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, CI histories, distributed traces, 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 local token patterns without understanding how bugs propagate across modules, how concurrency creates unpredictable timing, how regressions appear after refactors, or how system state drifts over weeks of deployments. They operate in isolation, with no memory or historical awareness.

Chronos was created to close that gap. It handles the full debugging lifecycle. This includes locating the root cause across files and time, retrieving only the relevant parts of the codebase, synthesizing structured fixes, running tests, refining the patch, documenting the solution, and storing new patterns for future reuse.

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, CI failures, diffs, configs, metrics, commit history, dependency graphs, and bug reports.

Adaptive Graph-Guided Retrieval (AGR)
Expands and refines context from a persistent repository graph using adaptive k-hop traversal. It follows imports, function calls, co-change signals, temporal edges, and test relationships.

Debug-Tuned LLM Core
A transformer trained on bug-fix pairs, regression histories, CI logs, stack traces, race conditions, and long-tail debugging edge cases.

Autonomous Orchestration Loop
Proposes a fix, tests it, interprets results, retrieves new context, refines the patch, and continues until a fully validated solution is reached.

Persistent Debug Memory
Stores long-term patterns of recurring bugs, anti-pattern signatures, test signals, configuration pitfalls, and fix shapes.

Execution Sandbox
Validates every fix in an isolated containerized environment that mirrors your CI and test stack.

Explainability Layer
Produces human-readable pull request descriptions, rationale, risk assessments, and change summaries.

Chronos operates as a closed loop. It does not require prompts or step-by-step supervision. It detects a bug, retrieves what it needs, plans a fix, tests it, refines it, and documents the final patch.

Retrieval Without Token Limits

Chronos does not rely on static context windows. Instead, it uses intelligent retrieval built on three signals:

1. AST and control flow embeddings

2. Repository dependency graph

3. Temporal and co-change history across commits

At runtime, Chronos assembles only the artifacts relevant to the issue. This allows it to reason across millions of lines of code without the cost or instability of giant context windows.

AGR retrieves deeper, more causally relevant context than any vector database or long-context transformer. In real-world tests, AGR yielded more than a two hundred percent increase in context precision compared to vector search.

Trained on Real Debugging Workflows

Chronos-1 was trained and fine-tuned on:

• 15 million debugging sessions from GitHub issues, fixes, and pull requests

• 8 million stack traces with validated resolution paths

• 3 million CI and distributed system failure logs

• Public bug datasets including Defects4J, SWE-bench, BugsInPy, and ML pipeline failures

• Specialized tasks for race conditions, timestamp bugs, root cause analysis, regression tracking, and multi-step refinement

• Iterative test generation and evaluation loops

Unlike code models that learn idealized patterns, Chronos learns from real world debugging signals.

Benchmark Performance

Chronos was evaluated on debugging benchmarks that scatter context across many files, commits, and system components.

Chronos outperforms general-purpose models by a wide margin. It retrieves more useful context, validates its patches, and learns from every run.

Chronos also achieves:

• 71.5 percent success on million-token debugging tasks

• 65.3 percent real world debugging success

• 80.33 percent on SWE-bench Lite (241 of 300 issues)

• 3 to 10 times improvement on concurrency, memory, and distributed system bugs

• Lower effective cost per fix than any competing system

Example Scenarios

In real evaluations, Chronos was able to:

Resolve an authentication failure across three modules
It traced a null pointer introduced during a refactor, adjusted the authentication token refresh logic, updated export validation, and generated three new edge case tests.

Fix message loss in a distributed async queue
Chronos detected a race condition between acknowledge and connection release, applied the correct ordering, added rollback logic, and validated handling across ten million messages.

Repair a payment regression
Chronos located a stale dependency introduced in two separate commits and applied consistent updates across four modules, then updated the test suite.

Chronos understands what broke, why it broke, and how to fix it.

Debugging Cost and Time Impact

Chronos autonomously completes an entire debug cycle in about 135 seconds per issue. It maintains a 65 percent success rate with an average cost of 89 cents per validated fix. Since the effective cost per successful fix is lower than both manual debugging and competing LLM systems, engineering teams see significant savings in hours and production incidents.

For teams with 50 to 100 developers, Chronos reduces more than ninety thousand debugging hours per year and yields multi-million dollar savings.

Available in Kodezi OS Starting Q1 2026

Chronos-1 will be available in early 2026 through Kodezi OS. It will run continuously inside your environment, integrated with your IDE, CI pipelines, observability tools, and version control. It requires no prompts. It requires no manual orchestration. Chronos observes issues as they emerge, retrieves the context it needs, proposes solutions, tests them, and applies fixes with precision.

Chronos does not serve as an autocomplete engine. It works as a true debugging partner that traces root causes, proposes validated fixes, and documents each change. It turns debugging from a scattered manual effort into a structured and automated engineering process.

Modern software maintenance is not limited by token windows. It is limited by whether a system can retrieve the right context and use it effectively. 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