Jev 如何把 Agent 循环成本压到地板
拆解 Jev 的 System One 决策模式：状态加问题直接返回带概率的类型化答案，两轮循环里六个判断合计只要 0.06 秒。
TYPESAFE RELEASED A MODEL THAT DOESN'T GENERATE TEXT, AND IT BREAKS THE COST FLOOR OF THE AGENT LOOP jev is what they call a system one model. you send it a state and a set of questions, it returns typed answers with probabilities. no tokens, no sampling, no sequential decoding. route → decide → guard → run tool → observe → done? they report 20-200x faster inference and 40-400x lower cost than comparable llms on classification tasks. every question in a request is evaluated in parallel, so adding questions barely moves latency. in a two-turn loop that means eight decisions. the two that need a plan still go to a chat model. the other six - pick a model, screen a tool call, check if the task is done - cost 0.06s combined. the takeaway is simple: stop paying generation prices for choices. most of what an agent decides is a pick, a score or a yes/no.
这条是引用着另一条写的：x.com/sydneyrunkle/status/2100754364545761643