mlsanityData to 7 Oct 2026

The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase

arXiv 2603.25697 ↗First posted 26 Mar 2026cs.SE cs.AIcited by 1

On the map: Synthetic data and self-improvement for LLMs → Self-evolving LLM agents

Abstract (arXiv)

Code production is now a commodity; the bottleneck is knowing what to build and proving it works. We present the Kitchen Loop, a framework for autonomous, self-evolving software built on a unified trust model: (1) a specification surface enumerating what the product claims to support; (2) 'As a User x 1000', where an LLM agent exercises that surface as a synthetic power user at 1,000x human cadence; (3) Unbeatable Tests, ground-truth verification the code author cannot fake; and (4) Drift Control, continuous quality measurement with automated pause gates. We validate across two production systems over 285+ iterations, producing 1,094+ merged pull requests with zero regressions detected by the regression oracle (methodology in Section 6.1). We observe emergent properties at scale: multi-iteration self-correction chains, autonomous infrastructure healing, and monotonically improving quality gates. The primitives are not new; our contribution is their composition into a production-tested system with the operational discipline that makes long-running autonomous evolution safe.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Self-evolving LLM agents, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 12 of its 15 references that are in the map. First posted on arXiv 26 Mar 2026.

Builds on

12 of 15 references.

References · 12

  1. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 93
  2. 2025SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarioscited by 49
  3. 2025The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reasoncited by 47
  4. 2026Agentic Much? Adoption of Coding Agents on GitHubcited by 31
  5. 2025Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Reviewcited by 28
  6. 2025Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflowscited by 27
  7. 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projectscited by 23
  8. 2025Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025cited by 17
  9. 2025Security Degradation in Iterative AI Code Generation -- A Systematic Analysis of the Paradoxcited by 10
  10. 2025A Survey of Bugs in AI-Generated Codecited by 4
  11. 2025Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationalecited by 3
  12. 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvementscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems
  2. 2026Software Self-Extension with SelfEvolve: an Agentic Architecture for Runtime Code Generation
  3. 2026Self-Evolving Coding Agents
  4. 2026The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
  5. 2026One Recipe, Many Harnesses: What Self-Evolution Encodes Across Languages and Models ↗
  6. 2026Skills as Verifiable Artifacts: A Trust Schema and a Biconditional Correctness Criterion for Human-in-the-Loop Agent Runtimes
  7. 2026AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
  8. 2026E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch