🚀 Kodezi Chronos

The World's First Debugging-First Language Model

🎯 65.3% Autonomous Debugging Success • 🔍 78.4% Root Cause Accuracy • ⚡ 2.2 Average Fix Cycles

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🌟 Revolutionary AI That Debugs Like a Senior Developer

Learn More • Get Early Access • Read Paper • View Benchmarks • Documentation • Case Studies

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📊 Performance Metrics

Overall Benchmark Results (5,000 Real-World Bugs)

Metric	Chronos	GPT-4	Claude-3	Gemini-1.5
Debug Success	65.3%±1.4%	8.5%±2.1%	7.8%±2.3%	11.2%±1.7%
Root Cause	78.4%±1.2%	12.3%±1.8%	11.7%±2.0%	15.8%±1.5%
Avg Cycles	2.2	6.5	6.8	5.1
Retrieval Precision	91%±0.8%	68%±2.3%	67%±2.4%	74%±1.8%
Cost per Bug	$1.36	$5.53	$6.67	$6.07
Improvement	—	7.7x	8.4x	5.8x

All comparisons show p < 0.001 (two-tailed t-test)

Performance Across Bug Categories

Bug Type	Chronos	GPT-4	Claude-3	Gemini-1.5	Improvement
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	58.3%	3.2%	2.8%	4.1%	18.2x
Memory Issues	61.7%	5.7%	4.3%	6.9%	10.8x
API Misuse	79.1%	18.9%	16.2%	22.4%	4.2x
Performance	65.4%	7.4%	6.1%	9.8%	8.8x

Repository Scale Performance

Repository Size	Chronos	Best Baseline	Notes
<10K LOC	71.2%	21.3% (Gemini)	Small projects
10K-100K LOC	68.9%	14.7% (Gemini)	Medium projects
100K-1M LOC	64.3%	8.9% (Gemini)	Large codebases
>1M LOC	59.7%	3.8% (Gemini)	Enterprise scale

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🚨 Model Availability

⚠️ Important Notice The Kodezi Chronos model is proprietary technology available exclusively through Kodezi OS Learn more at chronos.so This repository contains research findings, benchmarks, and evaluation frameworks. The model itself is not publicly available.

Release Timeline

• Q4 2025: Beta access for select enterprises
• Q1 2026: General availability via Kodezi OS
• Website: chronos.so
• Early Access: kodezi.com/os

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🧠 What Makes Chronos Revolutionary?

Debugging-First Architecture

Unlike code completion models, Chronos is purpose-built for finding and fixing bugs

Persistent Debug Memory

Learns from every debugging session, improving continuously

Repository-Scale Understanding

Handles codebases with millions of lines through intelligent retrieval

Autonomous Debugging Loop

Iteratively refines fixes until all tests pass

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🏗️ Architecture Overview

graph TD
    A[Multi-Source Input] --> 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| B
    G -->|Yes| H[Memory Update]
    H --> I[Fix Deployed]

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Seven-Layer Architecture

1. Multi-Source Input Layer - Code, logs, traces, tests, docs
2. Adaptive Retrieval Engine - AGR with dynamic k-hop expansion
3. Debug-Tuned LLM Core - Specialized for debugging workflows
4. Orchestration Controller - Manages autonomous debugging loop
5. Persistent Debug Memory - Cross-session pattern learning
6. Execution Sandbox - Safe validation environment
7. Explainability Layer - Human-readable explanations

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Breakthrough Results

Multi Random Retrieval (MRR) Benchmark

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

Cost Effectiveness

                 Cost per Bug    Success Rate    Effective Cost
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Chronos          $0.89           65.3%           $1.36
GPT-4            $0.47            8.5%           $5.53  
Claude-3         $0.52            7.8%           $6.67  
Human Dev        $180            94.2%           $191   

47:1 ROI in First Year for 100-Developer Team

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🚀 Getting Started

🔬 Explore the Research

# 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
jupyter notebook notebooks/performance_analysis.ipynb

# Generate visualizations
python scripts/generate_visualizations.py

📂 Repository Structure

chronos-research/
├── paper/                    # Research paper and materials
├── benchmarks/               # Evaluation frameworks
├── results/                  # Performance data and analysis
├── architecture/             # System design documentation
├── evaluation/               # Testing methodology
├── demos/                    # Interactive examples
├── docs/                     # Comprehensive documentation
├── notebooks/                # Jupyter analysis notebooks
├── scripts/                  # Utility scripts
└── tests/                    # Test suite

