#452 · Primary category: AI Coding Assistants

Chronos

artificial-intelligence autonomous-debugging benchmark benchmark-report bug-fixing chronos code code-analysis code-analysis-tool code-debugger code-understanding debugging developer-tools kodezi language-model machine-learning program-repair software-engineering

Open-source debugging-first language model with 80.33% SWE-bench Lite and 67% real-world fix accuracy, 6x better than GPT-4.

Project last updated:11/12/25

GitHub Stars

4.9K

Forks

211

Contributors

1

License

Other

Why we included this project

Teams wrestling with slow, manual debugging will find this project worth close study, because it tackles a blind spot most coding models leave untouched: general-purpose assistants that write code well often fail badly at locating and repairing faults in existing repositories. Chronos documents a debugging-first model trained specifically on root cause analysis and multi-file patch generation, with published results showing a large success-rate margin over mainstream LLMs on SWE-bench Lite and on logic, concurrency, and memory bugs. The repository itself is a research and evaluation package rather than runnable software; the model is proprietary and access is gated behind a waitlist, with release planned through Kodezi OS in Q1 2026. Treat it as a detailed benchmark report and architecture reference for what a purpose-built debugger can achieve, and an early signal of what specialized tooling in this niche may soon offer.

Articles for this project

No articles for this project yet.

To suggest a topic or contribute an article, contact us.

Related projects in this category