Paper Detail
Tencent WorkBuddy Bench Team, Siqi Cai, Shaopeng Chen, Xiang Fei, Yong Mao, Zihan Xu, Zhiheng Lyu, Zhijian Shao, Yuchen Shi, Shuwen Zhang, Chaofan Qiu, Linjie Che, Xiaoxi Zhao, Feng Wu, Kai Zhang, Chaofan Zhu, Yubin Qi, Xiaoyun Liang, Peijie Dong, Yunhao Zhang, Yuanjie Zhu, Ling Jiang, Xianjun Zhang, Zhehang Chu, Anyuan Sang, Zhen Feng, Sen Nie, Shi Wu, Yuanzhen Xu, Xin Li, Ning Yang, Zhiqiang Dong, Hande Dong, Qiang Lin, Yi Liu, Yunsheng Wu, Ke Li, Xing Sun
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.
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@misc{team2026tencent,
title = {Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction},
author = {Tencent WorkBuddy Bench Team and Siqi Cai and Shaopeng Chen and Xiang Fei and Yong Mao and Zihan Xu and Zhiheng Lyu and Zhijian Shao and Yuchen Shi and Shuwen Zhang and Chaofan Qiu and Linjie Che and Xiaoxi Zhao and Feng Wu and Kai Zhang and Chaofan Zhu and Yubin Qi and Xiaoyun Liang and Peijie Dong and Yunhao Zhang and Yuanjie Zhu and Ling Jiang and Xianjun Zhang and Zhehang Chu and Anyuan Sang and Zhen Feng and Sen Nie and Shi Wu and Yuanzhen Xu and Xin Li and Ning Yang and Zhiqiang Dong and Hande Dong and Qiang Lin and Yi Liu and Yunsheng Wu and Ke Li and Xing Sun},
year = {2026},
abstract = {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 re},
url = {https://huggingface.co/papers/2607.20911},
keywords = {huggingface daily},
eprint = {2607.20911},
archiveprefix = {arXiv},
}
{}