SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 44 of its 62 references that are in the map. First posted on arXiv 24 Sep 2026.
Builds on
44 of 62 references.
Closest ancestors · 12
Same direction, newest first
- 2026SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?20 Aug
- 2026SWE-Explore: Benchmarking How Coding Agents Explore Repositories5 Jun
- 2026SmellBench: Evaluating LLM Agents on Architectural Code Smell Repair7 May
- 2026HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks16 Apr
- 2026Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents26 Feb
- 2025SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents26 May
- 2025SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents11 Apr
- 2024Training Software Engineering Agents and Verifiers with SWE-Gym30 Dec
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents18 Jun
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repair25 Mar
Other references · 32
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2021Program Synthesis with Large Language Modelscited by 3,509
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
- 2023Teaching Large Language Models to Self-Debugcited by 963
20 more
- 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 357
- 2023RepoBench: Benchmarking Repository-Level Code Auto-Completion Systemscited by 346
- 2024LiveBench: A Challenging, Contamination-Limited LLM Benchmarkcited by 242
- 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 220
- 2024MANTIS: Interleaved Multi-Image Instruction Tuningcited by 212
- 2024SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?cited by 196
- 2024MuirBench: A Comprehensive Benchmark for Robust Multi-image Understandingcited by 178
- 2025Multi-SWE-bench: A Multilingual Benchmark for Issue Resolvingcited by 165
- 2025SWE-bench Goes Live!cited by 74
- 2024RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generationcited by 69
- 2024Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoningcited by 50
- 2024SWE-bench-java: A GitHub Issue Resolving Benchmark for Javacited by 34
- 2025Automated Benchmark Generation for Repository-Level Coding Taskscited by 27
- 2025OmniGIRL: A Multilingual and Multimodal Benchmark for GitHub Issue Resolutioncited by 24
- 2024On the Impacts of Contexts on Repository-Level Code Generationcited by 21
- 2024CodeV: Issue Resolving with Visual Datacited by 9
- 2025What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilitiescited by 8
- 2022Not All Dependencies are Equal: An Empirical Study on Production Dependencies in NPMcited by 3
- 2026OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Modelcited by 3
- 2026MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localizationcited by 2
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents
- 2026E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch
- 2026Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration
- 2026Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
- 2026SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?
- 2026Correct Code, Broken Contributions? SWE-CC: Benchmarking Repository Policy Compliance for Coding Agents
- 2026A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
- 2026SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution