Hypernetworks

A system breaks after a rule changes. The recommendation model suddenly faces a new product catalog. The warehouse routing policy meets a new constraint. A trading bot trained in one market regime walks into another and immediately discovers that yesterday’s “smart behavior” is today’s elegant way to lose money. The usual engineering instinct is to retrain, retune, or ask a human to adjust the knobs. Very modern. Very expensive. Very Tuesday. ...

TL;DR for operators Large language models age badly. Product names change, policies expire, executives move, medical or legal guidance becomes stale, and some facts in pre-training were never right in the first place. The usual repair options are clumsy: retrain the model, fine-tune it, hide updated facts in prompts, or bolt on retrieval and hope the model behaves. All useful. All annoying in different ways. ...