🚀 Kodezi Chronos

The World's First Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding

🎯 65.3% Autonomous Debugging Success • 🔍 78.4% Root Cause Accuracy • ⚡ 2.2 Average Fix Cycles • 💰 $1.36 per Bug Fix

⚠️ IMPORTANT: Model Availability Notice ⚠️

Kodezi Chronos is proprietary technology with exclusive access

Timeline	Access	Details
Q4 2025	Beta Access	Select enterprise partners via chronos.so
Q1 2026	General Availability	Via Kodezi OS platform

This repository contains research findings, benchmarks, and evaluation frameworks. The model itself is not publicly available.

🌟 Revolutionary AI That Debugs Like a Senior Developer

Quick Start • Get Early Access • Read Paper • View Benchmarks • Documentation • Case Studies

🏆 Breakthrough Performance Metrics

Overall Benchmark Results (5,000+ Real-World Debugging Scenarios)

Metric	Kodezi Chronos	GPT-4	Claude-3-Opus	Gemini-1.5-Pro	Improvement
Debug Success Rate	65.3%±1.4%*	8.5%±2.1%	7.8%±2.3%	11.2%±1.7%	5.8-8.4x
Root Cause Accuracy	78.4%±1.2%*	12.3%±1.8%	11.7%±2.0%	15.8%±1.5%	5.0-6.7x
Average Fix Cycles	2.2	6.5	6.8	5.1	2.3-3.1x faster
Retrieval Precision	91%±0.8%*	68%±2.3%	67%±2.4%	74%±1.8%	1.2-1.4x
Cost per Success	$1.36	$5.53	$6.67	$6.07	4.1-4.9x cheaper

*p < 0.001 compared to best baseline (two-tailed t-test, n=5,000)

Performance by Bug Category

Bug Category	Chronos	GPT-4	Claude-3	Gemini-1.5	Chronos Advantage
Syntax Errors	94.2%	82.3%	79.8%	85.1%	1.1x
Logic Bugs	72.8%	12.1%	10.7%	15.3%	6.0x
Concurrency Issues	58.3%	3.2%	2.8%	4.1%	18.2x
Memory Problems	61.7%	5.7%	4.3%	6.9%	10.8x
API Misuse	79.1%	18.9%	16.2%	22.4%	4.2x
Performance Bugs	65.4%	7.4%	6.1%	9.8%	8.8x

Repository Scale Performance

Repository Size	Chronos Success	Best Baseline	Baseline Model	Improvement
<10K LOC	71.2%±2.8%	21.3%±3.5%	Gemini-1.5-Pro	3.3x
10K-100K LOC	68.9%±2.5%	14.7%±3.2%	Gemini-1.5-Pro	4.7x
100K-1M LOC	64.3%±2.9%	8.9%±2.8%	Gemini-1.5-Pro	7.2x
>1M LOC	59.7%±3.1%	3.8%±1.9%	Gemini-1.5-Pro	15.7x

🧠 What Makes Chronos Revolutionary?

1. Debugging-First Architecture

Unlike code completion models trained on next-token prediction, Chronos is purpose-built from 42.5 million real debugging examples

2. Persistent Debug Memory

Learns from every debugging session across your codebase, improving continuously with cross-session pattern recognition

3. Adaptive Graph-Guided Retrieval (AGR)

Dynamic k-hop expansion enables unlimited context through intelligent graph traversal, not brute-force token expansion

4. Output-Optimized Design

Recognizes debugging as inherently output-heavy (~3K output vs ~3.6K input tokens), optimized for generating fixes, tests, and documentation

5. Autonomous Debugging Loop

Iteratively refines fixes through propose → test → analyze → refine cycles until all tests pass

🏗️ Seven-Layer Architecture

graph TD
    A[Multi-Source Input Layer] --> B[Adaptive Retrieval Engine]
    B --> C[Debug-Tuned LLM Core]
    C --> D[Orchestration Controller]
    D --> E[Execution Sandbox]
    E --> F[Validation Results]
    F --> G{Tests Pass?}
    G -->|No| H[Iterative Refinement]
    H --> B
    G -->|Yes| I[Persistent Memory Update]
    I --> J[Fix Deployed]
    
    style A fill:#f9f,stroke:#333,stroke-width:4px
    style C fill:#bbf,stroke:#333,stroke-width:4px
    style I fill:#bfb,stroke:#333,stroke-width:4px

