腾讯 WorkBuddy 基准：一种具有抗污染任务构建的多领域编码智能体基准

摘要

我们推出Tencent WorkBuddy Bench——一个面向编码智能体的多领域评估套件；本报告记录了其构建方法、评分协议及跨模型排行榜。其核心是一个统一评估框架，用于在四大工作领域（代码、网页、办公、安全）中构建并运行基于分布信息的编码智能体任务。每个任务并非改编自公开议题文本，而是从真实提交、拉取请求或业务场景中逆向推导，重新编写为简短、口语化、角色扮演式的请求，因此无法通过网络搜索底层议题、拉取请求或提交线程来还原任务提示。由于数据集（包括任务目录、环境镜像、评估框架、测试用例和参考解决方案）完全开放，其抗污染能力依赖于这种构建方法及数据集版本管理，而非保密性。四个子集——仓库级工程、前端开发、办公与业务工作流、红蓝队安全——从不同侧面探索真实工作场景，每个子集采用各自独特的验证方式。所有子集均封装为统一的任务目录格式，并在统一且可复现的协议下，于两个智能体框架（CodeBuddy Code和Claude Code）上运行；完整开源使基准测试可端到端复现并直接审计，任何第三方都能重新运行每个任务并检查其内容。由于每个子集使用不同评分工具，各子集分数不可相互比较，套件不报告整体平均值。我们发布了涵盖多个模型系列的跨模型排行榜。

English

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent tasks across four work domains - Code, Web, Office, and Security. Rather than adapting public issue text, every task is reverse-engineered from a real commit, pull request, or business scenario and rewritten as a short, colloquial, role-played request, so that a task's prompt is not recoverable by web-searching the underlying issue, pull request, or commit thread. Because the dataset is released openly - task directories, environment images, evaluation harness, tests, and reference solutions - contamination resistance rests on this construction together with dataset versioning rather than on secrecy. The four subsets - repository-level engineering, front-end development, office and business workflows, and red-/blue-team security - probe complementary facets of real work, each with its own verification style. All are packaged in a uniform task-directory format and run, under a uniform and reproducible protocol, on two agent harnesses (CodeBuddy Code and Claude Code); the full open release makes the benchmark reproducible end to end and directly auditable, since any third party can re-run each task and inspect its content. Because each subset uses a different scoring instrument, scores are not comparable across subsets and the suite reports no suite-wide average. We report a cross-model leaderboard across several model families.