Steven Huang

Tianhao Peng, Haochen Wang, Yuanxing Zhang et al.

The advent of Multimodal Large Language Models (MLLMs) has expanded AI capabilities to visual modalities, yet existing evaluation benchmarks remain limited to single-video understanding, overlooking the critical need for multi-video understanding in real-world scenarios (e.g., sports analytics and autonomous driving). To address this significant gap, we introduce MVU-Eval, the first comprehensive benchmark for evaluating Multi-Video Understanding for MLLMs. Specifically, our MVU-Eval mainly assesses eight core competencies through 1,824 meticulously curated question-answer pairs spanning 4,959 videos from diverse domains, addressing both fundamental perception tasks and high-order reasoning tasks. These capabilities are rigorously aligned with real-world applications such as multi-sensor synthesis in autonomous systems and cross-angle sports analytics. Through extensive evaluation of state-of-the-art open-source and closed-source models, we reveal significant performance discrepancies and limitations in current MLLMs' ability to perform understanding across multiple videos. The benchmark will be made publicly available to foster future research.

Tyler Wendland, Jingyang Sun, Junayed Mahmud et al.

Software maintenance constitutes a large portion of the software development lifecycle. To carry out maintenance tasks, developers often need to understand and reproduce bug reports. As such, there has been increasing research activity coalescing around the notion of automating various activities related to bug reporting. A sizable portion of this research interest has focused on the domain of mobile apps. However, as research around mobile app bug reporting progresses, there is a clear need for a manually vetted and reproducible set of real-world bug reports that can serve as a benchmark for future work. This paper presents ANDROR2: a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, ANDROR2 includes the original bug report, an apk file for the buggy version of the app, an executable reproduction script, and metadata regarding the quality of the reproduction steps associated with the original report. We believe that the ANDROR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly.