Why We Named It Chronos

When we set out to build Chronos, we weren't just building another LLM. We were designing a new kind of intelligence: one that understands code not as static text, but as a living system shaped by time, history, and evolution. That realization changed everything, from how we architected memory and retrieval to how we thought about naming the model.

We didn't want something trendy. We wanted something true.

So we named it Chronos.

Debugging Is Not a Snapshot. It's a Timeline.

Traditional LLMs see code as it exists in the moment: a function, a class, a test failure. This is why Claude 4 Opus achieves 72.5% on code generation but only 14.2% on debugging, and GPT-4.1 reaches 54.6% on synthesis yet manages just 13.8% debugging success. But bugs don't live in the moment. They creep in during a feature refactor two months ago. They lie dormant behind a deprecated setting. They surface when an old test collides with a new constraint.

Our analysis of 5,000 real-world debugging scenarios found that bugs span an average temporal range of 3-12 months of commit history, requiring understanding of code evolution across time.

Debugging is fundamentally temporal.

Chronos treats it that way. It doesn't just ask what went wrong. It asks when. It traces root causes through commit histories, stack traces, prior bug reports, and regression logs. It assembles narratives, not just context windows.

Time as Architecture

Chronos isn't time-aware by accident. Time is embedded in every layer of the system:

Persistent Debug Memory (PDM) forms a long-term record of 15M+ debugging sessions, maintaining patterns with 87% cache hit rate on recurring bugs

Adaptive Graph-Guided Retrieval (AGR) tracks how code artifacts evolved over time, using temporal edge weights (0.7 × e^(-λt)) that decay based on recency

Temporal embeddings encode commit timestamps, file histories, and context relevance, enabling retrieval across 3-12 months of code evolution

Adaptive depth lets Chronos zoom into the past as needed, averaging 3.7 hops for complex bugs, 1.2 for simple ones

Chronos doesn't operate on prompts alone. It operates on a timeline.

Debugging as a Temporal Loop

Chronos runs a continuous debugging loop formalized in Algorithm 2:

1. Detect the issue (Multi-Source Input Layer)

2. Retrieve temporally relevant context (AGR with 92% precision)

3. Propose a fix (Debug-Tuned LLM Core)

4. Validate via tests and CI (Execution Sandbox)

5. Refine the patch (avg 2.2 cycles to success)

6. Update memory (PDM stores patterns for future use)

7. Repeat until resolution (avg 7.8 iterations for complex bugs)

Each loop isn't just an action. It's a tick in Chronos's internal clock. With every cycle, the model learns. PDM's temporal decay function (e^(-0.1t)) ensures recent fixes have higher weight while preserving historical patterns.

We didn't just want a model that responds. We wanted one that evolves over time.

Why Not Just "FixBot" or "DebugGPT"?

Because this isn't a chatbot. It's not a helper.

Chronos is the first system designed to serve as a debugging brain for codebases. It maintains persistent memory across sessions, unlike Cursor (session-only), Windsurf (session-only), or even LangGraph (static memory). It remembers bugs from six months ago. It knows how you fixed them. It watches how they reappear. It learns your codebase's past to safeguard its future.

The numbers prove this: 47ms retrieval for cached patterns versus 3.2min cold start, demonstrating true temporal learning.

It doesn't complete code. It stewards it.

The Greek God of Time

In Greek mythology, Chronos is the personification of linear time. Not cyclical seasons or fleeting moments, but time as steady progression, from beginning to end, from cause to consequence.

We chose that name because debugging is linear. Every bug is a chain of cause and effect. Every fix is a resolution to a story that started long ago.

This philosophy drives our approach: bugs are typically located 3-5 hops away in the dependency graph, connected through temporal causality rather than textual similarity.

Chronos, the model, isn't fast for the sake of speed. It is deliberate. Informed. Temporal. It averages 134.7 seconds per fix, taking more time than competitors but achieving 4-5x higher success rates. It doesn't just look forward. It remembers.

Tokens Are Not Time

A common myth in LLMs is that more tokens equals more context. Gemini 2.5 Pro offers 1M tokens, yet achieves only 13.9% debugging success. GPT-4.1 with 128K tokens manages 13.8%. But in debugging, tokens are not time.

You can fit a million tokens into a context window and still miss the commit that caused the regression. You can index every file and still fail to trace the variable that changed shape during a refactor.

Chronos doesn't chase token count. Through AGR, it retrieves only 31.2K tokens on average while achieving 67.3% debugging success. It chases temporal relevance. That's what makes it different.

Time as a Debugging Interface

The deeper we built Chronos, the clearer it became:

Time is not metadata. It is structure - AGR's 8 signal types include temporal weights on every edge

Debugging is not search. It is recall - PDM maintains 15M+ sessions of learned patterns

Context is not about width. It is about when - Temporal proximity beats textual similarity for debugging

Our evaluation confirms this: removing temporal weights from AGR causes performance to drop by 5.1%, while removing PDM's temporal decay reduces success by 3.7%.

Chronos isn't just a name. It is a philosophy. We didn't just want to understand code. We wanted to understand how it became what it is.

The Temporal Advantage in Numbers

The importance of time in debugging is quantified across our evaluation:

Without temporal understanding, Chronos would be just another code model achieving ~20% debugging success instead of 67.3%.

Final Thought

Chronos is the first of its kind: a debugging-native model built for persistent, repository-scale, memory-driven software maintenance. With a Cohen's d effect size of 3.87, it represents not an incremental improvement but a paradigm shift enabled by temporal understanding.

We named it after time because time defines everything it does: what it remembers, how it learns, and why it works.

Debugging isn't just about what happened. It is about when.

And that's why we built Chronos.