What Will Happen Next: Large Models-Driven Deduction for Emergency Instances

Researchers propose WLDS, a Large Language Model-driven system for simulating and deducing emergency scenarios across multiple domains. The system addresses limitations of traditional simulation methods by using LMs to generate diverse, realistic emergency instance variations with calibration mechanisms to ensure factual accuracy and logical consistency.

This research addresses a genuine gap in emergency preparedness: traditional simulation methods rely on predetermined scenarios that lack the diversity needed to explore comprehensive risk landscapes. By leveraging Large Language Models' generative capabilities and cross-domain knowledge, WLDS introduces a fundamentally different approach to emergency scenario planning. The system's key innovation lies in its calibration mechanisms—factual and logical checks designed to prevent the hallucination problems that plague LM outputs in high-stakes applications.

The Emergency Instances Deduction benchmark represents methodical academic progress in applying LMs beyond text generation. Emergency management has historically depended on scenario planners and domain experts manually constructing training simulations. This work demonstrates that LMs can dynamically generate diverse variations of emergency trajectories while maintaining coherence, potentially accelerating preparedness planning across sectors like public health, infrastructure, and disaster response.

The practical impact extends to decision-support systems. Organizations currently relying on static scenario libraries could access broader risk exploration through WLDS, enabling planners to stress-test responses against scenarios their teams hadn't explicitly considered. The interactive module allowing human guidance reduces blind spots from model-generated hallucinations, creating a human-AI collaboration framework rather than full automation.

The research signals growing momentum in applying LMs to structured domain problems beyond content creation. As emergency management agencies evaluate such systems, adoption will likely depend on validation against real-world outcomes and regulatory acceptance. This positions LM-driven simulation as an emerging tool category that complements rather than replaces traditional planning expertise.

• →WLDS uses LMs to generate diverse emergency scenario deductions with factual and logical calibration mechanisms to minimize hallucinations.
• →The system enables interactive human-guided scenario exploration across multiple domains, supporting better decision-making in crisis situations.
• →LM-driven simulation represents a methodological shift from static, predetermined scenarios to dynamically generated, diversified emergency variations.
• →Interactive and visualization modules enhance interpretability and allow humans to steer deduction directions away from implausible outcomes.
• →The Emergency Instances Deduction benchmark dataset demonstrates measurable improvements in simulation fidelity across multiple specific domains.