FlightLLM, a prior-guided semantic LLM-based approach for interpretable flight safety analysis that achieves competitive classification performance while generating direct and reasonable explanations for event causes is proposed.
Abstract
Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explainable AI techniques, such as feature importance maps, often require considerable domain knowledge to translate them into operationally meaningful explanations. Large Language Models (LLMs), which excel at language reasoning, bring a promising solution to this issue. However, applying LLMs in this domain presents key challenges such as modal inconsistency, limited classification ability, scarcity of task-specific data for fine-tuning, and lack of domain knowledge. To overcome these challenges, we propose FlightLLM, a prior-guided semantic LLM-based approach for interpretable flight safety analysis. Specifically, we first perform feature engineering to address modal inconsistency, combining statistical descriptors with physically meaningful flight indicators. This representation is further processed by a Semantic Discretization module, which converts abstract numerical patterns into qualitative descriptions that are more compatible with language reasoning. In addition, since LLMs are not inherently strong classifiers, CatBoost is incorporated as a statistical expert, and its prediction results are injected into the prompt as prior guidance. A contrastive few-shot learning strategy is further adopted to compensate for limited data. Finally, we design structured prompts to embed aviation-specific knowledge into the inference process. Using hard landing, a representative risk event with complex causal mechanisms, as an anchor point, we evaluate FlightLLM on a dataset of 704 real-world A320 flight samples. Experimental results show that the proposed approach achieves competitive classification performance while generating direct and reasonable explanations for event causes.
TableManeuver is proposed, an explainable LLM-based FMR method that reformulates multivariate flight parameter time series as table-understanding inputs and combines recognition accuracy, cross-aircraft robustness, and readable step-by-step reasoning evidence, offering a practical route for applying LLMs to aviation time series analysis.
Liqiang Ren, Haipeng Wang, Xinlong Pan et al.· Entropy· 0 citations
In high-risk industrial settings, leveraging large language models (LLMs) for automated accident analysis and generating safety reports has emerged as an efficient workflow. However, this approach is fundamentally constrained by the models’ inherent knowledge limitations, frequently resulting in analyses that lack domain-specific understanding and regulatory alignment.
To tackle this issue, we introduce SafeRAG, a multistage retrieval-augmented framework for safety report generation. Specifically, the framework uses an entity-centric approach that prompts the LLMs to internally generate domain-specific knowledge. Concurrently, it performs a hierarchical retrieval of external regulations relevant to the accident at topic, concept, and context levels. To obtain well-structured reports, we leverage prompt engineering, integrating internal and external knowledge. Furthermore, a domain-expert persona is also assigned to help LLMs analyze accidents from a specific perspective. To evaluate our approach, we construct a data set from 10,818 accident-description/report pairs collected from real-world industry reports. Experiments show that SafeRAG substantially outperforms baseline LLMs on metrics that include bidirectional encoder representations from transformers (BERTScore) and bidirectional auto-regressive transformers (BARTScore), demonstrating the effectiveness of our approach.
Qi Cao, Chiyu Zhang, Junxiong Ning et al.· SPE Journal· 0 citations
Voluntary safety reports provide valuable information for identifying potential risks and improving safety management in civil aviation. However, these reports are often large in volume, unstructured in format, and rich in domain-specific terminology, making manual analysis costly, inefficient, and difficult to scale. To address these challenges, this paper proposes TMCAS, an efficient large language model-assisted topic modeling framework for civil aviation safety reports. The proposed framework combines domain-adapted text embeddings, density-based clustering, representative sampling, noise repair, and large language model-based topic generation. Specifically, a contrastive learning-based fine-tuning strategy is introduced to enhance the semantic representation of aviation safety texts. An HDBSCAN-based clustering and sampling mechanism is then designed to select representative reports and reduce the computational cost of large language model inference, while a noise-repair strategy is used to improve topic coverage. Finally, large language models are employed to generate interpretable sentence-level topic labels and descriptions. Experiments demonstrate that TMCAS achieves superior clustering and interpretability while substantially reducing inference cost compared with document-wise LLM baselines.
Current state-of-the-art LLMs as a tool for maritime navigation, which includes both codified rules in the Collision Regulations and uncodified best practices summarized in the concept of ``Good Seamanship'' are explored.
Julius Wirbel, P. N. Hansen, Line Clemmensen et al.· 0 citations
Understanding and complying with traffic regulations is a safety-critical requirement for autonomous driving, yet remains challenging due to the diversity and context dependence of traffic signage. Importantly, regulation understanding is not a simple recognition task, but a reasoning problem: whether a rule applies depends on interpreting the sign in relation to the spatial layout of lanes and scene context. To support such reasoning, MapDR provide fine-grained annotations that link each traffic sign's regulatory rules to the specific lanes they govern. Existing methods, however, largely treat this as direct sequence prediction, ignoring the underlying reasoning that connects sign semantics and map structure. To address this limitation, we explicitly incorporate reasoning into this task and propose a framework that equips vision-language models (VLMs) with chain-of-thought (CoT) capabilities. We first design a scalable CoT curation pipeline that bootstraps rationales from a strong LLM through a two-round strategy and employs a VLM-based verifier to filter out incorrect cases, yielding a high-quality set of (CoT, answer) pairs. Building on this foundation, we adopt a two-stage training scheme: supervised fine-tuning (SFT) to teach rationale-to-answer generation, followed by GRPO reinforcement learning with answer-grounded, fine-grained rewards to further improve final answer accuracy. Extensive experiments on MapDR show that our approach significantly improves both interpretability and accuracy, establishing the first reasoning-based framework for regulation-aware autonomous driving.
Yueru Luo, Xu Yan, Changqing Zhou et al.· 0 citations
Experimental results show that PD-CoTD significantly enhances the mathematical reasoning abilities of SLMs and provides an effective solution for deploying powerful reasoning in resource-constrained environments.
Zhe Wang, Long Zhang, Fengbo Zheng et al.· International journal of sof...· 0 citations