InfCode: Adversarial Iterative Refinement of Tests and Patches for Reliable Software Issue Resolution
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Abstract (arXiv)
Large language models have advanced software engineering automation, yet resolving real-world software issues remains difficult because it requires repository-level reasoning, accurate diagnostics, and strong verification signals. Existing agent-based and pipeline-based methods often rely on insufficient tests, which can lead to patches that satisfy verification but fail to fix the underlying defect. We present InfCode, an adversarial multi-agent framework for automated repository-level issue resolution. InfCode iteratively refines both tests and patches through adversarial interaction between a Test Patch Generator and a Code Patch Generator, while a Selector agent identifies the most reliable fix. The framework runs inside a containerized environment that supports realistic repository inspection, modification, and validation. Experiments on SWE-bench Lite and SWE-bench Verified using models such as DeepSeek-V3 and Claude 4.5 Sonnet show that InfCode consistently outperforms strong baselines. It achieves 79.4% performance on SWE-bench Verified, establishing a new state-of-the-art. We have released InfCode as an open-source project at https://github.com/Tokfinity/InfCode.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 42 references that are in the map. First posted on arXiv 20 Nov 2025.
Builds on
34 of 42 references.
References · 34
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- 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 191
22 more
- 2024RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learningcited by 146
- 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agentscited by 129
- 2024SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinementcited by 124
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- 2024TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?cited by 32
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Citing papers per year: 2025: 1; 2026: 6.
7 papers.
- 2025Agentic Software Issue Resolution with Large Language Models: A Surveycited by 6
- 2026Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repaircited by 6
- 2026PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agentscited by 2
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixescited by 2
- 2026Resolving Java Code Repository Issues with iSWE Agentcited by 1
- 2026Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discoverycited by 0
- 2026ExecCritic: Learn to Test, Test to Improve for Coding Agentscited by 0
- 2026ExecCritic: Learn to Test, Test to Improve for Coding Agents8 Sep 2026
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes22 Jul 2026
- 2026PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents21 Jul 2026
- 2026Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery21 Apr 2026
- 2026Resolving Java Code Repository Issues with iSWE Agent11 Mar 2026
- 2026Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair27 Jan 2026
- 2025Agentic Software Issue Resolution with Large Language Models: A Survey24 Dec 2025