Large Language Models (LLMs) have achieved remarkable performance across a wide range of mathematical benchmarks. However, concerns remain as to whether these successes reflect genuine reasoning or superficial pattern recognition. Existing evaluation methods, which typically focus either on the final answer or on the intermediate reasoning steps, reduce mathematical reasoning to a shallow input–output mapping, overlooking its inherently multi-stage and multi-dimensional cognitive nature. Inspired by P’olya’s problem-solving theory, we propose SMART, a benchmark that decomposes mathematical problem-solving into four cognitive dimensions: **S**emantic Understanding, **M**athematical Reasoning, **A**rithmetic Computation, and **R**eflection Refinemen**T**, and introduces dimension-specific tasks to measure the corresponding cognitive processes of LLMs. We apply SMART to 22 state-of-the-art open- and closed-source LLMs and uncover substantial discrepancies in their capabilities across dimensions. Our findings reveal genuine weaknesses in current models and motivate a new metric, the All-Pass Score, designed to better capture true problem-solving capability.

This survey aims to sort out the recent advances in video question answering (VideoQA) and point towards future directions. We firstly categorize the datasets into 1) normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA, according to the modalities invoked in the question-answer pairs, or 2) factoid VideoQA and inference VideoQA, according to the technical challenges in comprehending the questions and deriving the correct answers. We then summarize the VideoQA techniques, including those mainly designed for Factoid QA (e.g., the early spatio-temporal attention-based methods and the recently Transformer-based ones) and those targeted at explicit relation and logic inference (e.g., neural modular networks, neural symbolic methods, and graph-structured methods). Aside from the backbone techniques, we delve into the specific models and find out some common and useful insights either for video modeling, question answering, or for cross-modal correspondence learning. Finally, we point out the research trend of studying beyond factoid VideoQA to inference VideoQA, as well as towards the robustness and interpretability. Additionally, we maintain a repository, https://github.com/VRU-NExT/VideoQA, to keep trace of the latest VideoQA papers, datasets, and their open-source implementations if available. With these efforts, we strongly hope this survey could shed light on the follow-up VideoQA research.