80.33% SWE-bench Lite •67.3% Autonomous Debugging •89% Human Preference •40% Time Reduction

Table of Contents

Model Access Notice

Chronos is proprietary and available exclusively through Kodezi OS

This repository contains research paper, benchmarks, and evaluation results only.

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🏅 State-of-the-Art Results

📈 SWE-bench Lite Performance

Industry-Standard Benchmark Results

20 percentage point absolute lead over second place

The Debugging Gap

General-Purpose Models: Code Generation vs Debugging Performance

Key Insight: Even models achieving 70%+ on code generation drop to <15% on debugging tasks, revealing a 50+ percentage point gap.Chronos, purpose-built for debugging, achieves 80.33%—demonstrating that debugging requires specialized architectures, not just larger context windows.

Repository-Specific Results

SWE-bench Lite: Domain-Specific Performance

🔬 MRR Benchmark Results

p < 0.001 compared to best baseline (two-tailed t-test, n=5,000) • Sample dataset (n=500) available now, full benchmark Q1 2026

🐛 Performance by Bug Category

📏 Repository Scale Performance

💡 Key Innovations

1.Debugging-First Architecture

2.Persistent Debug Memory (PDM)

3.Adaptive Graph-Guided Retrieval (AGR)

4.Output-Optimized Design

5.Autonomous Debugging Loop

🏗️ Architecture

Seven-Layer System Design

┌─────────────────────────────────────────────┐│   7. Explainability Layer                   │  Human-readable root cause analysis├─────────────────────────────────────────────┤│   6. Execution Sandbox                      │  Isolated test validation├─────────────────────────────────────────────┤│   5. Persistent Debug Memory (PDM)          │  Repository-specific learning├─────────────────────────────────────────────┤│   4. Orchestration Controller               │  Autonomous debugging loop├─────────────────────────────────────────────┤│   3. Debug-Tuned LLM Core                   │  42.5M debugging examples├─────────────────────────────────────────────┤│   2. Adaptive Retrieval Engine (AGR)        │  Dynamic k-hop graph traversal├─────────────────────────────────────────────┤│   1. Multi-Source Input Layer               │  Code, logs, traces, tests, docs└─────────────────────────────────────────────┘

Layer Descriptions

🧪 Benchmarks & Evaluation

📋 Available Benchmarks

🏆 SWE-bench Lite Evaluation Results

🎯 Multi-Random Retrieval (MRR) Benchmark

📚 Research Paper

Published Research

Paper Resources

Key Contributions

🚀 Getting Started

Prerequisites

# Python 3.8+ requiredpython --version# Git for cloninggit --version

Quick Start: Running Benchmarks

# Clone the repositorygit clone https://github.com/kodezi/chronos-research.gitcd chronos-research# Install dependenciespip install -r requirements.txt# Run MRR benchmark on your modelpython benchmarks/run_mrr_benchmark_2025.py \  --model your_model \  --scenarios 100# Start with subset for testing# Run full sample evaluation (500 scenarios available)python benchmarks/run_mrr_benchmark_2025.py \  --model your_model \  --scenarios 500# Analyze resultspython benchmarks/analyze_results.py \  --results_dir results/your_model

Model Access

📁 Repository Structure

chronos-research/│├── benchmarks/                    # Benchmark Suite│   ├── multi-random-retrieval/      # 5,000 scenario MRR benchmark (500 sample available)│   ├── comprehensive_benchmarks/    # Extended test scenarios│   ├── debug_categories/            # Bug type categorization (6 types)│   ├── evaluation_metrics/          # Custom metrics implementation│   ├── run_mrr_benchmark_2025.py    # Main benchmark runner│   └── analyze_results.py           # Results analysis tools│├── evaluation/                    # Evaluation Results│   └── lite/                        # SWE-bench Lite results (80.33%)│       └── 20251111_kodezi_chronos_1/  # Official submission│           ├── all_preds.jsonl      # All 300 predictions│           ├── logs/                # 300+ execution logs│           ├── results/             # Per-instance results│           └── trajs/               # Debugging trajectories│├── paper/                         # Research Paper│   ├── chronos-research.md          # Full paper (arXiv:2507.12482)│   ├── chronos-research-2025.md     # 2025 updates│   ├── abstract.md                  # Executive summary│   ├── methodology.md               # Research methodology│   └── figures/                     # Visualizations│├── architecture/                  # Architecture Documentation│   ├── README.md                    # Architecture overview│   ├── AGR_ALGORITHM.md             # Adaptive Graph-Guided Retrieval│   ├── memory_engine.md             # Persistent Debug Memory (PDM)│   └── debugging_loop.md            # Autonomous loop design│├── results/                       # Performance Data│   ├── figures/                     # 15+ SVG visualizations│   ├── ablation_studies/            # Component impact analysis│   ├── case_studies/                # Real-world debugging examples│   └── raw_data/                    # Benchmark outputs (CSV/JSON)│├── reference_implementations/     # Algorithm Reference Code│   ├── algorithms/                  # AGR, PDM reference implementations│   └── NOTICE.md                    # Proprietary notice│├── docs/                          # Documentation│   ├── getting_started.md           # Quick start guide│   ├── API_DOCUMENTATION.md         # API reference (Q1 2026)│   ├── faq.md                       # Frequently asked questions│   └── limitations.md               # Known constraints│├── LEADERBOARD.md                 # Performance rankings├── CITATION.cff                   # Citation information (BibTeX)├── CONTRIBUTING.md                # Contribution guidelines├── LICENSE                        # MIT License + proprietary notice└── requirements.txt               # Python dependencies

🔬 Research Highlights

Training Dataset Composition

AGR Performance by Depth

PDM Learning Curve

📊 Detailed Performance

Language-Specific Performance

Debugging Cycle Efficiency

Note: Chronos performs more thorough iterations (7.8 avg) vs competitors (1-2 avg)

Context Window Efficiency

Ablation Studies

📖 Documentation

🤝 Contributing

How to Contribute

# 1. Fork and clone the repositorygit clone https://github.com/[your-username]/chronos-research.gitcd chronos-research# 2. Create a feature branchgit checkout -b feature/your-contribution# 3. Make your changes# - Add new benchmarks# - Improve documentation# - Fix bugs in evaluation scripts# 4. Run testspython -m pytest tests/# 5. Commit your changesgit add.git commit -m"feat: description of your changes"# 6. Push and create PRgit push origin feature/your-contribution

Contribution Guidelines

📝 Citation

@article{khan2025chronos,title={Kodezi Chronos: A Debugging-First Language Model for         Repository-Scale Code Understanding},author={Khan, Ishraq and Chowdary, Assad and          Haseeb, Sharoz and Patel, Urvish and Zaii, Yousuf},journal={arXiv preprint arXiv:2507.12482},year={2025},url={https://arxiv.org/abs/2507.12482},note={State-of-the-art: 80.33\% on SWE-bench Lite}}

🏢 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

Sales:sales@kodezi.comSupport:support@kodezi.comPartnerships:partnerships@kodezi.com

📄 License

MIT License

⚠️ Important Notice

📦 What's Included Under MIT License

🔒 Proprietary Components

🚀 Chronos Model Access

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_{Last Updated: November 2025 | Version: 2.0.0}