NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation

TL;DR

This paper presents NOTAM-Evolve, a novel framework using large language models and knowledge graphs to improve automated interpretation of complex, cryptic NOTAM messages, significantly enhancing accuracy over existing methods.

Contribution

We introduce a self-evolving LLM framework with knowledge grounding and a new NOTAM dataset, advancing automated NOTAM interpretation through deep parsing and autonomous learning.

Findings

Achieved 30.4% accuracy improvement over base LLM

Developed a new benchmark dataset of 10,000 NOTAMs

Demonstrated effectiveness of knowledge-guided self-evolving approach

Abstract

Accurate interpretation of Notices to Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to shallow parsing, failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as deep parsing, a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a large language model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state of the art on the task of structured NOTAM interpretation.