Large Language Models (LLMs) have achieved impressive results across a broad array of tasks, yet their capacity for complex, domain-specific mathematical reasoning—particularly in wireless communications—remains underexplored. In this work, we introduce WirelessMathBench, a novel benchmark specifically designed to evaluate LLMs on mathematical modeling challenges to wireless communications engineering. Our benchmark consists of 587 meticulously curated questions sourced from 40 state-of-the-art research papers, encompassing a diverse spectrum of tasks ranging from basic multiple-choice questions to complex equation completion tasks, including both partial and full completions, all of which rigorously adhere to physical and dimensional constraints. Through extensive experimentation with leading LLMs, we observe that while many models excel in basic recall tasks, their performance degrades significantly when reconstructing partially or fully obscured equations, exposing fundamental limitations in current LLMs. Even DeepSeek-R1, the best performer on our benchmark, achieves an average accuracy of only 38.05%, with a mere 7.83% success rate in full equation completion. By publicly releasing WirelessMathBench along with the evaluation toolkit, we aim to advance the development of more robust, domain-aware LLMs for wireless system analysis and broader engineering applications.

“Event detection (ED) is a crucial area of natural language processing that automates the extrac-tion of specific event types from large-scale text, and studying historical ED in classical Chinesetexts helps preserve and inherit historical and cultural heritage by extracting valuable informa-tion. However, classical Chinese language characteristics, such as ambiguous word classes andcomplex semantics, have posed challenges and led to a lack of datasets and limited research onevent schema construction. In addition, large-scale datasets in English and modern Chinese arenot directly applicable to historical ED in classical Chinese. To address these issues, we con-structed a logical event schema for classical Chinese historical texts and annotated the resultingdataset, which is called classical Chinese Historical Event Dataset (CHED). The main challengesin our work on classical Chinese historical ED are accurately identifying and classifying eventswithin cultural and linguistic contexts and addressing ambiguity resulting from multiple mean-ings of words in historical texts. Therefore, we have developed a set of annotation guidelinesand provided annotators with an objective reference translation. The average Kappa coefficientafter multiple cross-validation is 68.49%, indicating high quality and consistency. We conductedvarious tasks and comparative experiments on established baseline models for historical ED inclassical Chinese. The results showed that BERT+CRF had the best performance on sequencelabeling task, with an f1-score of 76.10%, indicating potential for further improvement. 1Introduction”