Ask ten questions, read the text once: making a decision model 6.7x faster

A lot of "AI" in production isn't generation at all. It's a pile of small decisions about a piece of text: which team should handle this ticket? how urgent is it? is the customer threatening to leave? is this e-mail phishing? Sending each of those to an LLM and parsing the answer is slow, expensive and occasionally creative in ways you didn't ask for. Laya is an open model for exactly this job: you send a state (a ticket, an e-mail, a JSON document) and typed questions — choice, score, or a yes/no noul — and a ModernBERT encoder returns calibrated probabilities in one forward pass. I built cbjev on top of it. This post is about the one idea that made it fast, how it could reuse Laya's weights, and what didn't work. The problem: the document is read once per question Laya builds one sequence per question: [CLS] question 1 [SEP] options [SEP] document [SEP] [CLS] question 2 [SEP] options [SEP] document [SEP] ... Ask ten questions about a 500-token document and