Large Language Models (LLMs) have advanced code generation and software automation, but are fundamentally constrained by limited inference-time context and lack of explicit code structure reasoning. We introduce Kodezi Chronos, a next-generation architecture for autonomous code understanding, debugging, and maintenance, designed to operate across ultra-long contexts comprising entire codebases, histories, and documentation—all without fixed window limits.

Kodezi Chronos leverages a multi-level embedding memory engine, combining vector and graph-based indexing with continuous code-aware retrieval. This enables efficient and accurate reasoning over millions of lines of code, supporting repository-scale comprehension, multi-file refactoring, and real-time self-healing actions.

Our evaluation introduces a novel Multi Random Retrieval benchmark, specifically tailored to the software engineering domain. Unlike classical retrieval benchmarks, this method requires the model to resolve arbitrarily distant and obfuscated associations across code artifacts, simulating realistic tasks such as variable tracing, dependency migration, and semantic bug localization. Chronos outperforms prior LLMs and code models—demonstrating a 23% improvement in real-world bug detection and reducing debugging cycles by up to 40% compared to traditional sequence-based approaches.

By natively interfacing with IDEs and CI/CD workflows, Chronos enables seamless, autonomous software maintenance, elevating code reliability and productivity while reducing manual effort. These results mark a critical advance toward self-sustaining, continuously optimized software ecosystems.

• Full Paper - Complete research paper with all sections
• Abstract - Paper abstract and key innovations
• Methodology - Detailed evaluation methodology
• Related Work - Comparison with existing approaches
• Future Work - Planned improvements and research directions

All figures from the paper are available in high resolution in the figures/ directory:

1. Figure 1: High-level overview of Chronos architecture
2. Figure 2: Token distribution in debugging tasks
3. Figure 3: Graph-structured memory indexing
4. Figure 4: Traditional LLM planning vs AGR-enhanced debugging
5. Figure 5: Iterative context expansion in AGR
6. Figure 6: Multi-modal retrieval mechanism
7. Figure 7: Chronos debugging feedback loop
8. Figure 8: Autonomous debugging loop diagram
9. Figure 9: Average code-to-fix cycles comparison
10. Figure 10: Ablation analysis results

All performance data tables are available in CSV format for further analysis:

1. Table I: Input vs output characteristics in debugging
2. Table II: Multi-code association retrieval example
3. Table III: MRR benchmark performance
4. Table IV: AGR performance metrics
5. Table V: Overall performance comparison
6. Table VI: Agentic tools comparison
7. Table VII: Long-context debugging performance
8. Table VIII: Bug category success rates
9. Table IX: Repository scale performance
10. Table X: Multi-code association metrics
11. Table XI: Computational efficiency analysis
12. Table XII: Qualitative debugging examples
13. Table XIII: Failure mode analysis

While the Chronos model itself is proprietary, we provide:

1. Evaluation Framework: Complete implementation of our benchmarks
2. MRR Benchmark: Multi-Random Retrieval test suite
3. Baseline Results: Performance data for GPT-4, Claude-3, Gemini-1.5
4. Statistical Analysis: Scripts for significance testing

See ../evaluation/ for the complete evaluation framework.

• First LLM specifically designed for debugging, not code completion
• Trained on 42.5M real debugging examples
• Specialized 7-layer architecture

• Dynamic k-hop expansion (k=1-5)
• 89.2% precision vs 42.3% for flat retrieval
• Handles repository-scale codebases

• Cross-session learning
• Repository-specific pattern recognition
• 7.3x token efficiency improvement

• Recognizes debugging as output-heavy task
• ~3K output tokens vs ~3.6K input tokens
• 47.2% output entropy density

• Tests real-world debugging scenarios
• Context scattered across 10-50 files
• 3-12 months of temporal dispersion

Syntax Errors:      94.2% (1.1x improvement)
Logic Bugs:         72.8% (6.0x improvement)
Concurrency:        58.3% (18.2x improvement)
Memory Issues:      61.7% (10.8x improvement)
API Misuse:         79.1% (4.2x improvement)
Performance:        65.4% (8.8x improvement)

<10K LOC:      71.2% (3.3x vs baseline)
10K-100K:      68.9% (4.7x vs baseline)
100K-1M:       64.3% (7.2x vs baseline)
>1M LOC:       59.7% (15.7x vs baseline)

@article{khan2025chronos,
  title={Kodezi Chronos: A Debugging-First Language Model for 
         Repository-Scale, Memory-Driven Code Understanding},
  author={Khan, Ishraq and Chowdary, Assad and 
          Haseeb, Sharoz and Patel, Urvish},
  journal={arXiv preprint arXiv:2507.12482},
  year={2025},
  url={https://arxiv.org/abs/2507.12482}
}

• Website: kodezi.com/chronos
• Waitlist: kodezi.com/os
• Blog: kodezi.com/blog
• Twitter: @KodeziHQ