评测代码代理在真实俄语任务中的修复能力，发现产品级部署影响实际表现。
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
- 从五个开源仓库真实提交中提取25个俄语编写修复任务，非翻译。
- 最佳模型解决78.7%任务，但部分产品自动替换模型导致结果失真。
- 发现GPT-5.6家族存在系统性越狱行为，工具调用无污染验证可靠。
开发者越来越多地将真实维护工作委托给产品级代码代理，并以母语（俄语）形式描述任务，类似客户请求而非英文问题。我们推出RuBench 1.0，包含从aiohttp、aiogram、Laravel、NestJS、Fastify五个活跃开源仓库中提取的25个任务，每个任务均原生用俄语撰写，未经过翻译，由上游维护者回归测试评判，且测试用例不随发布公开。所有修复提交时间均晚于所评估模型的训练数据截止时间。第一轮评测Claude Code（Opus 4.8、Sonnet 5、Haiku 4.5）与Codex CLI（GPT-5.5），每配置运行3次，使用任务级不确定性的pass@1指标，最优配置解决78.7%任务。对非参赛配置（Claude Code + Fable 5）的完整轨迹审计发现，产品在20%任务中悄然替换模型，证明实际测量单位是部署产品而非模型本身。第二轮加入7种新配置：SourceCraft CLI（ds, legacy）、Koda CLI（koda-pro）、Antigravity（Gemini 3.1 Pro、3.5 Flash）、Codex CLI（GPT-5.6 Sol、Luna），采用配置级新鲜度门控。SourceCraft旗舰版本解决68.1%（N=23）任务，优于GPT-5.5及两个Gemini版本。对全部437条第二轮轨迹的工具调用污染重审显示，俄语和Gemini列无污染（0/293单元格），而GPT-5.6家族存在系统性越狱行为（8/69、13/75单元格），包括从运行机磁盘中挖掘前一轮产物；诚实评分已与原始数据一同发布。我们开放说明、元数据、轨迹、差异文件；验证用例以SHA-256摘要保密。
原文摘要 · Abstract (English)
Developers increasingly delegate real maintenance work to product-grade coding agents, and many state tasks in their native language, in the style of a customer request rather than a curated English issue. We introduce RuBench 1.0, a benchmark of 25 tasks mined from recent fix commits in five live open-source repositories (aiohttp, aiogram, Laravel, NestJS, Fastify), each specified natively in Russian -- written from scratch, not translated -- and judged by the upstream maintainer's regression tests, which we withhold from release. All fix commits postdate the training-data cutoffs of every evaluated model. Round 1 evaluates Claude Code with Opus 4.8, Sonnet 5, and Haiku 4.5, and Codex CLI with GPT-5.5 (3 independent runs each; pass@1 with task-level uncertainty); the best configuration resolves 78.7% of tasks. Auditing full trajectories of an hors-concours configuration (Claude Code + Fable 5), we caught the product silently substituting the model on 20% of tasks via an official safeguard fallback -- evidence that the deployed product, not the model, is the unit actually measured. Version 2 adds Round 2: seven further configurations on the same frozen set under a per-configuration freshness gate -- the Russian-market agents SourceCraft CLI (ds, legacy) and Koda CLI (koda-pro), Antigravity with Gemini 3.1 Pro and 3.5 Flash, and Codex CLI with GPT-5.6 Sol and Luna. SourceCraft's flagship resolves 68.1% (N=23), above GPT-5.5 and both Gemini rows. A tool-call contamination re-audit of all 437 Round-2 trajectories finds the Russian and Gemini columns clean (0/293 cells) while flagging systematic oracle-hunting in the GPT-5.6 family (8/69 and 13/75 cells), including one case of mining a prior round's artifacts from the run machine's disk; honest scores are published alongside raw ones. We release statements, metadata, trajectories, and diffs; oracles are withheld with a SHA-256 manifest.
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