Jul 2026· International Conferences on Human-Machine Systems· pp. 192-197· 0 citations· 18 references
Abstract
We introduce a retrieval-augmented synthesis pipeline for deriving structured hazard logs for emerging aviation concepts from historical aviation accident evidence. NTSB accident reports are transformed into a schema-consistent corpus combining coded findings and narrative mechanisms for semantic indexing. Mechanism-level retrieval uses sentence-transformer embeddings, a FAISS inner-product index, evidence-derived seed extraction, and maximal marginal relevance to obtain diversified, scenario-relevant cases. Hazard generation is constrained by strict JSON schema validation, one-to-one evidence binding, explicit causal sequencing, and enforced primary-mechanism uniqueness. A multipass strategy with critic-based filtering and deterministic de-duplication improves robustness against mechanism repetition and evidence drift. Evaluation of an urban eVTOL safety-landing scenario compares locally deployed open-weight models under identical constraints. Retrieval augmentation supports mechanism-specific and traceable hazard derivation compared to unconstrained scenario-based prompting. Mistral-7B requires multipass generation to achieve acceptable mechanism diversity and evidence consistency, whereas GPT-OSS-20b produces structurally valid and mechanism-differentiated hazard sets in a single pass. Scaling to GPT-OSS-120b yields only marginal improvements at substantially higher computational cost.
This paper proposes TMCAS, an efficient large language model-assisted topic modeling framework for civil aviation safety reports that achieves superior clustering and interpretability while substantially reducing inference cost compared with document-wise LLM baselines.
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
Post-release user feedback is a major control signal for maintenance and evolution in modern software development, yet it is noisy, fragmented, and difficult to translate into developer-usable issue specifications. Large Language Models (LLMs) can assist this transformation, but they often hallucinate or over-commit when evidence is weak, conflicting, or incomplete, limiting their robustness in automated software engineering workflows. We propose AGR (Agent-Governed Retrieval-Augmented Generation), a framework that regulates evidence acquisition and generation decisions via agentic control. AGR first applies an agentic triage step to filter low-signal or off-topic feedback, then retrieves evidence from a three-category hierarchy comprising official documentation, historical bug reports, and targeted web sources. It further performs confidence-weighted fusion across authoritative categories and uses an agentic decision module to verify relevance and sufficiency, trigger additional retrieval or online search when needed, reuse prior reports via memory, and abstain when evidence-supported grounding cannot be established. We evaluate AGR on two open-source software ecosystems, Firefox and VS Code. Results show that AGR achieves strong decision accuracy in triage and evidence verification, and produces more actionable and engineering-useful issue specifications than both raw feedback and a strong LLM baseline, while reducing unsupported details.
Zhiyao Wang, Jialong Li, Xiujing Guo et al.· International Conference on...· 0 citations
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
A visual knowledge enhancement framework for construction OHS visual question answering (VQA) based on multimodal large language models (MLLMs) and retrieval-augmented generation (RAG) is proposed, augmenting managerial capacity for reliable and objective OHS hazard prevention.
Yang Liu, Luping Li, Xing Su et al.· Journal of Management in Eng...· 0 citations
Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables.
Abdulaziz Almaleh, Abdullah M. Alqahtani· Mathematics· 0 citations
