The information wasn't removed: it was moved.

Whenever a human remembers something specific, they actually don't. Instead, they remember a few small details, and the patterns that organize them. Then, the brain hallucinates more details that fit the overall pattern of the story. This phenomenon is called "Reconstructive Memory", and is one reason why eyewitness testimony is unreliable.

An LLM is similar to memory: you feed its neurons a bunch of data that has meaning encoded into it, and it becomes a model for any patterns present in that data. When an LLM generates a continuation, it is continuing the patterns that it modeled; including the data it was trained on, and whatever prompt it was given.

Natural language solved! Right?

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Not so fast!

The human mind performs much more than memory reconstruction. How else would we encode meaning into the semantics of language, and write that data into text?

There is more to this story. There is more to... story.

Remember when I said the information is "moved"? Where did it go? More importantly, how can we use that data?

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Let's consider a human named Dave, reading a book about boats. What is Dave using to read it? The short version: empathy.

Story is held together with semantics. Semantics are already-known patterns that define logical relationships between words.

When Dave reads the statement, "Sue got in the boat", he interprets that information into meaning. Sue is a person, probably a woman. She entered the top of a vessel that was floating on water.

But Dave was wrong! Sue is a cat, and the boat was lying on dry beach.

Here's the interesting part: Dave was totally correct until I declared otherwise. His interpretation matched his internal worldview: all of the ambiguity present in what he read was resolved by his assumptions. Making false assumptions is a completely valid result of the process that we call "reading". It's a feature.

After Dave overheard me talking to you just now, he learned the truth of the story, and immediately resolved his mistake. In an instant, Dave took the semantic information he had just read, and he reread it with a completely different worldview. But where did he read it from?

His worldview. You see, after reading the book, its meaning was added neatly into his worldview. Because of this, he was prepared to interpret what I was telling you: about Sue being a cat and so on. Dave performed the same "reading" process on my new story, and he used the statement he read in the book to do just that.

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Worldview is context. Context is the tool that resolves ambiguity. We use this tool to interpret story, particularly when story is ambiguous.

So what is context made of? Story.

It's recursive. Everything that we read is added to our internal story. One giant convoluted mess of logic is carved into our neurons. We fold those logical constructs together into a small set of coherent ideas.

But where is the base case? What's the smallest part of a story? What are the axioms?

This is the part I struggled with the most: there isn't one. Somehow, we manage to perform these recursive algorithms from the middle. We read the story down the ladder of abstraction, as close to its roots as we can find; but we can only read the story as far as we have read it already.

We can navigate the logical structure of ideas without ever proving that logic to be sound. We can even navigate logic that is outright false! Constraining this behavior to proven logic has to be intentional. That's why we have a word for it: mathematics. Math is the special story: it's rooted in axioms, and built up exclusively using theorems.

Theorems are an optimization: they let us skip from one page of a story to another. They let us fold and compress logical structure into a something more practical.

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LLMs do not use logic at all. The logic of invalidation is missing. When story categorizes an idea into the realm of fiction, the LLM simply can't react accordingly. The LLM has no internal story: only memory.