Inside a neural network, memorizing facts and reasoning through problems are completely separate jobs — learned from different data.

This explores why the parts of a network that store facts seem to be distinct from the parts that reason, and the corpus offers a surprisingly consistent picture: the two rely on different kinds of learned structure, so they end up living in different places. The clearest evidence comes from tracing what pretraining documents actually drive each behavior. When researchers looked across five million pretraining documents, factual recall turned out to depend on narrow, document-specific memorization — the model essentially points back to the one place it saw the fact — while reasoning drew on broad, transferable *procedural* knowledge spread across many diverse sources Does procedural knowledge drive reasoning more than factual retrieval?. Storage is lookup; reasoning is a reusable routine. Those are different jobs, learned from different data signatures, so it makes sense they don't share the same circuitry.

You can watch the seam directly in how models handle logic. Syllogistic reasoning runs through a content-independent three-stage circuit — recite, suppress the middle term, mediate — that works the same across architectures. But separate attention heads carrying world knowledge sit alongside it and bias the conclusion toward what's *plausible* rather than what's logically valid How do language models perform syllogistic reasoning internally?. That's the separation made visible: a reasoning engine and a knowledge store operating as distinct components, sometimes even fighting each other. Notably, the knowledge contamination gets *worse* at larger scale, which suggests the two systems don't merge as models grow — they stay separate and the stored-knowledge pull gets stronger.

There's a deeper reason this modularity shows up at all. Networks appear to naturally decompose compositional tasks into isolated subnetworks — pruning experiments show you can ablate one subroutine and only its function breaks, and pretraining sharpens this modular structure Do neural networks naturally learn modular compositional structure?. So separation isn't something engineers impose; it's a structure gradient descent tends to find on its own. It also explains why a 7-million-parameter network that just recurses on its own latent reasoning state can out-generalize billion-parameter LLMs on hard puzzles — the reasoning gains come from the recursive *process*, not from a giant pile of stored knowledge Can tiny recursive networks outperform massive language models?. Reasoning and storage scale differently because they're doing different things.

The most useful reframing in the corpus maps this onto the brain. Transformer weights behave like a distributed neocortex holding consolidated, slow-to-change knowledge; retrieval systems act like the hippocampus doing fast new encoding; and agentic state plays the prefrontal role of executive control Can brain memory systems explain how LLMs should store knowledge?. Under that lens the separation isn't a quirk — it's the same division of labor evolution landed on, because consolidated knowledge and active manipulation have opposing requirements (stable vs. fast, general vs. situation-specific).

Here's the twist worth leaving with: the separation is real but leaky, and the leak is a liability. When semantic content is stripped from a reasoning task, LLM performance collapses even when the correct rules are handed to them in context — meaning much of what looks like clean reasoning is actually stored semantic association doing the work Do large language models reason symbolically or semantically?. So knowledge and reasoning are separable enough to have distinct circuits, but entangled enough that the model leans on memorized meaning whenever the pure-reasoning path gets hard. The open frontier — which the brain-memory framing names explicitly — is the *consolidation* mechanism that would let the two integrate cleanly instead of interfering.