there’s a certain rightness to the idea of a hippocampus for the models. a kind of chain-of-thought cache, built around the intuition that an intelligence ought to be changed by the work it does, even while its weights stay frozen. i keep thinking about the dot product between problems. metaphorically. two questions can share little vocabulary and still contain the same obstruction. different answers, but the same useful decomposition, the same assumption worth checking first. where the structure aligns, some fraction of the discovery cost ought to be recoverable. we wring beautiful deductions out of these intelligence engines and leave them in the sediment of a transcript. the answer survives. the expensive little maneuver that made it possible often remains unextracted. an engineer comes away from a difficult failure with an acquired suspicion. something gets checked earlier next time. expertise lives partly in this altered order of operations. i’d like the models to have somewhere for that alteration to persist. this is above prompt caching. something closer to memoizing how a problem became tractable. a decomposition, a diagnostic procedure, a failed approach with the reason it failed still attached. amortize the discovery, even when the answer must be recomputed. a transcript is a laboratory notebook, not a protocol. extracting the protocol requires deciding what was necessary, what was incidental, and what can be reproduced elsewhere. resemblance doesn’t authorize reuse. the reusable object should be somewhat lemma-like, carrying its assumptions wherever it travels. usually we won’t have a proof. we can still preserve tests, counterexamples, and the distinction between what worked once and what has been independently verified. a successful answer doesn’t certify every step that accompanied it. i used to think an AI hippocampus would mostly remember facts. now i’m interested in experience becoming procedure without first becoming a weight update. episodes, procedures, strategies, with a return path to the evidence whenever an abstraction becomes suspect. acquired competence outside the parameters. this is why i’m increasingly blackpilled on finetuning as the thing to build everything around. my bet is that successive general models absorb enough of today’s narrow specialization that i’d rather build the apparatus they inherit. tools, procedures, reasoning memory that survive a change of model. there’s a loose von neumann instinct here: an intelligence engine drawing on memory that holds both information and instructions. treat the model as an interchangeable cpu, with a hierarchy of reasoning caches backed by durable memory. useful procedures close at hand, the episodes behind them still addressable. let the surrounding architecture carry the burden of remembering, rather than requiring the engine to internalize every new experience. the architectural attraction is giving the engine and its accumulated experience separate lifecycles. some procedures will need rechecking. others will turn out to be workarounds for limitations the new model no longer has. but an upgrade shouldn’t require cold-starting the apprenticeship. does the first useful version look like reasoning traces in postgres, with retrieval, synthesis, and verification on top? the database isn’t the part i’m uncertain about. it’s how much of the work we can turn into a reusable method, and how cheaply we can establish that it applies. surely even a very marginal savings of a few percent tokens would gradually compound over time? isn't this the shape of continual learning?