The task of issue resolving aims to modify a codebase to generate a patch that addresses a given issue. However, most existing benchmarks focus almost exclusively on Python, making them insufficient for evaluating Large Language Models (LLMs) across different programming languages. To bridge this gap, we introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench, covering 8 languages of Python, Java, TypeScript, JavaScript, Go, Rust, C, and C++. In particular, this benchmark includes a total of 2,132 high-quality instances, carefully curated by 68 expert annotators, ensuring a reliable and accurate evaluation of LLMs on the issue-resolving task. Based on human-annotated results, the issues are further classified into three difficulty levels. We evaluate a series of state-of-the-art models on Multi-SWE-bench, utilizing both procedural and agent-based frameworks for issue resolving. Our experiments reveal three key findings: (1) Limited generalization across languages: While existing LLMs perform well on Python issues, their ability to generalize across other languages remains limited; (2) Performance aligned with human-annotated difficulty: LLM-based agents' performance closely aligns with human-assigned difficulty, with resolution rates decreasing as issue complexity rises; and (3) Performance drop on cross-file issues: The performance of current methods significantly deteriorates when handling cross-file issues. These findings highlight the limitations of current LLMs and underscore the need for more robust models capable of handling a broader range of programming languages and complex issue scenarios.

Spike cameras, a novel neuromorphic visual sensor, can capture full-time spatial information through spike stream, offering ultra-high temporal resolution and an extensive dy-namic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless, due to disparities in data modality and information characteristics compared to frame stream and event stream, the current lack of effi-cient AC methods has made it challenging for spike cam-eras to adapt to intricate real-world conditions. To ad-dress this challenge, we introduce a spike-based autofo-cus framework that includes a spike-specific focus measure called spike dispersion (SD), which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally, the framework integrates a fast search strategy called spike-based goldenfast search (SGFS), allowing rapidfocal positioning without the need for a complete focus range traver-sal. To validate the performance of our method, we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore, experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.