WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents

[2608.14573] WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14573 (cs) [Submitted on 7 Jun 2026] Title: WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents Authors: Yuan Guo , Yilong Chen , Chao Hu , Xianghao Yu , Liang Hong , Jie Xu View a PDF of the paper titled WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents, by Yuan Guo and 5 other authors View PDF HTML (experimental) Abstract: Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a focus on wireless resource allocation optimization. We propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system for automated wireless optimization research. Given only an initial topic, WARA decomposes the workflow into three phases: research gap identification and problem proposal, wireless optimization modeling, algorithm design and experimentation, and research deliverable construction. Across these phases, WARA uses artifact-mediated control: upstream artifacts are consumed as inputs, structured outputs are stored for downstream use, and controller-managed gates validate consistency among models, algorithms, experiments, and claims. When validation fails, WARA repairs only the responsible artifact instead of restarting the whole workflow. We present a representative wireless resource allocation case study showing how WARA converts an initial topic into a complete research package with executable evidence and a synthesized technical manuscript. We further design a structured LLM-based ScoringAgent to evaluate manuscript-level research validity and optimization research maturity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed technical papers. These results indicate that closed-loop artifact control is a promising path toward end-to-end LLM-assisted wireless optimization research. The source code is available at this https URL . Subjects: Networking and Internet Architecture (cs.NI) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14573 [cs.NI] (or arXiv:2608.14573v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14573 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuan Guo [ view email ] [v1] Sun, 7 Jun 2026 01:31:57 UTC (1,882 KB) Full-text links: Access Paper: View a PDF of the paper titled WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents, by Yuan Guo and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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