Knowledge Centric Self Improvement

---
name: knowledge-centric-self-improvement
description: 'Skill generated from arXiv paper 2607.19592: Knowledge-Centric Self-Improvement'
metadata:
  {
    "arxiv": {
      "id": "2607.19592",
      "title": "Knowledge-Centric Self-Improvement",
      "authors": ['Xuefei Julie Wang', 'Lauren Hyoseo Yoon', 'Chengrui Qu', 'Amanda Zichang Wang', 'Atharva Sehgal', 'Eric Mazumdar', 'Yisong Yue'],
      "published": "2026-07-21",
      "categories": ['cs.AI', 'cs.CL', 'cs.LG', 'cs.MA'],
      "url": "https://arxiv.org/abs/2607.19592",
      "utility": 1.0
    }
  }
---
# Knowledge-Centric Self-Improvement

**arXiv:** 2607.19592  
**Published:** 2026-07-21  
**Authors:** Xuefei Julie Wang, Lauren Hyoseo Yoon, Chengrui Qu, Amanda Zichang Wang, Atharva Sehgal, Eric Mazumdar, Yisong Yue  
**Categories:** cs.AI, cs.CL, cs.LG, cs.MA  
**Utility:** 1.00

## Key Innovation
Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code. This agent-centric view can make improvements expensive to maintain and difficult to transfer, because gains become tied to a particular agent design, task distribution, or adaptation run. We study a complementary paradigm: knowledge-centric self-improvement, in which agents remain generic and disposable while the persistent object is a curat...

## Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.AI, cs.CL, cs.LG, cs.MA.

## References
- arXiv: https://arxiv.org/abs/2607.19592