Kodezi Chronos: Autonomous Code Debugging

• Kodezi Chronos is a next-generation architecture for repository-scale code understanding, debugging, and autonomous maintenance using a hybrid vector-graph memory engine.
• It employs an adaptive graph-guided retrieval mechanism to dynamically overcome fixed-context limitations in large language models.
• Its autonomous debugging workflow integrates real-time error detection, iterative fix proposals, and deep DevOps integration to enhance software reliability.

Kodezi Chronos denotes a next-generation architecture for repository-scale code understanding, debugging, and autonomous maintenance. It integrates a multi-level embedding memory engine to overcome fixed-context limitations in LLMs, enabling efficient reasoning, retrieval, and modification across millions of lines of code, heterogeneous documentation, and development history (Khan et al., 14 Jul 2025). Chronos is engineered for real-world software engineering workflows, elevating codebase comprehension, supporting complex debugging, and autonomously orchestrating repair and refactoring actions in a continuous feedback loop.

1. System Architecture and Memory Engine

Kodezi Chronos addresses the principal bottleneck in LLM-based code models: the constrained inference-time context and absence of explicit code structure reasoning. Its architecture is distinguished by a memory engine combining vector-based and graph-based indexing:

• Vector Encoding: Each code artifact—ranging from low-level tokens up to modules and entire repositories—is encoded into a high-dimensional vector space, enabling similarity-based retrieval. For an artifact $x_i$, the embedding is $v_{i} = f(x_i)$ and similarity can be quantified by $$\mathrm{sim}(v_{i}, v_{j}) = \frac{v_{i} \cdot v_{j}}{\|v_{i}\| \|v_{j}\|}$$.
• Graph-Based Indexing: Code components, documentation, logs, tests, commit history, and CI/CD pipeline artifacts are represented as nodes. Edges denote program relations (e.g., function calls, imports, bug-ticket links). This captures structural and semantic dependencies beyond what sequence-based retrieval can offer.
• Adaptive Memory Access: Chronos employs an Adaptive Graph-Guided Retrieval (AGR) mechanism, which dynamically expands the retrieval neighborhood (k-hop traversal) according to query complexity and a confidence-driven threshold. This enables ultra-long context reasoning without imposing tractable window limits or context fragmentation.

A typical retrieval workflow assembles the multi-modal context:

Code, Docs, Logs ──► Memory Engine (Embedding + Graph) ──► Adaptive Retriever ──► Reasoning Transformer

This design allows Chronos to efficiently retrieve, relate, and reason over arbitrarily dispersed and indirectly linked artifacts, thus supporting repository-scale cognitive tasks.

2. Autonomous Debugging Workflow

Chronos is explicitly designed for autonomous code debugging and maintenance without requiring manual orchestration:

1. Issue Detection: Chronos monitors heterogeneous signals (error traces, build/test logs, source code diffs, etc.) to autonomously pinpoint defects, even when causal relationships span distant parts of the repository.
2. Context Assembly: Upon detecting an issue, Chronos retrieves a minimal relevant subgraph—the set of code, historical bug reports, documentation, and related test failures—using the AGR engine.
3. Solution Generation: The transformer-based reasoning core, pre-trained and fine-tuned explicitly on debugging workflows, synthesizes fixes together with root-cause analyses, suggested test amendments, and updated documentation.
4. Self-Healing Loop: Chronos operates an iterative closed loop: it proposes a fix, executes it in sandboxed isolation, runs associated verification tests, and iteratively refines the solution until all validation checks pass. This loop reduces the developer’s manual debugging workload by automating not only patch synthesis but also validation and explanation generation.
5. DevOps Integration: Chronos is natively integrated with IDEs and CI/CD pipelines. It persistently updates its codebase memory in response to code reviews, test results, and operational logs, enabling continuous repository improvement.

3. Benchmarking and Empirical Performance

A novel evaluation method, termed the Multi Random Retrieval (MRR) benchmark, was introduced to rigorously test Chronos’s repository-scale capabilities. MRR requires the model to resolve associations and retrievals across randomly distributed, obfuscated, and heterogeneous code artifacts—representing realistic engineering challenges (variable tracing, cross-file bug localization, dependency migration).