Architecture Layers Explained

Multi-Source Input Layer
- Ingests heterogeneous debugging signals: source code, CI/CD logs, error traces, tests, documentation
- Processes 10+ input modalities simultaneously
Adaptive Retrieval Engine (AGR)
- Dynamic k-hop neighbor expansion (k=1-5 based on complexity)
- 89.2% precision vs 42.3% for flat retrieval
- Handles temporal code evolution and refactoring
Debug-Tuned LLM Core
- Trained on debugging workflows, not code completion
- Specialized tasks: root cause prediction, multi-file patches, test interpretation
- 78.4% root cause accuracy vs 15.8% best baseline
Orchestration Controller
- Manages autonomous debugging loop
- Hypothesis generation → fix refinement → rollback on failure
- Average 2.2 cycles to success
Persistent Debug Memory
- Repository-specific bug patterns and fixes
- Cross-session learning and adaptation
- 7.3x better token efficiency through memory
Execution Sandbox
- Isolated test execution environment
- CI/CD pipeline emulation
- Real-time validation without production risk
Explainability Layer
- Human-readable root cause explanations
- Automated PR descriptions and commit messages
- Risk assessment for proposed changes

📊 Multi-Random Retrieval (MRR) Benchmark

Revolutionary Evaluation Framework

Metric	Chronos	GPT-4+RAG	Claude-3+VectorDB	Gemini-1.5+Graph
Precision@10	89.2%	42.3%	48.1%	51.7%
Recall@10	84.7%	31.7%	36.2%	41.8%
Fix Accuracy	67.3%	8.9%	11.2%	14.6%
Context Efficiency	0.71	0.23	0.28	0.31

MRR tests real-world debugging by scattering context across 10-50 files over 3-12 months of history

🚀 Getting Started

Research Repository Setup

# Clone the repository
git clone https://github.com/kodezi/chronos-research.git
cd chronos-research

# Install dependencies
pip install -r requirements.txt

# Run performance analysis notebooks
jupyter notebook notebooks/performance_analysis.ipynb

# Generate benchmark visualizations
python scripts/generate_visualizations.py

Access Chronos Model

Step	Action	Timeline
1	Join Waitlist	Available Now
2	Beta Access	Q4 2025
3	General Availability	Q1 2026

📁 Repository Structure

chronos-research/
├── paper/                    # Research paper (arXiv:2507.12482)
│   ├── chronos-research.md   # Full paper content
│   ├── figures/              # All paper figures
│   └── tables/               # Performance data tables
├── benchmarks/               # Evaluation frameworks
│   ├── multi-random-retrieval/  # MRR benchmark suite
│   ├── debug_categories/        # Bug taxonomy
│   └── evaluation_metrics/      # Metrics implementation
├── results/                  # Performance analysis
│   ├── case_studies/         # Real debugging examples
│   ├── ablation_studies/     # Component analysis
│   └── performance_tables/   # Detailed metrics
├── architecture/             # System design docs
│   ├── agr_retrieval.md     # AGR algorithm details
│   ├── memory_engine.md     # Persistent memory design
│   └── debugging_loop.md    # Autonomous loop
├── evaluation/               # Testing methodology
├── examples/                 # Code examples
├── docs/                     # User documentation
├── notebooks/                # Analysis notebooks
└── scripts/                  # Utility scripts

🌟 Key Innovations

1. Revolutionary Training Dataset

42.5M total debugging examples
15M GitHub issues with linked PRs and fixes
8M stack traces paired with resolutions
3M CI/CD logs from failed and fixed builds
2.5M production debugging sessions
14M examples from Defects4J, SWE-bench, BugsInPy

2. Adaptive Graph-Guided Retrieval (AGR)

Performance by Retrieval Depth:
k=1 (Direct): 58.2% success
k=2 (Expanded): 72.4% success  
k=3 (Deep): 71.8% success
k=adaptive: 87.1% success (dynamic depth selection)
Flat retrieval: 23.4% success

