Kodezi Chronos-1: AI Debugging & Forecasting

Updated 15 December 2025

Kodezi Chronos-1 is a family of Transformer-based models designed for autonomous multi-file debugging and zero-shot load forecasting, integrating rich context retrieval mechanisms.
Its architecture features Adaptive Graph-Guided Retrieval, Persistent Debug Memory, and a fix–test–refine loop that enhance debugging efficiency and accuracy.
Empirical benchmarks show Chronos-1 reduces debugging iterations by 65% and time by 40%, while delivering significant improvements in load forecasting performance.

Kodezi Chronos-1 is a family of large-scale, Transformer-based models developed by Kodezi with specialized variants targeting code debugging at repository scale and zero-shot time-series forecasting. Designed and benchmarked distinctly for their respective problem domains, Chronos-1 systems share a unifying principle of leveraging large-scale pre-training, rich semantic retrieval pipelines, and output-centric architectures to solve complex, context-rich tasks that are unsolved or inefficiently handled by general-purpose LLMs. This article focuses primarily on Kodezi Chronos-1 for autonomous code debugging, while also summarizing the architecture and empirical properties of Chronos-1 as used for zero-shot load forecasting.

1. Architectural Overview

Chronos-1 is the first LLM architected specifically for autonomous, multi-file debugging at repository scale, as opposed to single-file code completion or shallow context reasoning tasks. The core model is trained not only on next-token prediction but also on over 15 million entire debugging sessions encompassing fixes, test failures, CI logs, and developer patterns, enabling the acquisition of global bug-fixing behavior, long-range code dependencies, and temporal error/fix correlations.

Key architectural innovations include:

Adaptive Graph-Guided Retrieval (AGR): A multi-hop graph-based contextualization pipeline enabling information retrieval across codebases exceeding 10 million lines, using a hybrid scoring mechanism for candidate node selection.
Persistent Debug Memory (PDM): A cross-session, continuously updated memory store indexing AST-embedded code, semantic context vectors, temporal event tags, and bug/fix pattern associations for fast retrieval and learning across similar error instances.
Autonomous Fix–Test–Refine Loop: A seven-layer debug architecture incorporating real test execution feedback, iterative context expansion, fix validation, and explainable output generation.
Output-heavy, Multi-modal Prompt Construction: Recognizes that debugging requires large, richly structured context windows composed of multi-file patches, explanations, and test code, departing from large-but-shallow single-context paradigms.

The zero-shot load forecasting variant of Chronos-1 re-purposes a T5-style encoder-decoder stack for time-series prediction, applying discrete quantization, learned condition tokens, and large-scale multi-domain pre-training (Liao et al., 18 Nov 2024).

2. Adaptive Graph-Guided Retrieval (AGR)

AGR provides repository-scale context navigation by parsing software into a directed graph $G=(V, E)$ with typed nodes (functions, classes, commits, tests) and edges (imports, calls, temporal co-changes). Given a bug report, AGR identifies semantically relevant seed nodes and executes adaptive k-hop expansion:

Candidate Scoring: Each node $d \in N_k$ receives a hybrid score:

$s(q,d) = 0.4 \cdot \mathrm{cosine}(q,d) + 0.3 \cdot e^{-\lambda \cdot \mathrm{age}(d)} + 0.2 \cdot \mathrm{structSim}(q,d) + 0.1 \cdot \mathrm{patternSim}(q,d)$

Context Assembly: Iteratively expands until model confidence $C(\mathrm{Ctx},q) \geq \tau$ or a maximum hop count is reached, forming a context from 5–50 selected nodes.

Formal metrics on a 5,000-scenario benchmark indicate Precision@10 of 89.2% and Recall@10 of 84.7%, supporting a fix accuracy exceeding 67% (compared to 13–17% for standard retrieval-augmented generation) (Khan et al., 14 Jul 2025).

3. Persistent Debug Memory (PDM)

PDM indexes the following data types:

Source-level artifacts (ASTs, semantic embeddings)
Historical bug-fix pairs, stack traces, and PR comments
Test outcomes and CI events

Each node $v \in V$ is annotated with:

AST-based structural embedding
Semantic vector (dimension 768)
Temporal tag
Failure/fix pattern associations

Retrieval from PDM combines recency, semantic, and structural scores: $\mathrm{Score}_{\mathrm{PDM}}(v,q) = \alpha \cdot \mathrm{cos}(q,v) + \beta \cdot e^{-\lambda \cdot \Delta t} + \gamma \cdot \frac{\mathrm{in{-}deg}(v)}{\max \mathrm{Deg}} + \delta \cdot \mathrm{patternFreq}(v)$ with coefficients $(\alpha=0.4, \beta=0.3, \gamma=0.2, \delta=0.1, \lambda=0.1)$ . Updates are triggered in real-time by Git/CI events (<100 ms/file; <1 min for CI failures) with weekly re-embedding and a 30-day default retention window.

