Improving Aviation Safety Analysis: Automated HFACS Classification Using Reinforcement Learning with Group Relative Policy Optimization

• URL: http://arxiv.org/abs/2508.21201v1
• Date: Thu, 28 Aug 2025 20:35:03 GMT
• Title: Improving Aviation Safety Analysis: Automated HFACS Classification Using Reinforcement Learning with Group Relative Policy Optimization
• Abstract summary: We introduce an automated HFACS classification framework for aviation safety analysis.<n>Our approach incorporates a multi-component reward system tailored for aviation safety analysis.<n>The resulting GRPO-optimized model achieved noticeable performance gains.
• Score: 0.29494468099506904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analyzing the human factors behind aviation accidents is crucial for preventing future incidents, yet traditional methods using the Human Factors Analysis and Classification System (HFACS) are limited by scalability and consistency. To address this, we introduce an automated HFACS classification framework for aviation safety analysis that utilizes Reinforcement Learning with Group Relative Policy Optimization (GRPO) to fine-tune a Llama-3.1 8B language model. Our approach incorporates a multi-component reward system tailored for aviation safety analysis and integrates synthetic data generation to overcome class imbalance in accident datasets. The resulting GRPO-optimized model achieved noticeable performance gains, including a 350% increase in exact match accuracy (from 0.0400 to 0.1800) and an improved partial match accuracy of 0.8800. Significantly, our specialized model outperforms state-of-the-art LLMs (Large Language Models), including GPT-5-mini and Gemini-2.5-fiash, on key metrics. This research also proposes exact match accuracy in multi-label HFACS classification problem as a new benchmarking methodology to evaluate the advanced reasoning capabilities of language models. Ultimately, our work validates that smaller, domain-optimized models can provide a computationally efficient and better solution for critical safety analysis. This approach makes powerful, low-latency deployment on resource-constrained edge devices feasible.

Related papers

• Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security [0.0]
  This study explores the use of machine learning algorithms to classify security levels of contingencies in power systems into safe, moderate or severe classes.<n>Newton-Raphson load flow method extracts system data from contingency scenarios.<n>Performance is assessed by precision, recall and F1 score, with priority given to the severe contingency classes.
  arXiv  Detail & Related papers  (2026-09-03T17:04:38Z)
• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis [0.0]
  Large language models (LLMs) show strong performance in natural language understanding and generation, but their evaluation and adaptation to domain-specific constraints in telecommunications customer support remain limited.<n>We present a systematic study of parameter-efficient fine-tuning (PEFT) using Low-Rank Adaptation (LoRA) applied to Qwen2.5-3B to build a domain-specific conversational assistant.<n>We introduce a synthetic data generation approach based on a glossary of 52 industry-specific terms, producing approximately 30,000 training examples across 1,560 distinct problem scenarios via a generative pipeline powered by Gemini Gemini.
  arXiv  Detail & Related papers  (2026-04-17T09:56:18Z)
• AWPO: Enhancing Tool-Use of Large Language Models through Explicit Integration of Reasoning Rewards [60.2998874976509]
  We propose advantage-weighted policy optimization (AWPO) to integrate explicit reasoning rewards to enhance tool-use capability.<n>AWPO incorporates variance-aware gating and difficulty-aware weighting to adaptively modulate advantages from reasoning signals.<n>Experiments demonstrate that AWPO achieves state-of-the-art performance across standard tool-use benchmarks.
  arXiv  Detail & Related papers  (2025-12-22T08:07:00Z)
• AI Agentic Vulnerability Injection And Transformation with Optimized Reasoning [2.918225266151982]
  This paper introduces a novel framework designed to automatically introduce realistic, category-specific vulnerabilities into secure C/C++s to generate datasets.<n>The proposed approach coordinates multiple AI agents that simulate expert reasoning, along with function agents and traditional code analysis tools.<n>Our experimental study on 116 code samples from three different benchmarks suggests that our approach outperforms other techniques with regard to dataset accuracy.
  arXiv  Detail & Related papers  (2025-08-28T14:59:39Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [78.18946529195254]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs [51.21041884010009]
  Ring-lite is a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL)<n>Our approach matches the performance of state-of-the-art (SOTA) small-scale reasoning models on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-06-17T17:12:34Z)
• Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach [2.8626097661711394]
  Reinforcement Learning from Human Feedback has achieved notable success in steering models, but is complex and can be unstable.<n>Recent approaches such as Direct Preference Optimization (DPO) simplify preference-based fine-tuning but may introduce bias or trade-off certain objectives.<n>We propose a Group Relative Policy Optimization framework with a multi-label reward regression model to achieve safe and aligned language generation.
  arXiv  Detail & Related papers  (2025-03-26T05:50:33Z)
• Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture [68.83934802584899]
  We propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC)<n>FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering.<n> Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.
  arXiv  Detail & Related papers  (2025-03-24T01:56:30Z)
• Privacy-Preserved Automated Scoring using Federated Learning for Educational Research [1.2556373621040728]
  We propose a federated learning (FL) framework for automated scoring of educational assessments.<n>We benchmark our model against two state-of-the-art FL methods and a centralized learning baseline.<n>Results show that our model achieves the highest accuracy (94.5%) among FL approaches.
  arXiv  Detail & Related papers  (2025-03-12T19:06:25Z)
• Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach [9.88281854509076]
  We implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity.<n>Our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA.<n>To interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global)
  arXiv  Detail & Related papers  (2024-12-05T14:21:18Z)
• SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [58.29510889419971]
  Existing benchmarks for evaluating the security risks and capabilities of code-generating large language models (LLMs) face several key limitations.<n>We introduce a general and scalable benchmark construction framework that begins with manually validated, high-quality seed examples and expands them via targeted mutations.<n>Applying this framework to Python, C/C++, and Java, we build SeCodePLT, a dataset of more than 5.9k samples spanning 44 CWE-based risk categories and three security capabilities.
  arXiv  Detail & Related papers  (2024-10-14T21:17:22Z)
• Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models [94.39278422567955]
  Fine-tuning large language models (LLMs) on human preferences has proven successful in enhancing their capabilities.<n>However, ensuring the safety of LLMs during fine-tuning remains a critical concern.<n>We propose a supervised learning framework called Bi-Factorial Preference Optimization (BFPO) to address this issue.
  arXiv  Detail & Related papers  (2024-08-27T17:31:21Z)
• Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy [6.368679897630892]
  This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA) It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.
  arXiv  Detail & Related papers  (2021-09-21T14:42:58Z)
• Neural Network Repair with Reachability Analysis [10.384532888747993]
  Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. This research proposes a framework to repair unsafe DNNs in safety-critical systems with reachability analysis.
  arXiv  Detail & Related papers  (2021-08-09T17:56:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.