Energy-Efficient Hierarchical Planning and Action Compression for Edge-Deployed Large Language Model Reasoning Systems - Brent Rhodes; Jordan Wood; Noah A. Simmons (Computational Intelligence Systems, 2026)
The deployment of large language models on edge devices for real-time reasoning tasks introduces substantial challenges related to energy consumption, latency, and computational resource constraints. This paper proposes a novel framework that integrates hierarchical planning with action compression to address these challenges, enabling energy-efficient reasoning in edge-deployed large language model systems. The hierarchical planning approach decomposes complex reasoning tasks into high-level strategic goals and low-level execution steps, reducing the computational overhead associated with full autoregressive generation. Action compression techniques further minimize the number of tokens and intermediate reasoning steps by leveraging learned abstractions and context distillation. We examine the architectural trade-offs between planning depth and energy savings, the governance implications of deploying compressed reasoning pipelines in resource-constrained environments, and the robustness and fairness considerations that arise when reducing model expressivity for efficiency. Drawing on cross-domain comparisons from robotics, autonomous systems, and distributed computing, we argue that a structured, multi-level planning paradigm combined with compression strategies can significantly decrease energy footprint without catastrophic loss of reasoning quality. The framework also enables more predictable latency, improved scalability across heterogeneous edge hardware, and enhanced sustainability for large-scale deployment. We discuss policy implications for carbon-aware computing and equitable access to intelligent edge services. This work contributes a systems-level perspective on making advanced reasoning capabilities viable for edge deployment while maintaining responsible stewardship of energy resources.