Multi-modal Causal RAG for Aviation Accident Analysis and Risk Prediction
Bibliographic record
Abstract
Aviation safety analysis has traditionally relied on structured reports and expert-driven causal reasoning. Under-standing aviation accident requires a holistic approach that integrates multi-modal data, including textual reports, images, and structured knowledge representations. This paper proposes a novel multi-modal retrieval and analysis framework that integrates Natural Language Processing (NLP), Causal Relation Extraction, CLIP-based image embedding, and Latent Con-textual Modeling (LCM) with Retrieval-Augmented Generation (RAG) to link textual and visual aviation accident evidence. The system enables users to upload new incident images, such as those from newspapers or social media, and automatically match them with similar historical accidents, revealing causes and contributing factors through generative explanation. This pipeline demonstrates the feasibility of a next-generation decision-support tool for aviation safety that is interpretable, context-aware, and multi-modal. Experimental results demonstrate that our model effectively identifies and aligns latent representations across modalities, while the LLM generates coherent, contextually grounded explanations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".