# Lingxi: Repository-Level Issue Resolution Framework Enhanced by Procedural Knowledge Guided Scaling

arXiv: https://arxiv.org/abs/2510.11838 · HTML: https://mlsanity.com/paper/2510.11838/

## Key facts

- Cited by 12 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.
- First posted on arXiv 13 Oct 2025.

## Placed in

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

## Built on by (most cited first, 12 of 12)

- [ContextBench: A Benchmark for Context Retrieval in Coding Agents](https://mlsanity.com/paper/2602.05892/) (2026)
- [Agentic Software Issue Resolution with Large Language Models: A Survey](https://mlsanity.com/paper/2512.22256/) (2025)
- [OpenSage: Self-programming Agent Generation Engine](https://mlsanity.com/paper/2602.16891/) (2026)
- [From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair](https://mlsanity.com/paper/2601.23257/) (2026)
- [Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment](https://mlsanity.com/paper/2606.14948/) (2026)
- [Reusing Past Repairs Through Hierarchical Trajectory Abstraction for Coding Agents](https://mlsanity.com/paper/2607.29658/) (2026)
- [One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents](https://mlsanity.com/paper/2512.20957/) (2025)
- [ExplainBench: Evaluating Code Explanations from Agents](https://mlsanity.com/paper/2607.26451/) (2026)
- [SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch](https://mlsanity.com/paper/2607.27167/) (2026)
- [openJiuwen: Beyond Static Harnesses for Long-Horizon Coding Agents](https://mlsanity.com/paper/2608.27969/) (2026)
- [RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair](https://mlsanity.com/paper/2610.04658/) (2026)
- [Chronos Enables Code Agents to Reason over Software Evolution](https://mlsanity.com/paper/2610.11578/) (2026)

## Abstract (arXiv)

Driven by the advancements of Large Language Models (LLMs), LLM-powered agents are making significant improvements in software engineering tasks, yet struggle with complex, repository-level issue resolution. Existing agent-based methods have two key limitations. First, they lack of procedural knowledge (i.e., how an issue is fixed step-by-step and rationales behind it) to learn and leverage for issue resolution. Second, they rely on massive computational power to blindly explore the solution space. % To address those limitations, we propose Lingxi, an issue resolution framework that leverages procedural knowledge extracted from historical issue-fixing data to guide agents in solving repository-level issues. ourTool first constructs this knowledge offline through a hierarchical abstraction mechanism, enabling agents to learn the how and why behind a fix, not just the final solution. During online application, it employs a knowledge-driven scaling method that leverages the procedural knowledge of similar issues to intelligently analyze the target issue from multiple perspectives, in sharp contrast to undirected, brute-force exploration. % Lingxi successfully resolves 74.6% of bugs on the SWE-bench Verified benchmark in Past@1 setting, outperforming five state-of-the-art techniques by a significant margin (5.4% to 14.9%). Our comprehensive ablation study confirmed that the success of Lingxi comes directly from its use of procedural knowledge. Without it, the performance gains from scaling alone is negligible. Our qualitative study further shows that the ``design patterns & coding practices'' is the most critical knowledge aspect, and that the roles of different knowledge aspects switch across different stages (i.e., analysis, planning, and fixing).