# An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

arXiv: https://arxiv.org/abs/2605.13221 · HTML: https://mlsanity.com/paper/2605.13221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 74 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 20 of 20)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [Interactive AI with Retrieval-Augmented Generation for Next Generation Networking](https://mlsanity.com/paper/2401.11391/) (2024)
- [Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective](https://mlsanity.com/paper/2411.14258/) (2024)
- [MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning](https://mlsanity.com/paper/2505.20096/) (2025)
- [Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration](https://mlsanity.com/paper/2504.04915/) (2025)
- [MARS: toward more efficient multi-agent collaboration for LLM reasoning](https://mlsanity.com/paper/2509.20502/) (2025)
- [Zero-Shot Verification-guided Chain of Thoughts](https://mlsanity.com/paper/2501.13122/) (2025)
- [DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV](https://mlsanity.com/paper/2208.02447/) (2022)
- [GraphThought: Graph Combinatorial Optimization with Thought Generation](https://mlsanity.com/paper/2502.11607/) (2025)
- [Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2506.23678/) (2025)
- [Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach](https://mlsanity.com/paper/2407.00978/) (2024)
- [AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing](https://mlsanity.com/paper/2507.01376/) (2025)
- [A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor](https://mlsanity.com/paper/2405.16887/) (2024)
- [Evaluating Chain-of-Thought Reasoning through Reusability and Verifiability](https://mlsanity.com/paper/2602.17544/) (2026)
- [TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network](https://mlsanity.com/paper/2411.02617/) (2024)
- [Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles](https://mlsanity.com/paper/2501.15544/) (2025)
- [Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniques](https://mlsanity.com/paper/2601.00891/) (2025)
- [Task scheduling system for UAV operations in indoor environment](https://mlsanity.com/paper/1604.06223/) (2016)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Scheduling with LLM-Assisted Contract Net Negotiation for Stream Processing in Mobile Edge Computing](https://mlsanity.com/paper/2608.12371/) (2026)

## Abstract (arXiv)

In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC). This joint operation forms a hybrid scheduling problem, where physical logistics decisions are coupled with computational task scheduling. In this paper, UAVs collect finished products from manufacturing stations and transport them back to a central depot. Meanwhile, computational tasks generated by industrial sensor devices at these stations are processed locally, at UAVs, or offloaded via UAVs to the cloud. This coupling makes the problem challenging. A UAV can provide MEC services only during its service window at a station, so routing decisions directly determine when UAV-assisted offloading is available. Routing decisions also affect the UAV energy budget and the availability of onboard computing and communication resources for computational task execution under task deadline constraints. To address this, we propose an agentic-AI-assisted optimization framework with two components. First, we develop an agentic AI that combines large language models, retrieval-augmented generation, and chain-of-thought reasoning to translate user input into an interpretable mathematical formulation for the hybrid scheduling problem. Second, we design a hierarchical deep reinforcement learning approach based on proximal policy optimization (PPO), where the upper layer learns UAV routing and the lower layer optimizes per-slot task execution and resource allocation. Simulation results show that the proposed framework yields more consistent formulations, while the hierarchical PPO achieves full product collection in 99.6% of the last 500 episodes and maintains a 100% deadline satisfaction rate, with more stable performance than the advantage actor-critic approach.