Jun 2026· arXiv.org· Vol abs/2606.26960· 0 citations· 39 references
Computer Science
TL;DR
NetLLMeval is presented, a framework for automatically evaluating LLM-based systems on network administration tasks by leveraging live network emulation to derive ground truth without human intervention, and is released open-source to support reproducible benchmarking of future models and solver designs.
Abstract
The growing complexity of computer networks, driven by cloud-native architectures, heterogeneous devices, and distributed systems, places increasing pressure on network administrators who must simultaneously manage configuration, troubleshooting, and security under tight operational constraints. Large Language Models (LLMs) have emerged as a promising tool to assist and partially automate these tasks, yet their systematic evaluation in networking scenarios remains an open challenge. Existing benchmarks rely on static reference outputs or manual expert validation, neither of which scales to the diversity of real-world network states or to the variety of orchestration strategies -- from monolithic prompting to fully agentic pipelines --through which LLMs are increasingly deployed. In this paper, we present NetLLMeval, a framework for automatically evaluating LLM-based systems on network administration tasks by leveraging live network emulation to derive ground truth without human intervention. Through a full-factorial study of 24000 runs spanning 10 foundation models, 4 solver architectures, 10 task types, and 6 network topologies of increasing complexity, we show that solver design has a great impact on accuracy -- lifting a 14B open-weight model from 0.43 to 0.88 correctness -- and that such locally-deployable models can match trillion-parameter frontier systems under the right configuration. NetLLMeval is released open-source to support reproducible benchmarking of future models and solver designs.
Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poorly understood. An essential prerequisite is to assess such agents in a realistic but risk-free environment. Existing benchmarks, however, fall short: they often treat configuration as static command generation or rely on overly simplified settings. Such evaluations understate the core challenges of network configuration, where correctness requires reasoning about protocol complexity and topology dependence. We present NetConfArena, an executable benchmark for evaluating LLM agents in closed-loop network configuration. NetConfArena places agents in emulated multi-device networks, provides a standardized and compact action interface for task execution, and evaluates the resulting network behavior with hidden task-specific executable test cases. The benchmark relies on an LLM-assisted, emulation-grounded pipeline, which converts human-oriented network materials into reusable parameterized task templates. We evaluate representative LLM agents on 480 task instances instantiated from 96 protocol-focused task templates, yielding 3840 execution trajectories, and show that failures are not limited to command errors. The failures also reveal gaps in task-specification adherence and robust planning and execution. These findings suggest two future directions: using validated trajectories as supervision signals to improve foundation models, and designing harness mechanisms that make agent execution more reliable and accountable.
Chang Liu, Xiaohui Xie, Xinyi Chen et al.· 0 citations
This work presents Aries, a full-stack experimentation framework that separates task semantics from execution configurations, reconstructs cross-component agent trajectories with correlated system telemetry, and exposes stateful tool execution through a consistent interface across heterogeneous sandbox substrates.
Leonid Kondrashov, Hongrui Liu, JooYoung Park et al.· 1 citation
Experimental results show that the proposed self-calibrating agentic framework successfully profiles the zero-knowledge workloads, achieving a higher accuracy than baseline LLM agents and establishing a robust foundation for deploying autonomous AI in decentralized infrastructures.
Fin Gentzen, Marla Grunewald, Iulisloi Zacarias et al.· 0 citations
A solver-grounded design principle is presented: a numerical result is reported only when it originates from a trusted tool and passes explicit verification, and a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency is proposed.
Daniel Rojas, Abdulwahab Albassam, Aidan G. Leung et al.· 0 citations
Large Language Models (LLMs) are serving as a catalyst of change for research practices, touching the daily lives of staff scientists, software engineers, and system administrators. The developments promise new degrees of autonomy, where categories of human work and decision making are replaced by autonomous, goal-oriented systems. This transition necessitates novel architectural paradigms and solid understanding of the strengths and limitations of LLMs. In this work, we design agents to intelligently deliver the entire life-cycle of an HPC application experimental run in cloud -- creation and build of a container, deployment in Kubernetes, optimization, and orchestration of a scaling study. We pursue this task for four well-known HPC applications to build multi-platform images and optimize across 21 instance types in Kubernetes. We demonstrate successful linear scaling with patterns approved by human experts, designs that improve work time to completion, and review suggested best practices for agentic design and collaboration.
Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defined semantics, and finally consolidation around those abstractions into a platform that applications can portably target. POSIX did this for classical operating systems; Kubernetes did it for the cloud. Agentic AI systems - autonomous, LLM-driven agents that plan, use tools, maintain memory, and collaborate - are currently in the experimentation phase of the third such wave. dozens of frameworks and protocols have emerged, but no community consensus exists on what the core abstractions are or what guarantees they carry. Without that consensus, agentic applications cannot be written portably, platforms cannot compose reliably, and the field cannot advance beyond prototype deployments. We argue that the path forward is to follow the prior-wave methodology: derive new agentic abstractions by extending classical OS and cloud OS primitives to stochastic, natural-language-mediated execution, specify their semantics precisely, and consolidate around them - just as POSIX and Kubernetes consolidated their respective waves.