MIT 神经科学家用 Jev 做路由，推理账单降 71%
生产 agent 每天数百万请求，改用 Jev 先给信号打 0–1 分再决定推理深度，低信号走快路、噪声直接过滤，推理成本降 71%。
an MIT neuroscientist used JEV to cut a production agent's inference bill by 71% by copying how the human brain filters attention his system routes millions of requests a day and automatically picks the depth of reasoning each one deserves. his team spent almost a year building it with his neural-modeling lab. the first version sent every request to the same LLM at max effort. it worked, but running deep reasoning on trivial inputs stacked latency into seconds and pushed inference cost into five figures a month. then they rebuilt the routing layer around Jev. instead of letting the LLM decide how hard to think, Jev scores every incoming signal from 0.0 to 1.0 and picks the effort level before the model ever sees it. high signal gets deep reasoning, low signal gets a fast route at ~30% of the cost, noise gets dropped in the filter layer. I found the routing formula they used and it maps almost 1:1 onto how the cortex prioritizes about 20 signals per second and drops the rest. full pipeline breakdown in the article below ↓
这条是引用着另一条写的：「用 Jev 从零搭高频交易 bot：每区块一次买卖决策」