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Frequently Asked Questions

General Questions

Q: What is Kodezi Chronos?

A: Kodezi Chronos is the first debugging-first language model specifically designed for autonomous bug detection, root cause analysis, and validated fix generation. It achieves a 67.3% debugging success rate, representing a 4-5x improvement over state-of-the-art models including Claude Opus 4 and GPT-4.1.

Q: How can I access the Chronos model?

A: The Chronos model is proprietary and will be available:

  • Q4 2025: Beta access for select enterprise partners via chronos.so
  • Q1 2026: General availability through Kodezi OS

Q: What makes Chronos different from GitHub Copilot or other code assistants?

A: Key differences include:

  • Purpose-built for debugging (not code completion)
  • Persistent memory across sessions
  • Autonomous debugging loop with validation
  • Repository-scale understanding via AGR
  • Output-optimized architecture for generating fixes

Technical Questions

Q: What is Adaptive Graph-Guided Retrieval (AGR)?

A: AGR is Chronos's novel retrieval mechanism that:

  • Represents code as a graph with typed relationships
  • Dynamically expands retrieval depth based on query complexity (O(k log d) complexity)
  • Achieves unlimited effective context without massive context windows
  • Provides 92% precision at 85% recall
  • Achieves 87.1% debug success vs 23.4% for flat retrieval

Q: How does Chronos handle large repositories (>1M LOC)?

A: Chronos maintains strong performance on large codebases through:

  • Hierarchical embeddings (token → statement → function → module)
  • Lazy loading with smart caching
  • AGR's focused retrieval (only relevant context)
  • Success rate of 59.7% even on 1M+ LOC repos (15.7x better than best baseline)

Q: What programming languages does Chronos support?

A: The research focused on Python, JavaScript, and Java. Additional language support will be announced closer to the release date.

Q: Can Chronos fix all types of bugs?

A: No. Chronos excels at:

  • Logic errors (72.8% success)
  • API issues (79.1% success)
  • Concurrency bugs (58.3% success)

But has limitations with:

  • Hardware-dependent bugs (23.4% success)
  • Dynamic language issues (41.2% success)
  • Distributed systems coordination (~30% success)

Research Questions

Q: How was the 67.3% success rate measured?

A: Success rate was measured on 5,000 real-world debugging scenarios (12,500 total bugs with variations) where:

  • Each bug had a verified human fix
  • Success meant the generated fix passed all tests
  • No regressions were introduced
  • Results were statistically validated (p < 0.001)
  • Cohen's d = 3.87 effect size vs baselines

Q: What is the Multi Random Retrieval (MRR) benchmark?

A: MRR is our novel benchmark that:

  • Scatters debugging context across 10-50 files
  • Spans 3-12 months of commit history
  • Includes obfuscated dependencies
  • Tests multi-modal retrieval (code, tests, logs, docs)
  • Chronos achieves 89.2% precision vs 55-62% for competitors

Q: How does Chronos compare to Claude 4 and GPT-4.1?

A: On debugging tasks specifically:

  • Chronos: 67.3% success, 7.8 iterations, 89% human preference
  • Claude 4 Opus: 14.2% success, 2.3 iterations
  • GPT-4.1: 13.8% success, 1.8 iterations
  • Despite Claude 4 achieving 72.5% on SWE-bench for code generation

Q: What is Persistent Debug Memory (PDM)?

A: PDM is Chronos's cross-session learning system that:

  • Stores patterns from 15M+ debugging sessions
  • Maintains bug patterns, fixes, and codebase evolution
  • Achieves 87% cache hit rate on recurring bugs
  • Enables 6.8x faster resolution of similar issues

Q: Why does Chronos perform more iterations (7.8) than other models?

A: Chronos's iterative approach:

  • Validates each fix through actual test execution
  • Refines based on test failures (not just syntax)
  • Prevents regressions through comprehensive testing
  • Results in 94.6% regression avoidance vs ~70% for single-shot approaches

Q: How does AGR achieve O(k log d) complexity?

