Emergency response is a safety-critical public governance task that demands accurate and timely decision-making based on complex event information. This process involves multiple stages, including information collection, integration, analysis, risk assessment, and decision recommendation. Existing research has predominantly concentrated on the earlier stages, while studies focusing on the decision support phase remain underexplored, primarily due to the lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation. To bridge this gap, we introduce the first real-world Emergency Decision-Making dataset EDM-Bench, comprising 1,179 instances spanning diverse task formats, including judgment, choice, short-answer, and structured emergency report generation. We also construct a structured rule repository, EDM-R², which contains 3,406 parsed emergency regulations to enhance decision reliability. Building on these resources, we propose a rule-enhanced reasoning framework, R³V-EDM, which integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. Extensive experiments demonstrate the inherent complexity of emergency decision-making and validate the effectiveness of our approach in enabling more reliable and trustworthy decisions.

Large language models (LLMs) demonstrate remarkable capabilities in understanding complex tasks and have achieved commendable performance in graph-related tasks, such as node classification, link prediction, and subgraph classification. These tasks primarily depend on the local reasoning capabilities of the graph structure. However, research has yet to address the graph partitioning task that requires global perception abilities. Our preliminary findings reveal that vanilla LLMs can only handle graph partitioning on extremely small-scale graphs. To overcome this limitation, we propose a three-phase pipeline to empower LLMs for large-scale graph partitioning: coarsening, reasoning, and refining. The coarsening phase reduces graph complexity. The reasoning phase captures both global and local patterns to generate a coarse partition. The refining phase ensures topological consistency by projecting the coarse-grained partitioning results back to the original graph structure. Extensive experiments demonstrate that our framework enables LLMs to perform graph partitioning across varying graph scales, validating both the effectiveness of LLMs for partitioning tasks and the practical utility of our proposed methodology.

Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. On one hand, powerful large language models (LLMs) currently show poor performance because EE task is more complex than other tasks. On the other hand, state-of-the-art (SOTA) small language models (SLMs) for EE tasks are typically developed through fine-tuning, lack flexibility, and have considerable room for improvement. We propose an approach, **L**LMs-as-**C**orrector for **E**vent **E**xtraction (**LC4EE**), aiming to leverage the superior extraction capability of SLMs and the instruction-following ability of LLMs to construct a robust and highly available EE system. By utilizing LLMs to identify and correct errors of SLMs predictions based on automatically generated feedback information, EE performances can be improved significantly. Experimental results on the representative datasets ACE2005 and MAVEN-Arg for Event Detection (ED) and EE tasks validated the effectiveness of our method.