Kodezi Chronos: A Debugging-First Language Model for Repository-Scale Code Understanding
- URL: http://arxiv.org/abs/2507.12482v2
- Date: Tue, 29 Jul 2025 05:09:30 GMT
- Title: Kodezi Chronos: A Debugging-First Language Model for Repository-Scale Code Understanding
- Abstract summary: Large Language Models (LLMs) have improved code generation and software automation, but remain limited by inference-time context.<n>We introduce Kodezi Chronos, a language model built specifically for debug.<n> Chronos combines Adaptive Graph-Guided Retrieval to navigates up to 10 million lines using multi-hop synthesis (92% precision, 85% recall), Persistent Debug Memory trained on 15M+ sessions, and a 7-layer architecture for iterative fix-test-refine loops.<n>On 5,000 real-world scenarios, Chronos achieves 67.3% fix accuracy, compared to 14.2% and 13.8% for Claude and GPT-
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) have improved code generation and software automation, but remain limited by inference-time context and lack structured reasoning over code. Debugging remains unsolved despite these advances. While Claude Opus 4 and GPT-4.1 achieve >70% on code synthesis benchmarks, they perform <15% on real debugging tasks. We introduce Kodezi Chronos, a language model built specifically for debugging. Chronos combines Adaptive Graph-Guided Retrieval to navigate codebases up to 10 million lines using multi-hop traversal (92% precision, 85% recall), Persistent Debug Memory trained on 15M+ sessions, and a 7-layer architecture for iterative fix-test-refine loops. On 5,000 real-world scenarios, Chronos achieves 67.3% fix accuracy, compared to 14.2% and 13.8% for Claude and GPT-4.1 respectively. Chronos reduces debugging time by 40% and iteration count by 65%. It resolves complex multi-file bugs involving cross-repository context and temporal reasoning. Key limitations include 23.4% success on hardware-dependent issues and 41.2% on dynamic language errors. Theoretical analysis shows O(k log d) retrieval complexity with convergence guarantees. In a human evaluation (N=50), 89% of participants preferred Chronos over baseline models. Chronos will be available in Kodezi OS in Q4 2025 and via API in Q1 2026.
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