Skip to content

Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction

Jul 2026 · arXiv.org · Vol abs/2607.20911 · 4 citations · 25 references
Computer Science

TL;DR

Tencent WorkBuddy Bench is introduced, a multi-domain evaluation suite for coding agents, and its construction methodology, scoring protocol, and a cross-model leaderboard are documents, with a cross-model leaderboard across several model families.

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 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.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

AgentHop: A Diagnostic Benchmark for Agentic Multi-Hop Question Answering over Scientific Citation Graphs

AgentHop, a diagnostic benchmark of 1,011 multiple-choice questions paired with a controlled seven-tool sandbox under fixed token, turn, and tool-call constraints, and the full benchmark set and the harness are released to support diagnostic agent benchmarking.

Chanhee Park, Jeongho Yoon, Sung-Bin Han et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Zero2Repo: Can Coding Agents Build Repositories from Scratch?

Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limited to a single language and depend on manually curated tasks. We introduce Zero2Repo, a benchmark in which an agent receives a product requirements document, an interface...

Pei Yang, Tian-Yu Shi, Yu-Hang Yao et al. · 0 citations
#natural language process... Preprint Sep 2026

MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding

Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute, and utilize external tools iteratively to tackle complex tasks. Beyond achieving functional correctness, these agents must faithfully follow process instructions and c...

Bo-Si Wen, Cun-Xiang Wang, Jia-Yi Gui et al. · 1 citation
Review Sep 2026

WideSWE: Can Coding Agents Coordinate Changes Across Repositories?

Coding-agent evaluation has progressed from resolving individual issues to carrying out long-horizon development, yet task completion is still largely assessed within a single codebase. In software ecosystems, many features and bug fixes require coordinated changes across multiple repositories. We introduce WideSWE to...

Bao-Yi Wang, Xing-Liang Wang, Jin-Yang Wu et al. · 0 citations
Book Open access Aug 2026

Kozuchi Agent: A Language-Agnostic Open-Weight Agent for Software Repair

Kozuchi Agent, a language-agnostic open-weight repair agent and CI-operated evaluation pipeline, is presented, showing that the remaining gap is primarily semantic correctness and selection rather than edit formatting or proprietary-model access.

M. Bahrami, Kosaku Kimura, Satoshi Munakata et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.