$E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference

• URL: http://arxiv.org/abs/2605.27428v1
• Date: Thu, 21 May 2026 12:32:43 GMT
• Title: $E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference
• Abstract summary: $E3$-Agent is an evolving agent for edge artificial intelligence generated content (AIGC) resource management.<n>Agent learns online from execution feedback and continuously adapts to unknown and time-varying service-time mappings.
• Score: 39.00570828094742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time, and it is non-stationary due to user-driven semantic events, background load, and device churn. Consequently, a resource manager that is tuned offline under a fixed regime can become brittle and expensive to maintain. This paper presents $E^3$-Agent, an executable and evolving agent for edge artificial intelligence generated content (AIGC) resource management. $E^3$-Agent separates a fast-path router that makes millisecond-level dispatch decisions from a slow-path, event-driven large language model (LLM) meta-controller that mitigates regime shifts through a small, explicit control surface exposed via a tool interface, including risk gating, router configuration, and rapid performance calibration. The agent learns online from execution feedback and continuously adapts to unknown and time-varying service-time mappings. We evaluate $E^3$-Agent in a discrete-event simulator driven by MLPerf-derived device-model measurement priors, covering cold-start warmup and three dynamic regimes: semantic dynamics, device churn, and hidden drift. Across the dynamic scenarios, $E^3$-Agent reduces average latency by 65%-73% compared to the best static baseline, stays within 7%-10% of an online full-information Oracle used for evaluation, and effectively suppresses stutter rate under semantic degradation.

Related papers

• AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving [16.735743806437487]
  We present AGENTSERVESIM, a hardware-aware simulator for multi-turn LLM agent serving.<n>We show that AGENTSERVESIM reproduces real-system behavior within 6% error across key performance metrics while running entirely on commodity CPUs.
  arXiv  Detail & Related papers  (2026-06-08T15:20:23Z)
• ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox [61.862814740220806]
  $textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
  arXiv  Detail & Related papers  (2026-05-11T16:20:51Z)
• Step-level Optimization for Efficient Computer-use Agents [51.29573359027217]
  We argue that strong computer-use agents remain expensive and slow in practice.<n>We propose an event-driven, step-level cascade for computer-use agents.
  arXiv  Detail & Related papers  (2026-04-29T19:59:36Z)
• Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization [58.59491516762626]
  We propose AMRO-S, an efficient and interpretable routing framework for Multi-Agent Systems (MAS)<n>AMRO-S models MAS routing as a semantic-conditioned path selection problem, enhancing routing performance through three key mechanisms.<n>Extensive experiments on five public benchmarks and high-concurrency stress tests demonstrate that AMRO-S consistently improves the quality--cost trade-off over strong routing baselines.
  arXiv  Detail & Related papers  (2026-03-13T12:26:05Z)
• AgentCgroup: Understanding and Controlling OS Resources of AI Agents [2.8139711959925244]
  AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers.<n>We present a systematic characterization of OS-level resource dynamics in sandboxed AI coding agents.<n>Preliminary evaluation demonstrates improved multi-tenant isolation and reduced resource waste.
  arXiv  Detail & Related papers  (2026-02-10T02:37:42Z)
• EvoRoute: Experience-Driven Self-Routing LLM Agent Systems [100.64399490164959]
  EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments.<n> Experiments on challenging agentic benchmarks demonstrate that EvoRoute, when integrated into off-the-shelf agentic systems, not only sustains or enhances system performance but also reduces execution cost by up to $80%$ and latency by over $70%$.
  arXiv  Detail & Related papers  (2026-01-06T04:06:46Z)
• Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios [0.9069311779417014]
  This paper introduces an agent framework grounded in real-world practical experience.<n>An end-to-end framework named Jenius-Agent has been integrated with three key optimizations.<n>Experiments show a 20 percent improvement in task accuracy, along with a reduced token cost, response latency, and invocation failures.
  arXiv  Detail & Related papers  (2026-01-05T07:35:12Z)
• Astraea: A State-Aware Scheduling Engine for LLM-Powered Agents [12.884297990127985]
  Astraea is a service engine designed to shift the optimization from local segments to the global request lifecycle.<n>It employs a state-aware, hierarchical scheduling algorithm that integrates a request's historical state with future predictions.<n>Astraea reduces average JCT by up to 25.5% compared to baseline methods.
  arXiv  Detail & Related papers  (2025-12-16T06:55:10Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• Structure-aware reinforcement learning for node-overload protection in mobile edge computing [3.3865605512957457]
  This work presents an adaptive admission control policy to prevent edge node from getting overloaded. We extend the framework to work for node overload-protection problem in a discounted-cost setting. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms.
  arXiv  Detail & Related papers  (2021-06-29T18:11:41Z)

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.