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Source note2 min read

SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution

Source

arXiv

arxiv.org/abs/2608.18933v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Silin Chen, Han Li, Xiaodong Gu, Yuling Shi, Haibing Guan
  • Venue: arXiv: cs.SE
  • Published: 2026-08-19
  • Collected: 2026-08-20
  • URL: http://arxiv.org/abs/2608.18933v1
  • PDF: https://arxiv.org/pdf/2608.18933v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

SkillForge is a framework that helps large language model-based agents improve their ability to resolve software issues in specific repositories. It does this by proactively acquiring project-specific knowledge from the repository itself through a process called self-distillation, where synthetic issues are created and resolved to distill reusable project-specific knowledge into skills that can be used for future issue resolution.

What the Document Says

  • The paper presents SkillForge, a framework that helps large language model-based agents resolve software issues in specific repositories more effectively.
  • SkillForge proactively acquires project-specific knowledge from the repository by synthesizing and resolving synthetic issues.
  • This process creates reusable project-specific skills associated with relevant repository entities for future issue resolution.
  • Extensive experiments show that SkillForge consistently improves issue resolution performance over strong baselines.
  • The results suggest that proactively acquiring project-specific knowledge before solving real issues substantially improves downstream software issue resolution.

Key Concepts Mentioned

  • large language model
  • software issue resolution
  • project-specific knowledge
  • self-distillation
  • synthetic issues

Claims Made by the Document

  • SkillForge improves issue resolution performance.
  • Proactively acquiring project-specific knowledge before solving real issues improves downstream software issue resolution.

Evidence Used

  • Results from extensive experiments show consistent improvement over strong baselines.

Methods / Approach

  • Self-distillation by synthesizing and resolving synthetic issues

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss potential limitations of the SkillForge framework, such as the scalability or generalizability of the approach.

Open Questions

  • How scalable is the SkillForge approach?
  • Can it be generalized to other types of projects beyond those tested?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on demonstrating the effectiveness of SkillForge in improving issue resolution performance. Questions regarding potential limitations, scalability, and generalizability would be valuable for a more comprehensive understanding.
  • The experiments were conducted using both open-source and closed-source models.

Raw Abstract / Excerpt

Large language model (LLM) based agents have demonstrated remarkable proficiency in automated software issue resolution, yet they often struggle to resolve issues in a specific repository because they lack project-specific knowledge. Existing self-evolving approaches acquire such knowledge from repository history or online repair trajectories, but they either depend on available historical issue-resolution signals or incur substantial per-issue test-time exploration cost. In this paper, we propose SkillForge, a self-distillation framework that proactively acquires project-specific knowledge from the repository itself. Instead of waiting for real issues to expose project-specific knowledge gaps, SkillForge synthesizes project-specific issues by re-implementing test-covered core functionalities of the repository. By resolving these synthetic issues, SkillForge distills reusable project-specific knowledge into entity-grounded skills and associates them with relevant repository entities for future issue resolution. Extensive experiments using both open-source and closed-source models show that SkillForge consistently improves issue resolution performance over strong baselines. These results demonstrate that proactively acquiring project-specific knowledge before solving real issues substantially improves downstream software issue resolution.