The authors propose a system (WLDS) that uses large language models to simulate emergency scenarios and deduce how they might unfold differently. Traditional emergency simulations just replay what happened; this system generates diverse alternative timelines by having the LM branch scenarios in multiple directions, using factual and logical calibration to keep outputs plausible, plus a visualization module for text-image outputs. They test on a new Emergency Instances Deduction benchmark and claim high-precision branching scenario generation across domains.
Main takeaways:
- System generates multiple divergent timelines for emergency events instead of replaying a single recorded instance
- Factual and logical calibration mechanisms attempt to keep generated scenarios accurate and coherent
- Interactive module lets users select deduction directions to avoid hard-to-detect hallucinations
- Combines text and image outputs for interpretability
- Tested on a new Emergency Instances Deduction dataset covering multiple specific domains