mlsanityData to 14 Oct 2026

End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent

arXiv 2607.06964 ↗First posted 8 Jul 2026cs.RO cs.AIcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) aircraft deployment. Flight planning traditionally relies on classic algorithms that struggle to incorporate flexible human preferences. We present FRAMe, an End-to-End Large Language Model (LLM) Flight Planning tool with RAG-based Memory and Multi-modal Coach Agent. Our system integrates a planner LLM with a multi-modal coach agent and retrieval augmented generation (RAG)-based memory to generate flight plans that satisfy mission constraints while aligning with human flight operator preferences. We demonstrate the system in a range of real-world-inspired scenarios of varying difficulty levels. Across four LLMs, the full FRAMe system (RAG and coach) yields the highest validity for every planner (up to 93.8% aggregate, 99% on Easy scenarios for the strongest planner) and shifts preference-relevant metrics in the operator-favored direction where the metric has headroom. FRAMe signifies how advanced LLMs can be deployed for human-centric mission planning, translating natural language instructions into safe, efficient, and flexible flight routes. The code is available at: github.com/amin-tabrizian/FlightPlanningLLMs

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 23 references that are in the map. First posted on arXiv 8 Jul 2026.

Builds on

12 of 23 references.

References · 12

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  4. 2023Dynamic Planning with a LLMcited by 61
  5. 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planningcited by 45
  6. 2024STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learningcited by 44
  7. 2024Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguitycited by 27
  8. 2024P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Taskcited by 15
  9. 2024Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planningcited by 11
  10. 2025GSCE: A Prompt Framework with Enhanced Reasoning for Reliable LLM-driven Drone Controlcited by 7
  11. 2024What's the Plan? Evaluating and Developing Planning-Aware Techniques for Language Modelscited by 4
  12. 2024Can LLMs plan paths with extra hints from solvers?cited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025LLMs Can Plan Only If We Tell Them
  2. 2025HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance
  3. 2024RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models
  4. 2026PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models
  5. 2025Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification
  6. 2025Coarse-to-Fine Grounded Memory for LLM Agent Planning
  7. 2024RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents
  8. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks