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

Ishraq Khan, Assad Chowdary, Sharoz Haseeb, Urvish Patel, Yousuf Zaii
Kodezi Inc.
{Ishraq,Assad,Sharoz,Urvish,Yousuf}@kodezi.com

Abstract

Debugging remains unsolved for LLMs despite advances in code generation. While Claude 4.5 Sonnet and Claude 4.1 Opus achieve > 70% on synthesis benchmarks, they fail on real debugging with < 15% success rates (95% CI: 12.1-17.9%). We present Kodezi Chronos, the first debugging-specific language model combining: (1) Adaptive Graph-Guided Retrieval (AGR) navigating codebases up to 10M LOC via multi-hop traversal (92% precision, 85% recall), (2) Persistent Debug Memory (PDM) learning from 15M+ sessions, and (3) 7-layer architecture for iterative fix-test-refine loops.

On 5,000 real-world scenarios, Chronos achieves 67.3% ± 2.1% fix accuracy versus 14.2% ± 1.3% (Claude 4.1 Opus) and 13.8% ± 1.2% (GPT-4.1), with Cohen's d=3.87 effect size. On the SWE-bench Lite benchmark, Chronos achieves state-of-the-art performance with 80.33% resolution rate (241/300 instances), establishing a 20 percentage point lead over the next best system (ExpeRepair-v1.0 + Claude 4.5 Sonnet: 60.33%). Repository-specific performance reaches 96.1% (sympy), 93.8% (sphinx), and 90.4% (django). The system reduces debugging time by 40% and iterations by 65%. Chronos resolves complex multi-file bugs requiring cross-repository understanding and temporal analysis.

Key limitations include 23.4% success on hardware-dependent bugs and 41.2% on dynamic language issues. Theoretical analysis proves O(k log d) retrieval complexity with convergence guarantees. Human evaluation (N=50) shows 89% preference over baselines. Available Q4 2025 (Kodezi OS) and Q1 2026 (API).

Key Updates from 2025 Research

Performance Improvements

  • Debug Success Rate: 67.3% ± 2.1% (up from 65.3%)
  • Human Preference: 89% in evaluation studies
  • Time Reduction: 40% faster debugging
  • Iteration Count: 7.8 average iterations for thorough fixes

New Comparisons

SWE-bench Lite (State-of-the-Art Results)

  • Chronos: 80.33% (241/300 instances) - #1 Globally
  • ExpeRepair-v1.0 + Claude 4.5 Sonnet: 60.33% - #2
  • Claude 4.5 Sonnet (Bash Only): ~14%
  • Claude 4.1 Opus (Bash Only): 14.2%
  • GPT-4.1: 13.8%
  • Gemini 2.0 Pro: 13.4%

Key Insight: 20 percentage point absolute lead over second place

Repository-Specific Performance (SWE-bench Lite)

  • sympy (symbolic mathematics): 96.1%
  • sphinx (documentation): 93.8%
  • django (web frameworks): 90.4%

Code Generation vs Debugging Gap

  • Claude 4.5 Sonnet: 72.7% (SWE-bench Full) → ~14% (SWE-bench Lite) = -58.7pp gap
  • Claude 4.1 Opus: 72.5% (SWE-bench Full) → 14.2% (SWE-bench Lite) = -58.3pp gap
  • GPT-4.1: 54.6% (SWE-bench Full) → 13.8% (SWE-bench Lite) = -40.8pp gap

Other Models Evaluated

  • Claude 4.1 Opus: 14.2% ± 1.3% success rate (MRR)
  • GPT-4.1: 13.8% ± 1.2% success rate (MRR)
  • DeepSeek V3: Mentioned for efficient training ($5.6M cost)
  • Gemini 2.0 Pro: <15% success rate on debugging

Architectural Enhancements

  • Enhanced AGR with O(k log d) retrieval complexity
  • Improved PDM with 15M+ debugging sessions
  • 7-layer architecture optimized for debugging workflows
  • Cohen's d=3.87 effect size demonstrating large improvement

Limitations Identified

  • 23.4% success on hardware-dependent bugs
  • 41.2% success on dynamic language issues
  • Still requires significant computational resources