{"ID":6138064,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T03:40:41.502488863Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06964","arxiv_id":"2607.06964","title":"End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent","abstract":"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","short_abstract":"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 La...","url_abs":"https://arxiv.org/abs/2607.06964","url_pdf":"https://arxiv.org/pdf/2607.06964v1","authors":"[\"Amin Tabrizian\",\"Arsyi Aziz\",\"Aarifah Ullah\",\"Mahyar Ghazanfari\",\"Pouria Razzaghi\",\"Peng Wei\"]","published":"2026-07-08T03:40:38Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}