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🌟 Key Innovations

15M+ GitHub Issues Training Data

Real-world debugging scenarios with verified fixes from production codebases

Adaptive Graph-Guided Retrieval (AGR)

• Dynamic k-hop expansion based on query complexity
• 89.2% precision vs 42.3% for flat retrieval
• Handles temporal code evolution and refactoring

Output-Optimized Architecture

• Debugging is output-heavy: ~3K output tokens vs ~3.6K input
• Specialized for generating fixes, tests, and documentation
• Quality over quantity approach

Persistent Debug Memory

• Learns from every debugging session
• Cross-session pattern recognition
• Repository-specific bug patterns

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📚 Documentation

User Guide	Architecture	Benchmarks
Get started with Chronos	Understand the system design	Evaluation methodology

Results	Case Studies	FAQ
Detailed performance metrics	Real-world debugging examples	Common questions

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Detailed Performance Analysis

Language-Specific Performance

Language	Chronos	GPT-4	Claude-3	Gemini-1.5	Test Suite Size
Python	68.7%	11.2%	10.3%	14.6%	1,823 bugs
JavaScript	64.2%	7.8%	6.9%	10.1%	1,547 bugs
Java	63.9%	6.3%	5.7%	9.2%	1,630 bugs

Iteration Efficiency

Iterations	Chronos Success	GPT-4 Success	Time Saved
1	42.3%	3.2%	87%
2	58.7%	5.1%	83%
3	65.3%	6.8%	79%
4+	65.3%	8.5%	74%

Root Cause Analysis Performance

Analysis Type	Chronos	GPT-4	Claude-3	Gemini-1.5
Syntax Issues	95.8%	87.3%	84.2%	89.1%
Logic Errors	81.3%	15.7%	13.2%	19.4%
State Problems	76.2%	8.9%	7.4%	11.3%
Concurrency	71.4%	4.2%	3.8%	5.9%

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🤝 Contributing

We welcome contributions to the research and evaluation frameworks!

# Fork the repository
git fork https://github.com/kodezi/chronos-research

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

# Commit your changes
git commit -m 'Add amazing contribution'

# Push to the branch
git push origin feature/amazing-contribution

# Open a Pull Request

See CONTRIBUTING.md for detailed guidelines.

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💰 ROI Analysis

Return on Investment for 100-Developer Team

Metric	Value	Annual Impact
Bugs Fixed Autonomously	65.3%	3,265 bugs/year
Developer Hours Saved	2.4 hrs/bug	7,836 hours
Cost Savings	$150/hour	$1,175,400
Chronos Cost	$25/dev/mo	$30,000
Net ROI		$1,145,400
ROI Ratio		47:1

Based on average of 50 bugs per developer per year

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🔬 Research Contributions

1. Novel Architecture: First debugging-specific language model
2. AGR Algorithm: Adaptive Graph-Guided Retrieval for unlimited context
3. MRR Benchmark: New evaluation framework for code understanding
4. Debug Memory: Persistent learning across debugging sessions
5. 15M+ Dataset: Largest curated debugging dataset from GitHub

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📝 Citation

@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}
}

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📁 Repository Contents

Performance Data

• /results/performance_tables/ - All 13 benchmark tables
• /results/figures/ - Architecture and performance visualizations
• /results/case_studies/ - Detailed debugging examples
• /results/ablation_studies/ - Component analysis

Evaluation Framework

• /benchmarks/multi-random-retrieval/ - MRR benchmark suite
• /evaluation/ - Testing methodology and protocols
• /notebooks/ - Interactive analysis notebooks

📚 Documentation

• /docs/ - Comprehensive user and technical guides
• /architecture/ - System design documentation
• /paper/ - Research paper and supplementary materials

Tools & Scripts

• /scripts/ - Evaluation and visualization tools
• /tests/ - Test suite for framework validation

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🌐 Deployment & Integration

Available via Kodezi OS (Q1 2026)

Learn More: chronos.so
Join Waitlist: kodezi.com/os

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📞 Contact & Community

Connect With Us

Join the Discussion

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📄 License

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

⚠️ Note: The Chronos model itself is proprietary and available only through Kodezi OS.

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The Future of Debugging is Here

Learn More → | Get Early Access →

_{Made with ❤️ by the Kodezi Team}