A: AGR's efficiency comes from:

  • Adaptive k-hop expansion (stops when confident)
  • Typed edge traversal (prioritizes relevant paths)
  • Entropy-based early stopping
  • Average 127 nodes retrieved vs 500+ for flat top-k
  • Includes temporal dispersion (3-12 months)
  • Features obfuscated dependencies
  • Better reflects real-world debugging complexity

Performance Questions

Q: What are Chronos's performance characteristics?

A: Key performance metrics:

  • Retrieval: 92% precision, 85% recall at k=10
  • Speed: 47ms cached retrieval vs 3.2min cold start
  • Tokens: 31.2K average retrieved (vs 89K+ for competitors)
  • Time: 42.3 minutes average debug time
  • Cost: ~$2.10 per successful fix

Q: How does Chronos handle different repository sizes?

A: Performance by repository scale:

  • <10K LOC: 71.2% success (3.3x improvement)
  • 10K-100K LOC: 68.9% success (4.7x improvement)
  • 100K-1M LOC: 64.3% success (7.2x improvement)
  • >1M LOC: 59.7% success (15.7x improvement)

Q: What types of bugs is Chronos best at fixing?

A: Success rates by category:

  • Syntax errors: 94.2% (1.1x improvement)
  • API misuse: 79.1% (4.2x improvement)
  • Logic bugs: 72.8% (6.0x improvement)
  • Performance issues: 65.4% (8.8x improvement)
  • Memory problems: 61.7% (10.8x improvement)
  • Concurrency issues: 58.3% (18.2x improvement)

Implementation Questions

Q: Can I use Chronos with my existing IDE?

A: Chronos will integrate with:

  • VSCode, IntelliJ, and other major IDEs
  • CI/CD pipelines (Jenkins, GitHub Actions, etc.)
  • Git workflows for automated debugging
  • Details will be announced with the Kodezi OS release

Q: What are the system requirements?

A: Specific requirements will be announced, but expect:

  • Cloud-based inference (no local GPU required)
  • Repository indexing time: 2-4 hours per 1M LOC
  • Incremental updates: <100ms per file change
  • Storage: ~100GB per million LOC for PDM

Q: How does Chronos ensure code security?

A: Security measures include:

  • All processing in isolated containers
  • No code leaves your infrastructure (on-premise option)
  • Audit logs for all model actions
  • Configurable fix approval workflows

Q: Can I reproduce the evaluation results?

A: While the model is proprietary, you can:

  • Use our benchmark specifications
  • Apply our evaluation protocols to your models
  • Compare results using our metrics
  • Access anonymized result data

Practical Questions

Q: When will Chronos be available?

A:

  • Q1 2026: Full release via Kodezi OS platform
  • Early Access: Join the waitlist at kodezi.com/os

Q: How much will Chronos cost?

A: Pricing will be announced closer to release. The research shows an effective cost of $1.36 per successfully fixed bug, compared to $5.53-$6.67 for competing models.

Q: Can Chronos be integrated with my existing tools?

A: Yes, Chronos will integrate with:

  • Popular IDEs (VS Code, IntelliJ, etc.)
  • CI/CD pipelines
  • Code review systems
  • Issue tracking platforms

Q: Does Chronos require internet connectivity?

A: Details about deployment options (cloud vs. on-premise) will be announced with the product release.

Privacy and Security

Q: How does Chronos handle proprietary code?

A:

  • Persistent memory is stored locally per repository
  • No code sharing between organizations
  • Enterprise deployment options available
  • SOC 2 compliance planned

Q: Can Chronos introduce security vulnerabilities?

A: Chronos includes:

  • Automated security scanning of generated fixes
  • Sandboxed execution environment
  • Regression testing to prevent new vulnerabilities
  • Option to require human approval for sensitive code

Research Collaboration

Q: Can I contribute to Chronos research?

A: Yes! You can:

  • Submit benchmark improvements
  • Propose new evaluation metrics
  • Share anonymized debugging scenarios
  • Contribute to analysis tools

See CONTRIBUTING.md for details.

Q: How do I cite the Chronos research?

A: Use the BibTeX 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}
}

Contact

Q: Who do I contact for:

Q: Where can I learn more?