Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed papers, demonstrating the potential of closed-loop artifact control for end-to-end LLM-assisted wireless optimization research.
Abstract
Large language model (LLM) agents have shown growing capabilities in tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a particular focus on wireless resource allocation optimization, an essential area for characterizing the fundamental performance limits of wireless systems and enhancing their practical performance under dynamic channel and network conditions. Specifically, we propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system that transforms an initial research topic into a complete research package. WARA organizes the research workflow into three phases: 1) research gap identification and problem proposal, 2) optimization modeling, algorithm design, and experimentation, and 3) research deliverable construction. Each phase follows an artifact-mediated process, in which structured upstream artifacts are consumed to generate downstream outputs. Controller-managed gates validate these artifacts and maintain consistency among problem formulations, algorithms, experiments, and research claims. When validation fails, WARA repairs only the affected artifact instead of restarting the entire workflow. We further design an LLM-based ScoringAgent to evaluate manuscript-level research validity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed papers. These results demonstrate the potential of closed-loop artifact control for end-to-end LLM-assisted wireless optimization research. The source code is available at https://github.com/guoyuan-dotcom/WARA_CUHKSZ
A ScholForge - hierarchical multi-agent LLM system that tackles end-to-end software engineering research end-to-end, with a natural-language research goal, and produces working code, experimental results, and a compiled manuscript without human intervention.
A. Stepin, Boris Tolstokulakov, V. Kulikov et al.· SIGSOFT FSE Companion· 0 citations
Cloud robotics relies on the timely uplink of high-volume sensing streams, yet dynamic environments continually shift the feasible combinations of trajectories, active-robot count, and per-robot QoS. Because existing approaches formulate trajectory planning (TP) and radio resource management (RRM) as a single fixed optimization problem, they cannot reconfigure these coupled decisions as conditions evolve, resulting in transient QoS violations. However, evolving operator intents change which quantities-such as the active-robot count and per-robot QoS-are fixed, optimized, or relaxed. Furthermore, the computational cost of evaluating trajectory-dependent wireless conflicts has made it difficult to build large-scale Digital-Twin-in-the-Loop (DITL) testbeds responsive enough for such dynamic orchestration. We present LUCID, an LLM-agent--orchestrated, uplink-aware cloud-robotics pipeline that moves TP--RRM from solving a fixed formulation to dynamically orchestrating optimization problem schemas within a DITL environment. Driven by the operator's high-level intent, LUCID treats the TP--RRM formulation as a bounded template whose variables, objectives, and constraints are dynamically configured, while SimBridge enables repeated ray-tracing evaluation by converting large-scale robotics scenes into wireless-ready DTs. By integrating collision-free path planning with a spectral-radius RRM validator, LUCID identifies wireless bottlenecks and restructures the problem schema on the fly to efficiently find the verified feasible state. Experiments confirm that LUCID robustly adapts to changing intents, active-robot counts, and scenes, while a multimodal surrogate model, FastConfigNet, reduces planning latency.
This work proposes OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code, and introduces a novel multi-loop validation architecture with four specialized feedback mechanisms.
Adriana Laurindo Monteiro, Nayse Fagundes, G. Langeloh et al.· 0 citations
With the rapid expansion of the Internet of Things (IoT) and heterogeneous wireless networks, Age of Information (AoI) has emerged as a critical metric for evaluating information freshness in real-time systems. AoI-oriented access optimization in heterogeneous multiple access networks is challenging because legacy access mechanisms, such as TDMA and ALOHA, may coexist over a shared channel, while conventional rule-based and learning-based methods often suffer from limited adaptability, slow convergence, and poor interpretability. In this paper, we propose Reflex-Core, an LLM-agent-based framework for AoI-oriented adaptive access in heterogeneous wireless networks. Reflex-Core adopts an “Observe-Reflect-Decide-Execute” closed-loop mechanism to refine transmission strategies through semantic feedback and historical memory. To provide an analytical foundation for reflection-guided strategy refinement, we derive a drift-plus-penalty design principle and construct a reflection-cycle-level reward target that jointly captures weighted AoI reduction and collision cost. This reward target guides reflection selection, reward model training, and PPO-based post-training. Based on Reflex-Core, we develop the Reflexive Multiple Access (RMA) protocol and a priority-aware RMA variant for differentiated freshness requirements. We further discuss an asynchronous edge-assisted implementation, where LLM-based reflection can be offloaded without blocking slot-level random access. Simulation results show that RMA reduces AoI by up to 14.9% compared with representative baselines and maintains robust performance in dynamic and priority-aware scenarios. Additional scalability and backbone-sensitivity experiments further confirm that Reflex-Core remains effective in a 20-node heterogeneous scenario with varied ALOHA loads and is robust when LongChat-7B-16k is replaced by Qwen2.5-7B-Instruct.
Fang Liu, Erchao Zhu, Jiedan Tan et al.· IEEE Open Journal of the Com...· 0 citations
This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts and reveals that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.
Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks. Because sensing and communication share hardware, spectrum, and waveform resources, ISAC design now involves many tightly coupled choices, including waveform selection, sensing algorithm setup, resource scheduling, and deployment planning. This design space is already too large to manage well through manual tuning or isolated optimizers. This article introduces the \textit{Agent Compiler}, a large language model (LLM)-enabled compilation layer that translates high-level engineering intent into complete and executable ISAC system configurations. The Agent Compiler works in four stages: intent parsing, task decomposition, policy graph synthesis, and infrastructure mapping. It produces a verifiable intermediate representation called the ISAC Policy Graph (IPG). A runtime engine then deploys the compiled configuration and supports closed-loop adaptation at three levels: fast parameter updates, partial recompilation of affected subgraphs, and full workflow recompilation. The core design principle is strict time-scale separation: the LLM handles slow-loop strategic decisions, while proven algorithms retain real-time control in the fast loop. A UAV-assisted disaster rescue example illustrates the full compilation process. We also discuss open issues, including compilation latency, output reliability, constraint verification, and pipeline security, to guide future research.
Lijie Zheng, Xudong Zhong, Baoquan Ren et al.· 0 citations