论文多源确认

SWE-Prometheus:用 60 个代码仓库评测工程治理能力的新基准

SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories

精选理由

给做代码智能体的朋友:这个新基准不测修 bug,测的是治理仓库,10 个模型里 Kimi-K3 在完整对比里表现最好,评分方法也挺有意思。

SWE-Prometheus 是一个评测编码智能体工程治理能力的新基准,任务从修 bug 扩展到识别仓库风险、排序干预并验证改动,覆盖 6 个治理维度、共 60 个仓库。10 个模型在 22 个仓库的公开子集上评测,平均 Normalized Governance Improvement 介于 0.0568 到 0.5760,行为破坏率在 0% 到 23% 之间。一个与仓库无关的模板在冻结的 10 仓库批次上取得平均 NGI 0.272,但增益集中在 Tests & CI、Quality Gates 和 Documentation,在 Reproducible Environment 与 Dependency & Security 上没有提升。论文还报告了 no-op 条件的中位 NGI 为零,两名教师在 60 个维度评分中 exact agreement 达 57 个,并指出包含行为失败的对比中 Kimi-K3 占优。

原文 · arXiv cs.AI

SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories

Large language model based coding agents have made substantial progress on repository-level software engineering tasks. Existing repository benchmarks, however, usually start from a human-identified issue and evaluate whether a patch satisfies a functional signal. We present SWE-Prometheus, a benchmark for the broader task of improving repository engineering governance. Each task provides a fixed snapshot and an open-ended objective, requiring the agent to identify risks, prioritize interventions, and verify the resulting changes. SWE-Prometheus evaluates six governance dimensions through paired evidence, clean-environment probes, behavior gates, and two independent teacher ratings of the same evidence. The benchmark contains 60 repositories; ten models are evaluated on a shared 22-repository public subset, where mean Normalized Governance Improvement ranges from 0.0568 to 0.5760 and observed behavior-breakage rates range from 0% to 23%. On a frozen ten-repository batch, a repository-blind template obtains mean NGI 0.272, but its gains concentrate in Tests & CI, Quality Gates, and Documentation; it improves Reproducible Environment and Dependency & Security on none of the repositories. This baseline makes the distinction between adding governance artifacts and producing execution-backed improvements measurable. The no-op condition has median NGI zero and standard deviation 0.073; two teachers agree exactly on 57 of 60 dimension scores for the same no-op evidence. For the two highest conditional-mean systems, common-valid NGI is similar, while full-pool comparisons that include behavior failures favor Kimi-K3. These results show why repository-governance evaluation should report improvement, behavior preservation, evidence quality, and coverage together.