Research Paper

Chronos-1 is supported by extensive academic-grade research and evaluation published in peer-reviewed venues. This foundation establishes Chronos as the first language model engineered specifically for repository-scale debugging, rather than code generation.

Official Publication

Title: Kodezi Chronos-1: A Debugging First Language Model for Repository Scale Code Understanding

Authors: Ishraq Khan, Assad Chowdary, Sharoz Haseeb, Urvish Patel, Yousuf Zaii

Institution: Kodezi Inc.

Publication: arXiv:2507.12482 (2025)

Training Cutoff: December 2024

The paper details Chronos’ architecture, retrieval strategies, memory systems, and large-scale evaluation across real-world debugging scenarios.

Key Contributions

Chronos-1 introduces multiple fundamental breakthroughs:

1. First debugging-specific language model architecture, featuring persistent memory and adaptive retrieval.
2. Adaptive Graph Guided Retrieval (AGR), a multi-hop, edge-weighted traversal algorithm with O(k log d) complexity and proven convergence guarantees.
3. New real-world debugging benchmarks built from 12,500 bugs, including complex multi-random retrieval tasks.
4. 4-5× improvement over state-of-the-art models with strong statistical significance.
5. State-of-the-art 80.33% score on SWE-Bench Lite, holding a 20-point lead.

These contributions collectively establish Chronos-1 as the premier debugging-oriented LLM.

Theoretical Analysis

The paper provides rigorous complexity analysis and convergence proofs:

• AGR retrieval complexity: O(k_max × |S| × d^(k_max) × log(d^(k_max)))

• Confidence convergence: Under power-law distribution (α > 1), confidence reaches threshold with probability 1 − δ after O(log_d(1/δ)) iterations.

• Bounded retrieval path cost ensures efficient traversal even on codebases containing millions of nodes.

These guarantees allow AGR to scale efficiently while maintaining high precision.

Access to Research Materials

The research artifacts available publicly include:

• arXiv Paper: https://arxiv.org/abs/2507.12482
• GitHub Repository: https://github.com/kodezi/chronos
• Benchmark datasets and evaluation frameworks
• Architecture and algorithm documentation
• Case studies and debugging analysis
• Reference implementations of AGR and PDM algorithms

Important: The Chronos-1 model itself is proprietary and is not included in the public research repository.

Evaluation Methodology

Data contamination is prevented through strict temporal validation, with all test sets created after the training cutoff (December 2024). Leakage detection is performed using n-gram overlap analysis, showing less than 0.1 percent similarity.

Evaluation uses:

• 5-fold stratified cross-validation
• Repository-level isolation
• Nested cross-validation for hyperparameter tuning

Statistical rigor includes:

• Two-tailed t-tests
• Effect size measurement using Cohen’s d
• Confidence intervals for all metrics
• Multiple comparison correction using the Bonferroni method

These methodological safeguards ensure that Chronos’ outcomes are scientifically valid and reproducible.