3. Output-Heavy Optimization

Token Distribution in Debugging:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Input Tokens:           ~3,600 (sparse)
Output Tokens:          ~3,000 (dense)
Output Entropy Density: 47.2% (vs 12.8% for code completion)

4. Persistent Debug Memory

Cross-session learning improves success rate from 35% → 65% over time
7.3x token efficiency through intelligent memory
Repository-specific pattern recognition
Temporal code evolution tracking

📈 Detailed Performance Analysis

Language-Specific Performance

Language	Chronos	GPT-4	Claude-3	Gemini-1.5	Test Suite
Python	68.7%±2.1%	11.2%±2.8%	10.3%±2.9%	14.6%±2.6%	1,823 bugs
JavaScript	64.2%±2.3%	7.8%±2.5%	6.9%±2.6%	10.1%±2.4%	1,547 bugs
Java	63.9%±2.2%	6.3%±2.2%	5.7%±2.3%	9.2%±2.1%	1,630 bugs
Go	66.8%±2.4%	9.1%±2.6%	8.4%±2.7%	12.3%±2.5%	892 bugs
C++	61.2%±2.6%	5.2%±2.1%	4.8%±2.2%	7.9%±2.0%	1,108 bugs

Debugging Cycle Efficiency

Iteration	Chronos Success	GPT-4 Success	Time Reduction
1st Attempt	42.3%	3.2%	-87% time
2nd Attempt	58.7% (+16.4%)	5.1% (+1.9%)	-83% time
3rd Attempt	65.3% (+6.6%)	6.8% (+1.7%)	-79% time
4+ Attempts	65.3% (converged)	8.5% (+1.7%)	-74% time

Context Window Efficiency

Model	Context Size	Debug Success	Note
GPT-4-32K	32K tokens	7.2%	More context ≠ better debugging
Claude-3-200K	200K tokens	9.8%	Attention dilution at scale
Gemini-1.5-Pro-1M	1M tokens	14.3%	Best traditional model
Chronos	Unlimited*	71.2%	*Via intelligent retrieval

🔬 Ablation Studies

Component Contribution Analysis

Configuration	Debug Success	Impact
Full Chronos	65.3%	Complete system
No Multi-Code Association	35.8%	-45% performance
Static Memory Only	40.1%	-39% performance
No Orchestration Loop	42.5%	-35% performance
No AGR (Flat Retrieval)	28.7%	-56% performance

📚 Documentation

Getting Started	Architecture	Benchmarks	API Reference
Quick start guide	System design details	Evaluation methodology	Future API documentation

Performance	Case Studies	FAQ	Limitations
Detailed metrics	Real-world examples	Common questions	Known constraints

🤝 Contributing

We welcome contributions to the evaluation framework and benchmarks!

# Fork and clone
git clone https://github.com/[your-username]/chronos-research
cd chronos-research

# Create feature branch
git checkout -b feature/your-contribution

# Make changes and test
python -m pytest tests/

# Submit PR
git push origin feature/your-contribution

See CONTRIBUTING.md for detailed guidelines.

📝 Citation

If you use this research in your work, please cite:

@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}
}

🏢 About Kodezi

Kodezi is building the future of autonomous software maintenance. Our mission is to empower developers with AI that truly understands code at scale.

📞 Contact & Community

Connect With Us

Join the Discussion

📄 License

This research repository is licensed under the MIT License - see LICENSE for details.

⚠️ Important: The Kodezi Chronos model itself is proprietary technology and is not included in this repository. Model waitlist access is available at chronos.so.

🚀 The Future of Debugging is Here

Join Waitlist → | Read Paper → | Learn More →

_{Built with ❤️ by the Kodezi Team}

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.github		.github
architecture		architecture
benchmarks		benchmarks
docs		docs
evaluation		evaluation
examples		examples
model_access		model_access
notebooks		notebooks
paper		paper
results		results
scripts		scripts
tests		tests
visualizations		visualizations
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MODEL_ACCESS.md		MODEL_ACCESS.md
Makefile		Makefile
NOTICE.md		NOTICE.md
QUICK_START.md		QUICK_START.md
README.md		README.md
requirements.txt		requirements.txt
setup.py		setup.py

License

aamir786-crypto/Chronos

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