Kodezi Chronos: A Debugging-First Language Model for Repository-Scale Code Understanding

Large Language Models (LLMs) have advanced code generation and software automation but remain constrained by inference-time context and lack structured reasoning over code, leaving debugging largely unsolved. While Claude 4.5 Sonnet and Claude 4.1 Opus exceed 70% on code synthesis benchmarks, they achieve under 15% on real debugging tasks. We introduce Kodezi Chronos, a language model purpose-built for debugging that integrates Adaptive Graph-Guided Retrieval to traverse codebases up to 10 million lines, Persistent Debug Memory trained on over 15 million sessions, and a seven-layer fix-test-refine architecture. On 5,000 real-world scenarios, Chronos achieves 67.3% fix accuracy compared to 14.2% and 13.8% for Claude 4.1 Opus and GPT-4.1, respectively. On SWE-bench Lite, Chronos reaches a state-of-the-art 80.33% resolution rate (241 of 300), outperforming the next best system by 20 points and achieving repository-specific highs of 96.1% on Sympy and 90.4% on Django. Chronos reduces debugging time by 40% and iterations by 65%, resolving complex multi-file and cross-repository bugs. It remains limited on hardware-dependent and dynamic language errors. Chronos will be available in Kodezi OS in Q4 2025 and via API in Q1 2026.