4. Seven-Layer Fix–Test–Refine Architecture

Chronos-1’s debugging pipeline comprises the following layers:

Multi-Source Input Layer: Accepts heterogeneous input such as stack traces, error logs, and test results.
Adaptive Retrieval Engine (AGR): Context construction via graph traversal.
Debug-Tuned LLM Core: Proposes fixes conditioned on retrieved context.
Orchestration Controller: Automates iterative fix execution and error analysis.
Persistent Debug Memory: Supplies historical context and pattern associations.
Execution Sandbox: Runs patches against tests in an emulated CI environment.
Explainability Layer: Produces PR summaries, root-cause explanations, and interaction graphs.

The core loop follows the formal algorithm:

Require Bug %%%%5%%%%, Codebase %%%%6%%%%, Tests %%%%7%%%%, Memory %%%%8%%%%
Ensure Valid fix %%%%9%%%% or failure
1. %%%%10%%%% AGR.retrieve%%%%11%%%%
2. %%%%12%%%%
3. while %%%%13%%%%
    a. %%%%14%%%% LLM.proposeFix%%%%15%%%%
    b. %%%%16%%%% Sandbox.run%%%%17%%%%
    c. if %%%%18%%%% and not %%%%19%%%%
        i. %%%%20%%%%
        ii. return %%%%21%%%%
    d. %%%%22%%%%
    e. %%%%23%%%%
4. return FAIL

On aggregate, Chronos-1 requires 2.2 iterations to reach a valid fix, compared to 4.8 for the closest agentic frameworks. Average bug resolution time is 42.3 minutes versus ~70 minutes for GPT-4.1+RAG pipelines (Khan et al., 14 Jul 2025).

5. Empirical Performance and Benchmarking

Chronos-1’s debugging performance has been validated on 5,000 real-world scenarios and standard benchmarks:

System	Fix Accuracy (%)	SWE-bench Lite (%)	Avg Time/Bug (min)
Chronos-1	67.3 ± 2.1	80.33 (241/300)	42.3
Claude 4.1 Opus	14.2 ± 1.3	–	~70
GPT-4.1	13.8 ± 1.2	–	~70
ExpeRepair + Claude 4.5 Sonnet	–	60.33	–
Refact.ai Agent	–	60.00	–
SWE-agent + Claude 4 Sonnet	–	56.67	–

Repository-specific success rates on SWE-bench Lite demonstrate maximal effectiveness on Sympy (96.1%), Django (90.4%), and Sphinx (93.8%).

Chronos-1 achieves a 40% reduction in debugging time and 65% fewer iterations versus competing methods, resolving complex bugs that require integrating multi-file and temporal context (Khan et al., 14 Jul 2025).

6. Zero-Shot Load Forecasting with Chronos-1

In time-series forecasting, Chronos-1 applies an encoder-decoder Transformer architecture (T5 base), employing quantized token embedding, horizon/quantile prompt tokens, and cross-domain pre-training over 84 billion points (Liao et al., 18 Nov 2024).

Zero-shot adaptation entails tokenizing the input history, prepending the forecast horizon and quantile, and decoding the next $h$ tokens—all without any dataset-specific fine-tuning:

Scale and quantize historical load values.
Stack condition tokens (horizon $h$ , quantile $\alpha$ ).
Perform auto-regressive decoding for the next $h$ tokens.
Dequantize to obtain continuous-valued output.

On multiple real-world datasets (UT Austin, Midea, Nongfu), Chronos-1 achieves up to 84% RMSE reduction (e.g., at 1 hour ahead RMSE = 0.79 MW, compared to baseline minima of 0.85–5.05 MW) and substantial improvements in CRPS and quantile scores, especially in data-scarce conditions. The improvements are statistically significant (Diebold–Mariano $|DM|\gg 1.96$ , $p<0.01$ ) (Liao et al., 18 Nov 2024).

7. Limitations and Future Directions

Documented failure cases include:

Low success (23.4%) on hardware-dependent bugs.
Reduced accuracy (41.2%) on dynamic language issues (Python, Ruby, JS).
Difficulty with distributed-system race conditions (31.2%).

Planned enhancements include sub-quadratic retrieval for ultra-large codebases, neuro-symbolic reasoning (symbolic execution, theorem proving), integration of visual debugging, and privacy-preserving federated PDM. Public API access is planned for Q1 2026 and an embedded version in Kodezi OS for Q4 2025.

For load forecasting, limitations include the computational cost of pre-training, support only for univariate input, and discretization errors from quantization. Extensions under consideration involve few-shot fine-tuning and the inclusion of exogenous variables (Liao et al., 18 Nov 2024).

Chronos-1 represents the state-of-the-art in automated debugging and generalizes robustly to time-series forecasting tasks, providing a unified LLM-based approach for scenarios where domain context, temporal reasoning, and extensive execution feedback are essential (Khan et al., 14 Jul 2025, Liao et al., 18 Nov 2024).