Key empirical findings:

• Chronos achieves a MRR Fix Accuracy of 67.3%, compared to single-digit percentages for competitive models such as GPT-4 + retrieval-augmented generation (RAG) or Gemini-1.5 + Graph augmentation.
• Chronos reduces debugging cycles by up to 40% compared to sequence-based models—the average number of cycles drops from 6.5–7 to 2.2 per bug fix.
• Context assembly via AGR achieves retrieval precision above 89% and recall above 84%, directly supporting the overall improvement in bug detection and repair accuracy.
• Debug success rates approach 87% when retrieval depth adapts dynamically based on the complexity of the debugging query.

4. Technical Innovations

Chronos introduces several architectural innovations for code intelligence and debugging:

• Persistent, Multi-Level Memory: The hybridization of vector and graph representations circumvents the limitations of window-based memory and supports “unlimited” contextualization and cross-artifact association.
• Adaptive Graph-Guided Retrieval: Unlike fixed-hop or static retrieval, AGR optimally balances retrieval depth against computational cost and contextual relevance, expanding only as necessary to resolve dependency chains.
• Debugging-Centric Transformer Training: The transformer backbone is pre-trained and fine-tuned on real-world debugging sessions—not just on raw code or code-comment pairs. This enables cohesive outputs comprising code fixes, explanations, and auxiliary artifacts.
• Autonomous Orchestration Loop: Chronos manages the full life cycle of bug detection, fix proposal, execution, validation, and refinement, supporting continuous deployment and self-healing systems.
• Deep IDE/CI Integration: Embedding Chronos in development environments and pipelines allows metadata (test outcomes, code reviews) to be incorporated in its memory, facilitating holistic maintenance and rapid response to detected issues.

5. Applications in Software Engineering Practice

By enabling repository-scale comprehension and autonomous self-healing, Chronos impacts several operational areas:

• Repository-Scale Comprehension: Chronos supports program analysis and code navigation at the scale of millions of lines and hundreds of interdependent modules, facilitating large-scale maintenance and migration tasks.
• Automated Bug Localization and Repair: By bridging code, history, documentation, and external feedback signals, Chronos outperforms sequence-based approaches in localizing root causes and synthesizing valid repairs autonomously.
• Continuous Integration and Deployment Support: Its seamless interaction with test, review, and deployment pipelines enables automatic validation and reduces mean time to resolution (MTTR).
• Knowledge Accumulation: Chronos builds a persistent, evolving knowledge base of debugging patterns, historical resolutions, and development metadata, thus improving with extended operation and repository growth.

6. Comparative Evaluation and Broader Significance

Chronos sets a new standard in code intelligence by fundamentally altering how context is modeled, how cross-repository relations are established, and how debugging workflows are automated at scale. In empirical evaluations on the MRR benchmark and in real-world debugging cycles, Chronos achieves:

Model/Metric	Fix Accuracy (%)	Avg. Debug Cycles	Retrieval Precision (%)	Recall (%)
Chronos	67.3	2.2	89	84
GPT-4 + RAG	<10	>6.5	~	~
Gemini-1.5 + Graph	<10	>6.5	~	~

("~" indicates not explicitly reported.)

This positions Chronos as a benchmark for repository-wide, memory-driven code understanding and debugging, elevating automation, reliability, and productivity in contemporary software engineering.

7. Implications and Future Directions

Kodezi Chronos exemplifies a shift toward self-sustaining, continuously optimized software ecosystems. Its architecture, combining unlimited context retrieval and autonomous orchestration, offers a plausible pathway for future research in persistent code intelligence, large-scale software knowledge graphs, and fully automated maintenance agents. Its integration strategy suggests wide applicability across domains where knowledge, code, and metadata co-evolve rapidly and must be reasoned about holistically.

In sum, Chronos represents a technical advance in persistent, scalable, and autonomous code intelligence, with demonstrated improvements in debugging effectiveness, operational efficiency, and software reliability at repository scale (Khan et al., 14 Jul 2025).