MIT neuroscientist uses Jev to route agent requests and cuts inference bill 71%
A production agent handling millions of requests a day was rebuilt around Jev, which scores each signal from 0 to 1 and picks reasoning depth before the model sees it, sending low-signal requests to a fast route and filtering noise, cutting inference cost by 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 ↓
This post quotes another post: “Building an HFT trading bot with Jev: one buy/sell decision per block”