# SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review

arXiv: https://arxiv.org/abs/2607.06065 · HTML: https://mlsanity.com/paper/2607.06065/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 47 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 28 of 28)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [Training Software Engineering Agents and Verifiers with SWE-Gym](https://mlsanity.com/paper/2412.21139/) (2024)
- [SWE-smith: Scaling Data for Software Engineering Agents](https://mlsanity.com/paper/2504.21798/) (2025)
- [SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?](https://mlsanity.com/paper/2509.16941/) (2025)
- [R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents](https://mlsanity.com/paper/2504.07164/) (2025)
- [Generative Reward Models](https://mlsanity.com/paper/2410.12832/) (2024)
- [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://mlsanity.com/paper/2505.20411/) (2025)
- [Learning From Mistakes Makes LLM Better Reasoner](https://mlsanity.com/paper/2310.20689/) (2023)
- [SWE-bench Goes Live!](https://mlsanity.com/paper/2505.23419/) (2025)
- [Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate](https://mlsanity.com/paper/2501.17703/) (2025)
- [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021)
- [LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2308.11148/) (2023)
- [CodeAgent: Autonomous Communicative Agents for Code Review](https://mlsanity.com/paper/2402.02172/) (2024)
- [SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving](https://mlsanity.com/paper/2601.01426/) (2026)
- [SWE-RM: Execution-free Feedback For Software Engineering Agents](https://mlsanity.com/paper/2512.21919/) (2025)
- [AUGER: Automatically Generating Review Comments with Pre-training Models](https://mlsanity.com/paper/2208.08014/) (2022)
- [BitsAI-CR: Automated Code Review via LLM in Practice](https://mlsanity.com/paper/2501.15134/) (2025)
- [CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells](https://mlsanity.com/paper/2409.19801/) (2024)
- [CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects](https://mlsanity.com/paper/2509.14856/) (2025)
- [AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context](https://mlsanity.com/paper/2601.19494/) (2026)
- [Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study](https://mlsanity.com/paper/2411.07091/) (2024)
- [CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents](https://mlsanity.com/paper/2603.11078/) (2026)

## Built on by (most cited first, 5 of 5)

- [When Policies Change Probabilities: Modular Decision-Making for LLM Code Review](https://mlsanity.com/paper/2608.02677/) (2026)
- [BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests](https://mlsanity.com/paper/2608.02685/) (2026)
- [REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows](https://mlsanity.com/paper/2609.00643/) (2026)
- [Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification](https://mlsanity.com/paper/2609.38812/) (2026)
- [SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories](https://mlsanity.com/paper/2610.13356/) (2026)

## Abstract (arXiv)

Coding agents increasingly generate pull requests (PRs) for real-world software issues, yet one-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision. We introduce SWE-Review, a framework for closing this loop with agentic code review. Given an issue and an AI-generated PR, a reviewer agent explores the repository, decides whether the PR should be accepted, and provides structured feedback for revision. We evaluate this setting with our proposed SWE-Review-Bench to measure both review correctness and downstream revision usefulness. We further curate SWE-Review-Traj dataset to study broader applications of agentic review and fill the data-scarcity gap for open reviewer training. Experiments show that agentic review continuously improves PRs through a generate-review-revise loop, outperforms single-turn fixed-context review in both decision accuracy and resolve rate after revision, transfers beyond review to improve issue-resolution models, and enables effective and efficient test-time scaling. These results position agentic code review as a practical mechanism for moving AI coding agents from one-shot PR generation toward closed-loop issue resolution.