mlsanityData to 14 Oct 2026

Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair

arXiv 2607.24604 ↗First posted 27 Jul 2026cs.CL cs.AIcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Generate--test--revise loops are common in coding agents, but repetition alone provides no reliability guarantee. We study the gap between finding a correct patch and retaining, verifying, and submitting it. A sealed five-seed study over 30 HumanEval repairs produces 900 three-revision trajectories. Under forced revision, current correctness with current traces falls from 0.820 after one revision to 0.673 after two, although ever-correct rises to 0.847. Two common-state studies use 2,430 branches from identical frozen programs to remove post-treatment risk-set bias. In a prespecified 14B replication, stale traces harm 34/135 correct starts versus 4/135 with current traces, a 22.2-point increase (task-cluster 95% CI [8.9,37.0], exact Holm p=0.0337). A prospective 540-rollout policy eliminates observed correct-start harm but reduces wrong-start repair and fails its joint criterion. Repository experiments over 24 bugs and four coder stacks expose floor effects and component heterogeneity without Holm-significant effects. We therefore separate admission, preservation, grounded certification, competence, and liveness. We derive an evidence-bound typed loop contract and instantiate its mechanically enforceable subset in a reference implementation that binds verifier evidence to exact code states, preserves verified checkpoints, and emits auditable admission receipts. The implementation is an executable specification and conformance artifact, not evidence of improved repair competence or calibrated verifier dependence.

Key facts. Cited by 1 paper 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 21 of its 42 references that are in the map. First posted on arXiv 27 Jul 2026.

Builds on

21 of 42 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation7 Jul
  2. 2026Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows30 Jun
  3. 2026EviACT: An Evidence-to-Action Framework for Agentic Program Repair26 May
  4. 2026SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents20 May
  5. 2026Coding Agents Don't Know When to Act8 May
  6. 2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories23 Jun
  7. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 14

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  6. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
  7. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  8. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  9. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  10. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 240
  11. 2024Small Language Models Need Strong Verifiers to Self-Correct Reasoningcited by 86
  12. 2026AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluationcited by 15
2 more
  1. 2025FeedbackEval: A Benchmark for Evaluating Large Language Models in Feedback-Driven Code Repair Taskscited by 10
  2. 2026Benchmarking Code Improvement with Progressive, Adaptive, and Interactive Feedbackcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows
  2. 2026Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code
  3. 2026SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?
  4. 2026Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
  5. 2025Beyond Accuracy: Behavioral Dynamics of Agentic Multi-Hunk Repair
  6. 2026A Jagged Frontier: Evaluating Robustness of Code Agents to Semantics-Preserving Transformations
  7. 2026Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits
  8. 2026To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair