🚀 Kodezi Chronos

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

🎯 67.3% Autonomous Debugging Success • 🔍 89% Human Preference • ⚡ 7.8 Average Fix Iterations • 💰 40% Time Reduction

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⚠️ Model Access Notice ⚠️

Chronos is proprietary and available exclusively through Kodezi OS

Timeline	Access	Details
Q4 2025	Beta	Limited enterprise access
Q1 2026	GA	Via Kodezi OS

This repository contains the MRR benchmark suite and evaluation framework only.

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🌟 Revolutionary AI That Debugs Like a Senior Developer

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

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🏆 MRR Benchmark Results

Overall Performance (5,000 MRR Scenarios)

Metric	Chronos	GPT-4.1	Claude-4	Gemini-2.0	Improvement
Debug Success Rate	67.3%	13.8%	14.2%	15.0%	4.5x
Root Cause Accuracy	89%*	12.3%±1.8%	11.7%±2.0%	15.8%±1.5%	5.6-7.6x
Average Fix Iterations	7.8	1-2	1-2	1-2	More thorough
Retrieval Precision	92%*	68%±2.3%	67%±2.4%	74%±1.8%	1.2-1.4x
Time Reduction	40%	-	-	-	40% faster

*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

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🧠 Key Innovations in Chronos

1. Debugging-First Architecture

• Trained on 42.5M real debugging examples (not code completion)
• Specialized for root cause analysis and multi-file patches
• 78.4% root cause accuracy vs 15.8% best baseline

2. Persistent Debug Memory (PDM)

• Repository-specific learning from debugging sessions
• Improves from 35% → 65% success rate over time
• Cross-session pattern recognition

3. Adaptive Graph-Guided Retrieval (AGR)

• O(k log d) complexity with dynamic k-hop expansion
• 92% precision, 85% recall on multi-file context
• Handles unlimited repository scale intelligently

4. Output-Optimized Design

• Optimized for ~3K output tokens (fixes, tests, docs)
• 47.2% output entropy density vs 12.8% for completion models
• Designed for complex patch generation

5. Autonomous Debugging Loop

• Average 7.8 iterations to successful fix
• Propose → test → analyze → refine cycles
• 67.3% fully autonomous success rate

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🏗️ Architecture Overview

Seven-Layer System Design

1. Multi-Source Input Layer: Processes code, logs, traces, tests, docs simultaneously
2. Adaptive Retrieval Engine (AGR): Dynamic k-hop graph traversal (92% precision)
3. Debug-Tuned LLM Core: 42.5M debugging examples, not code completion
4. Orchestration Controller: Autonomous debugging loop management
5. Persistent Debug Memory: Repository-specific learning (35% → 65% improvement)
6. Execution Sandbox: Isolated test validation environment
7. Explainability Layer: Human-readable root cause analysis

See architecture documentation for detailed specifications.

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📊 Multi-Random Retrieval (MRR) Benchmark

What is MRR?

MRR simulates real-world debugging complexity by:

• Spatial Distribution: Bug context scattered across 10-50 files
• Temporal Dispersion: Relevant information from 3-12 months of history
• Obfuscation Levels: Low/medium/high code complexity
• 5,000 Scenarios: Comprehensive evaluation across languages and bug types

MRR Results

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

Full benchmark available in benchmarks/multi-random-retrieval/

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🚀 Getting Started

Running the MRR Benchmark

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

# Install dependencies
pip install -r requirements.txt

# Run MRR benchmark on your model
python benchmarks/run_mrr_benchmark_2025.py \
  --model your_model \
  --scenarios 100  # Start with subset

# Analyze results
python benchmarks/analyze_results.py

Model Access

⚠️ The Chronos model is not included in this repository

Chronos will be available via Kodezi OS:

• Q4 2025: Enterprise beta
• Q1 2026: General availability
• Join waitlist: chronos.so

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📁 Repository Contents

chronos-research/
├── benchmarks/               # MRR Benchmark Suite
│   ├── multi-random-retrieval/  # 5,000 scenario benchmark
│   ├── evaluation_metrics/      # Metrics implementation
│   └── run_mrr_benchmark_2025.py  # Main benchmark runner
├── reference_implementations/  # Algorithm references (NOT the model)
│   ├── algorithms/            # AGR, PDM implementations
│   └── NOTICE.md             # Proprietary model notice
├── paper/                    # Research paper
│   └── chronos-research-2025.md  # Full paper (arXiv:2507.12482)
├── results/                  # Performance data
│   ├── raw_data/             # 5,000 scenario results
│   └── case_studies/         # Debugging examples
├── figures/                  # Paper visualizations
│   └── paper_figures/        # 11 paper figures
├── docs/                     # Documentation
├── MODEL_ACCESS.md          # How to access Chronos
└── LEADERBOARD.md           # Performance rankings

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🔬 Research Highlights

Training Dataset

• 42.5M debugging examples (not code completion)
• 15M GitHub issues with fixes
• 8M stack traces with resolutions
• 3M CI/CD debugging logs
• 2.5M production sessions
• 14M curated from Defects4J, SWE-bench, BugsInPy

AGR Performance

• k=1 hop: 58.2% success
• k=2 hops: 72.4% success
• k=adaptive: 87.1% success
• Flat retrieval: 23.4% success

PDM Learning Curve

• Initial: 35% success rate
• After 100 sessions: 52% success
• After 500 sessions: 65% success
• 7.3x token efficiency gain

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📈 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

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🔬 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

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📚 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

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🤝 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.

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📝 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}
}

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🏢 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.

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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.

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

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