Why Cache Reasoning as Code?

The Case for Crystallizing Inference into Procedures

An AI That Thinks from Scratch Every Time

Imagine you are teaching a junior colleague how to create a pivot table in a spreadsheet.

Day one, they ask. You spend thirty minutes explaining. Day two, the same colleague asks the same question. You spend another thirty minutes. Day three, day four – the same thing.

This is exactly how today’s LLMs operate.

Ask GPT to “parse a CSV in Python,” and the model mobilizes billions of parameters to reason from scratch. Ask the same question tomorrow, or the day after, and it pays the same cost every time. Yesterday’s reasoning evaporates. It is not recorded, not reused, not accumulated.

This is a web server running without a cache. A student solving the same exam problem repeatedly without taking notes. And intelligence that does not accumulate experience can never grow.

The LLM Is a Compiler, Not a Runtime Engine

SEGLAM offers a fundamentally different answer to this problem.

The LLM is not a runtime engine that executes every request – it is a compiler that crystallizes reasoning into code.

Here is how it works:

1. When a request arrives, check the reasoning cache first.
2. Cache Hit: An identical or similar reasoning process has already been crystallized into code. The LLM is not invoked. The corresponding code is executed immediately. Fast, cheap, and deterministic.
3. Cache Miss: This is a previously unseen type of reasoning. Now the LLM is invoked. But the LLM does not generate “an answer” – it generates “code that produces the answer.” This code is added to the cache.

When a similar request comes next time? Cache hit. The LLM can stay asleep.

The Analogy to JIT Compilation

This architecture is a rediscovery of a pattern already proven in computer science.

Consider the JIT (Just-In-Time) compiler. Java and JavaScript engines initially execute code line by line through an interpreter. Slow, but functional. When the same code path is executed repeatedly – “this is a hot path” – the engine compiles that path into native machine code. From then on, it runs directly without going through the interpreter.

In SEGLAM:

• Interpreter = LLM. Slow, expensive, and probabilistic, but capable of handling any request.
• Native code = cached reasoning code. Fast, cheap, and deterministic.
• JIT compilation = the process of the LLM generating code on a cache miss. Costly, but only needs to happen once.

Just as a JIT compiler optimizes “hot paths,” SEGLAM crystallizes “hot reasoning” into code.

Why Cache “Code” Instead of “Answers”?

This is the crux. A simple response cache and SEGLAM’s reasoning cache are fundamentally different.

A response cache stores “Q: What is the capital of Korea? -> A: Seoul.” It only hits when the question matches exactly. Ask “What is the capital of the Republic of Korea?” and it misses. This is a dictionary, not intelligence.

SEGLAM’s reasoning cache stores code that says “for this type of question, construct an answer through this procedure.” It crystallizes not the specific value, but the reasoning path itself. Therefore, even when the input changes, the same type of question still hits. This is understanding. This is growth.

An analogy: a response cache memorizes the multiplication table; a reasoning cache learns how to multiply.

What Happens Over Time

The most powerful characteristic of this design is that time is on its side.

• Day 1: The cache is empty. Almost every request is a cache miss. The LLM works hard. Slow and expensive.
• Day 30: A significant portion of routine reasoning patterns are cached. LLM invocations decrease.
• Day 365: Most requests are cache hits. The LLM is invoked only for genuinely novel types of problems. The system is fast, cheap, and predictable.
• Beyond: The cache itself becomes “crystallized intelligence” for its domain. Portable, verifiable, and cumulable intellectual assets.

Dependence on the LLM decreases over time. System efficiency increases over time. This curve never reverses.

The Principle of Reasoning Preservation

The most fundamental principle of this approach is:

“The reasoning process of an AI must not be discarded – it must be recorded.”

The reasoning cache is the most direct implementation of this philosophy.

Reasoning that an LLM performs once is crystallized into a structured representation and stored. It is not discarded. It is reused. Verified. Improved. Accumulated.

And because this cached code is described in a clear, structured language:

• You can trace why a given procedure was created,
• You can correct a procedure when it turns out to be wrong,
• You can replace it when a better procedure is discovered.

Not reasoning that evaporates inside a black box with every call, but intelligence that accumulates on a white box. That is the vision of AI worth pursuing.

Summary

Calling the LLM for every request is like taking a plane to the house next door. Once you pave a road, you can walk from then on.

SEGLAM is the system that paves roads.