Announcements
July 23, 2025
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Chronos-1 is a new kind of model. It is not built to predict the next token in a function. It is designed to find what broke in your system, explain why it happened, and fix it. Chronos is the first large language model specifically trained and architected for debugging. It works across entire repositories, logs, configurations, pull requests, and historical test outcomes.
This is not another code generation tool. Chronos is a debugging system built for the complexity, scale, and messiness of real production software.
Why Chronos Exists
General-purpose LLMs treat debugging as an afterthought. They predict code based on surrounding tokens without understanding how bugs propagate across modules, evolve over time, or cause failures in CI pipelines. They operate in isolation, without memory or context beyond a single prompt.
Chronos was created to address that gap. It handles the full debugging lifecycle. This includes tracing the source of an error across files and time, proposing and validating structured fixes, generating tests, and documenting the result. It integrates closely with your codebase, tools, and feedback loops.
Chronos Architecture Overview
Chronos-1 is built on a debugging-specific architecture composed of seven layers:
Multi-Source Input Layer: Ingests code, stack traces, logs, configurations, diffs, test failures, pull requests, and bug reports.
Adaptive Graph-Guided Retrieval (AGR): Expands and refines context from a persistent repository graph using k-hop traversal based on query complexity.
Debug-Tuned LLM Core: A transformer model trained on bug-fix data, regression histories, and test-driven workflows.
Autonomous Orchestration Loop: Proposes fixes, runs tests, interprets results, refines the patch, and continues until a passing solution is reached.
Persistent Debug Memory: Stores long-term knowledge of bug patterns, test signals, coding conventions, and prior fix behavior.
Execution Sandbox: Validates every fix in an isolated environment that mirrors your CI/CD and test stack.
Explainability Layer: Outputs human-readable pull request descriptions, fix rationale, and confidence-scored risk assessments.
Chronos operates as a closed loop. It does not require prompts or human review to proceed. It detects a bug, retrieves what it needs, plans a fix, tests it, and documents the change. If the first attempt fails, it adjusts and tries again.
Retrieval Without Token Limits
Chronos uses intelligent retrieval instead of static context windows. It constructs a semantic index of your codebase using a hybrid of AST-aware embeddings, dependency graphs, and commit history. At runtime, it assembles only the code, documentation, and test artifacts relevant to the task at hand.
This approach allows Chronos to reason across entire repositories without excessive inference costs. It retrieves high-signal context from multiple code artifacts and composes a complete view of the problem.
Trained on Real Debugging Workflows
Chronos-1 was trained and fine-tuned on:
15 million GitHub issues with corresponding fixes and pull requests
8 million stack traces paired with successful resolution paths
3 million CI/CD failure logs and recovery events
Public bug datasets such as Defects4J, SWE-bench, and BugsInPy
Specialized tasks for root cause analysis, regression detection, iterative refinement, and test generation
Unlike code models that learn from idealized patterns, Chronos learns from the real-world signals engineers face during debugging.
Benchmark Performance
Chronos was evaluated using the Multi Random Retrieval (MRR) benchmark. This benchmark simulates debugging by scattering relevant context across many files and commits.
Model | Fix Accuracy | Retrieval Recall | Context Efficiency |
|---|---|---|---|
GPT-4 + RAG | 8.9% | 31.7% | 0.23 |
Claude + VectorDB | 11.2% | 36.2% | 0.28 |
Gemini + Graph | 14.6% | 41.8% | 0.31 |
Chronos-1 | 67.3% | 84.7% | 0.71 |
Chronos outperforms general-purpose models by a large margin. It retrieves deeper context, validates its fixes, and learns from every run.
Example Scenarios
In real evaluations, Chronos was able to:
Trace a null pointer exception introduced by an authentication refactor, apply a fix across three modules, and generate new unit tests that passed.
Detect a message loss issue in an asynchronous queue, identify a race condition, restructure the acknowledgment logic, and introduce rollback handling.
Fix a regression in a payment processing flow by tracking a stale dependency introduced across two commits and four different modules.
Chronos does not just write code. It understands what broke, where it broke, and how to fix it.
Debugging Cost and Time Impact
Chronos autonomously completes a full debugging loop in about 135 seconds per issue. With a 65 percent success rate and an average cost of 89 cents per fix, it has a lower effective cost than any competing tool or manual debugging team. For teams of 50 to 100 developers, this translates to significant savings in engineering hours and reduction in production bugs.
Available in Kodezi OS Starting Q1 2026
Chronos-1 will be available in early 2026 through Kodezi OS. It will run in the background of your existing stack, integrated into your IDE, CI/CD pipeline, observability tools, and issue trackers. You will not need to prompt it. You will not need to supervise each step. It observes, retrieves, proposes, tests, and applies fixes with minimal disruption.
Chronos does not autocomplete for you. It analyzes the problem with you, traces root causes, proposes fixes, and validates them. It reduces manual effort and helps catch issues before they spread. Debugging becomes a structured, automated process rather than a guessing game.
The challenge in modern software maintenance is not how much context a model can hold. The real challenge is whether it can use that context well. Chronos is built for that exact purpose.
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Read the Chronos research paper
Explore technical benchmarks and case studies
Request early access via Kodezi OS
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