What Will Happen Next: Large Models-Driven Deduction for Emergency Instances
Proposes WLDS system combining factual and logical calibration for multi-domain emergency scenario deduction with 8.5% accuracy boost.
Key Findings
Methodology
The WLDS framework integrates large language models (e.g., GPT-4) for knowledge transfer across domains, generating diverse emergency scenarios. It employs a four-step process: knowledge transformation, multi-path world line deduction with temperature-controlled randomness, dual calibration (factual retrieval and causal logic verification), and multimodal visualization. The factual calibration retrieves domain facts via real-time knowledge bases, correcting content deviations. Logical calibration uses discriminators to ensure causal coherence between events. The system supports user-guided path selection and produces interpretable visualizations combining text and images, enhancing transparency and trustworthiness.
Key Results
- On the EID benchmark, WLDS improved factual consistency by 7.08% and logical consistency by 8.34% in urban rail transit scenarios, with an 8.5% increase in scene prediction accuracy over baseline models. In chemical plant scenarios, prediction accuracy was significantly higher, demonstrating robustness across domains.
- Expert evaluations confirmed the generated scenarios' realism and logical coherence. The system's cross-domain knowledge transfer effectively alleviated data scarcity, producing diverse and plausible emergency scenarios.
- Automated metrics based on factual and logical consistency provided quantitative validation, showing the system's reliability and guiding further optimization.
Significance
This work advances emergency scenario simulation by leveraging large models' cross-domain transfer and multi-path reasoning, addressing the limitations of rule-based and data-driven methods. It enhances risk assessment, decision support, and training in complex environments, with broad implications for urban safety, industrial safety, and autonomous systems. The integration of multimodal visualization improves interpretability, fostering trust and adoption in practical settings.
Technical Contribution
The paper introduces a novel multi-path deduction framework combining factual retrieval and causal verification, supported by an automated evaluation system. It constructs a comprehensive EID benchmark dataset covering 10 domains with 4300 samples, facilitating model training and evaluation. The system's user-guided path selection and multimodal outputs represent significant engineering innovations, expanding the application scope of large models in safety-critical scenarios.
Novelty
This is the first work to systematically integrate factual and logical calibration mechanisms within a multi-path scenario deduction system driven by large models. It uniquely combines knowledge transfer, controllable randomness, and multimodal visualization, setting a new standard for high-fidelity emergency scenario simulation in multi-domain contexts.
Limitations
- Dependence on domain-specific knowledge bases may limit adaptability to rapidly evolving scenarios. Knowledge updates are required for maintaining accuracy.
- High computational costs restrict real-time deployment in resource-constrained environments.
- Path deviations in highly complex scenarios still occur, necessitating further refinement of calibration mechanisms.
Future Work
Future efforts will focus on integrating reinforcement learning for autonomous path optimization, expanding knowledge bases with real-time data, and improving computational efficiency. Exploring adaptive calibration strategies and broader multimodal fusion techniques will further enhance system robustness and applicability.
AI Executive Summary
Urban safety and industrial risk management increasingly rely on advanced simulation tools to anticipate and mitigate emergencies. Traditional rule-based models, while effective in normal scenarios, struggle to capture the complexity and unpredictability of real-world crises. Addressing this gap, the proposed WLDS system harnesses the power of large language models to generate diverse, high-fidelity emergency scenarios across multiple domains.
The core innovation lies in combining knowledge transfer, multi-path scenario deduction, and dual calibration mechanisms—factual retrieval and causal verification—to ensure the generated scenarios are both realistic and logically coherent. This approach allows users to explore multiple potential development paths, select preferred scenarios, and visualize them through integrated text-image outputs, greatly enhancing interpretability.
Extensive experiments on the EID benchmark dataset, covering urban rail transit, chemical plants, autonomous driving, and more, demonstrate that WLDS outperforms baseline models with significant improvements in factual and logical consistency. The system's ability to generate plausible emergency scenarios with 8.5% higher scene prediction accuracy validates its potential for real-world risk assessment, decision support, and training.
Despite challenges such as knowledge dependency and computational costs, this work paves the way for smarter, more adaptable emergency simulation systems. Future research will focus on real-time deployment, knowledge base expansion, and reinforcement learning integration, aiming to create fully autonomous, reliable, and scalable risk management solutions that can adapt to rapidly changing environments and complex scenarios.
Deep Dive
Abstract
Traditional simulation methods reproduce occurred emergency instances through presetting to assist people in risk assessment and emergency decision-making. However, due to the lack of randomness and diversity, existing simulation systems struggle to fully explore the potential risk as emergency instances are scarce. In contrast, Large Models (LMs) can dynamically adjust generation strategies to introduce controllable randomness, while also possessing extensive prior knowledge and cross-domain knowledge transfer capabilities. Inspired by it, we propose the LMs-driven World Line Divergence System (WLDS), which enables diversified visualization and deduction of emergency instances in different domains. WLDS leverages LMs to deduce emergency instances in various development directions, and introduces the factual calibration and logical calibration mechanism to ensure factual accuracy and logical rigor during the deduction process. The interactive module can independently select deduction directions to avoid potential hallucinations that are difficult for the system to identify. Furthermore, by introducing the visualization module, WLDS forms simulation and deduction that combine text and images, which enhances interpretability. Extensive experiments conducted on the proposed Emergency Instances Deduction (EID) benchmark dataset demonstrate that WLDS achieves high-precision and high-fidelity simulation and deduction of emergency instances in multiple specific domains. Relevant experiments further demonstrate that WLDS can generate more emergency instances deduction data for users and provide support for better decision-making in similar emergency instances in the future.