How Real Bugs Taught Chronos More Than Any Dataset
What we thought we were teaching the model, and what it ended up learning from us instead.
Kodezi Team
Jul 20, 2025
When we started training Chronos, we were confident we had the data. 15 million GitHub issues with associated fixes. 8 million stack traces mapped to successful PRs. 3 million CI/CD logs from failed builds and resolutions. Everything aligned, de-duped, filtered, tokenized. We had built what we believed was the most exhaustive corpus of debugging examples ever assembled.
If code was the input, and a clean fix was the output, then this should have been enough. But it wasn't. Debugging is not a transformation. It is a process. And you don't learn a process by observing its endpoints.
The First Real Test
The inflection point came from a bug so ordinary it should have been trivial. A user reported an intermittent auth failure. No stack trace. No crash. Just a silent logout after a successful sign-in.
We traced it to our session management module. The regression had been introduced during a recent refactor. Token validation logic was split across two files. One handled token timestamps. The other handled cache invalidation.
The condition only triggered when a user logged out and back in within a narrow window. Even our CI didn't catch it.
We fed Chronos the logs, the test failure, the recent commits, and the relevant files. It proposed a fix in ten seconds. It looked clean. It passed the test. It was completely wrong.
The patch adjusted a timeout threshold. The real problem was an inversion in state propagation between two modules that no longer shared context. The patch masked the symptom and buried the root cause.
What struck us wasn't that it failed. It was how confidently it failed. We weren't looking at a bug-fixing model. We were looking at a pattern-matching machine. And this bug had no pattern. Only sequence.
This is exactly why even today's best models like Claude 4 Opus achieve only 14.2% debugging success despite 72.5% on code generation. They pattern-match rather than reason causally.
What Synthetic Data Gets Wrong
Our dataset had all the right ingredients: structured pairs of buggy and fixed code, PRs annotated with timestamps, reviewers, and test logs, regression cases pulled from CI pipelines, failures linked to issue threads and user reports.
But synthetic bugs follow a dangerous set of assumptions:
The bug is close to the fix
The context is available in the same file
The model needs to complete a patch, not investigate
The test failure directly reveals the cause
In reality, none of that holds. Our analysis found that real debugging requires an average of 3-7 retrieval steps across multiple files, with bugs often located 3-5 hops away from the error location in the dependency graph. Bugs in production are distributed. They arise from interactions between systems, not violations inside them. They are asymmetric. Small signals, large causes. And they rarely introduce themselves with clarity.
Our data taught Chronos what a resolution looked like. It never taught it how to search for one.
Dogfooding Became the Only Reliable Benchmark
After the session bug, we turned Chronos loose on our own pipelines. Every flaky test. Every deployment error. Every CI fail labeled "investigating." Chronos got a copy. Not to fix, but to watch, propose, and fail.
We logged everything: what did it retrieve? What did it ignore? Did it reuse a bad strategy? Did it contradict its previous suggestion?
We expected failure. But something changed.
Chronos started skipping filename matches and reading control flow. It used shorter prompts but included deeper commits. It abandoned fixes faster when test failures returned. It began citing documentation it once ignored.
This behavioral shift is what led to Adaptive Graph-Guided Retrieval (AGR), which now achieves 92% precision at 85% recall by following causal paths rather than textual similarity.
What emerged wasn't a new patching strategy. It was a behavioral shift. Chronos wasn't just producing fixes. It was reacting to failure.
What Feedback Loops Changed That Fine-Tuning Couldn't
We introduced internal replay logs and a validation loop. For every Chronos-generated patch, the system received:
The raw test outcome
Any reviewer comments
The diff that eventually passed
The full trace of changes applied across the repo
Within weeks:
It stopped re-suggesting rejected fixes
It began tagging patches with fallback confidence
It adjusted its message tone to reflect prior reviewer language
It started offering rollback suggestions and postmortem notes
It even deleted unused test scaffolding after multiple fix cycles
This iterative learning became Persistent Debug Memory (PDM), which now maintains patterns from 15M+ debugging sessions with an 87% cache hit rate on recurring bugs.
None of this came from instruction. It came from accumulated failure.
When Benchmarks Stopped Mattering
Chronos never did particularly well on static benchmarks like SWE-bench, HumanEval, or CodeContests in early iterations. It was passable. But not dominant. And that became the point.
These benchmarks reward precision and completion. Chronos had been conditioned on adaptation and recovery. Standard tasks expect a correct answer in one pass. Real debugging demands evidence, revision, and patience.
This is why we developed the Multi Random Retrieval (MRR) benchmark with 5,000 real-world scenarios. On MRR, Chronos achieves 67.3% success while GPT-4.1 manages only 13.8%, despite GPT-4.1 scoring 91.2% on HumanEval.
We stopped optimizing for accuracy scores. We started measuring:
Patch success after multiple iterations (averaging 7.8 iterations for Chronos vs 1-2 for others)
Reuse of prior failure memory (47ms retrieval for cached patterns vs 3.2min cold start)
Total regression rate across PRs (94.6% of fixes avoid introducing new bugs)
Developer trust and merge latency (89% developer preference in human evaluation)
This wasn't just evaluation. It was operational grounding.
The Architecture That Emerged From Reality
What started as patches to a failing system became the foundation of Chronos's 7-layer architecture:
Multi-Source Input Layer: Born from needing to ingest logs, traces, and configs, not just code
Adaptive Graph-Guided Retrieval: Emerged from watching Chronos learn to follow control flow over filename matches
Debug-Tuned LLM Core: Trained on chain-of-cause reasoning after seeing pattern-matching fail
Orchestration Controller: Added when we realized debugging is inherently iterative
Persistent Debug Memory: Created to stop re-learning the same lessons
Execution Sandbox: Essential for the test-fail-refine loop
Explainability Layer: Required when developers needed to trust the reasoning
Each layer contributes 15-30% to overall performance. Remove any one, and debugging success drops catastrophically.
Debugging Changed Chronos. It Changed Us Too.
As Chronos evolved, we adjusted our expectations. We no longer asked for a perfect fix. We asked whether it understood the shape of the failure. We watched how it reasoned, not just what it generated. We looked at its path to recovery, not its first attempt.
The numbers tell the story: Chronos averages 2.2 fix cycles to success, reduces debugging time by 40%, and achieves 65% fewer iterations than competitors. But what matters more is that it learns. Each debugging session improves the next.
Chronos didn't become a debugger by ingesting the right answers. It became one by making mistakes in the right places, with memory, with feedback, with consequences.
No dataset taught it that. Only reality did.
Today, with a Cohen's d effect size of 3.87, Chronos represents not an incremental improvement but a paradigm shift in how AI systems approach debugging. It's the difference between a model that generates code and a system that understands failure.
Chronos-1 will be available Q4 2025, integrated into Kodezi OS Q1 2026.
