Large Universe Model/Essays
Essays on the Large Universe Model
The arguments underneath the Large Universe Model, taken one at a time and at length.
Why retrieval is not continuityRetrieval fetches evidence when asked. A Large Universe Model is already holding it. The difference between pull and push is the difference between thWhy there is no fourth modelThe sequence Large Language Model, Large World Model, Large Universe Model is not open-ended. Each step widened intake, and after everything still hapWhat a belief costsHolding beliefs rather than generating answers has real costs: storage, provenance, calibration and the plasticity problem. An honest account of what Surprise is the signalThe most valuable output of a Large Universe Model is not its current estimate but the size of its last revision and the observation that caused it.From corpus to scene to streamThe Large Language Model took a corpus, the Large World Model took a scene, the Large Universe Model takes a stream. Each step changed what counts as The economics of noticing lateThe value of a Large Universe Model is the difference between noticing now and noticing at the next review. That quantity is measurable, and it is whaWhat Large Universe Models cannot doA Large Universe Model does not verify, does not predict, does not remove uncertainty and does not decide. The limits of continuous belief revision, sThe training cutoff as original sinAlmost every technique built on top of the Large Language Model between 2020 and 2026 exists to work around one property: the training cutoff. The LarDisagreement should not be averagedWhen two credible sources conflict, the conflict is the finding. A Large Universe Model that collapses disagreement to a mean destroys its most valuabDecay and the honesty of forgettingA belief nothing has confirmed for six months should not be as confident as it was. Deliberate decay is what separates a maintained belief from an accCalibration is the whole productA Large Universe Model that says 80% must be right 80% of the time. Without calibration, continuous belief revision is worse than a Large Language ModProvenance, or it did not happenA belief whose evidence cannot be reconstructed is an assertion. Provenance is what makes continuous belief revision deployable in domains that matterThe alert that nobody readsA system that reports every revision is useless. Deciding what deserves human attention is the hardest unsolved problem in continuous belief revision.Why the third generation looks differentThe Large Language Model and Large World Model are weights. The Large Universe Model is weights plus an explicit belief state — and that difference isThe companion problemA model that watches one life continuously is the most useful and most uncomfortable application of the Large Universe Model. Both halves deserve stat