LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning

[2608.15041] LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.15041 (cs) [Submitted on 15 Aug 2026] Title: LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning Authors: Changhong He , Jinda Gao , Xinkuan Liu , Le Zhang , Xizi Luo , Yu Mei View a PDF of the paper titled LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning, by Changhong He and 5 other authors View PDF HTML (experimental) Abstract: Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational information is heterogeneous, and low-level actions must satisfy strict constraints. We propose an LLM-based hierarchical framework in which the LLM coordinates interacting units based on heterogeneous operational context, while task-specific controllers or optimizers generate executable and constraint-aware actions. We further introduce Continuation-Aware GRPO to capture the consequences of coordination decisions over subsequent control intervals. Rather than judging a decision only by its immediate outcome, the method also evaluates how the system evolves afterward under the current policy. We validate the framework on multi-ramp traffic control and virtual power plant (VPP) energy management, using simplified system models for training and more realistic simulators for evaluation. Across both tasks, the proposed method consistently outperforms direct task-specific control and optimization, end-to-end reinforcement learning, rule-based and RL-based hierarchical coordination, and prompting-only LLM coordinators, demonstrating the value of heterogeneous-context reasoning, hierarchical execution, and continuation-aware policy learning. Comments: 30 pages, 5 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15041 [cs.AI] (or arXiv:2608.15041v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15041 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Changhong He [ view email ] [v1] Sat, 15 Aug 2026 05:00:02 UTC (581 KB) Full-text links: Access Paper: View a PDF of the paper titled LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning, by Changhong He and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning#610995
• FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs#429309
• Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient#131217
• Training Dynamics and Induced Preconditioning of ReLU Policy Gradient for Scalar LQR#1115537
• Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision#1034609