learnX is a React Native/Expo mobile application designed for Tsinghua University students to access the Web Learning platform (learn.tsinghua.edu.cn). It provides features such as SSO authentication, course management, assignment tracking, file handling, calendar synchronization, and course information sharing via the courseX platform. The app uses Redux for state management, React Navigation for routing, and React Native Paper for UI components, with TypeScript ensuring type safety. It includes background data synchronization and supports iOS, Android, macOS, and Windows (via WSA).
GoAuthing is a command-line authentication tool for Tsinghua University's network (TUNET). It provides login, logout, and keep-alive functionality for both IPv4 and IPv6 connections. The application features a clean separation between the CLI interface (built with urfave/cli) and the core authentication logic (in the libauth package). It handles credential input via prompts or config files, communicates with the authentication server using custom encryption and encoding schemes, and can run as a daemon via systemd or init.d scripts. The project is actively maintained with regular releases and CI/CD pipelines for multi-platform builds.
This repository is a comprehensive archive of course materials from Tsinghua University's Computer Science and Technology department, spanning multiple years and semesters. It contains lecture notes, exam papers, homework solutions, programming assignments, and reference materials for a wide range of undergraduate and graduate courses. The content is organized by academic year and course name, making it easy to navigate. While primarily an educational resource collection rather than a software application or data pipeline, it includes some reusable code snippets and tutorials, such as Python data science notebooks and neural network implementations. The repository is actively maintained with frequent contributions from students, and its vast collection serves as a valuable study aid for current and prospective students.
This repository contains the research code for Video-SALMONN-2, a family of audio-visual large language models for video understanding, developed by Tsinghua University and ByteDance. It includes three generations: Video-SALMONN-2 (based on LLaVA), Video-SALMONN-2+ (based on Qwen2.5-VL), and Video-SALMONN-2 Pro (based on Qwen3-VL). The core contribution is a caption-enhanced training paradigm that tightly aligns audio and visual signals. The codebase provides full training pipelines for supervised fine-tuning (SFT) and direct preference optimization (DPO), along with evaluation scripts, a demo mode, and a vLLM-based serving plugin for deployment. While the project is predominantly research-focused, it includes application-like components such as CLI inference and OpenAI-compatible serving. The code exhibits high duplication across the three model variants, hardcoded paths to internal cluster storage, and brittle dependency pinning, making it challenging to reproduce outside the original environment. However, several components—such as the multimodal batch sampler, audio-video processing utilities, and QFormer-based fusion—are well-structured and potentially reusable for other multimodal research projects.