboration-are then described to illustrate how intelligent behaviors are orchestrated.
  arXiv  Detail & Related papers  (2025-11-04T12:34:57Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks [0.0]
  Foundation models have rapidly advanced AI, raising the question of whether their decisions will surpass human strategies in real-world domains.<n>Common challenge is adapting complex systems to dynamic environments.<n>We argue that monolithic foundation models face conceptual limits in overcoming it.<n>We propose a decentralized architecture of interacting small agent networks (SANs)
  arXiv  Detail & Related papers  (2025-10-07T18:10:31Z)
• From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
  Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
  arXiv  Detail & Related papers  (2025-10-07T05:45:25Z)
• Adaptive and Resource-efficient Agentic AI Systems for Mobile and Embedded Devices: A Survey [11.537225726120495]
  Foundation models have reshaped AI by unifying fragmented architectures into scalable backbones with multimodal reasoning and contextual adaptation.<n>With FMs as their cognitive core, agents transcend rule-based behaviors to achieve autonomy, generalization, and self-reflection.<n>This survey provides the first systematic characterization of adaptive, resource-efficient agentic AI systems.
  arXiv  Detail & Related papers  (2025-09-30T02:37:52Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.