论文：把 Jev 决策模型接入 agent harness
论文提出 JevAgentHarness，把常规选择交给蒸馏的 4B 决策模型，按置信阈值决定是否交回 LLM，工具级联成本降 14.2% 且 500 个任务结果不变。
Must-read paper integrating decision models like Jev into harnesses. They distilled an open 4B decision model, mostly from Jev's answers on the agent's own states, that beats Jev inside that agent and is much cheaper to run. Their harness, JevAgentHarness, sends each routine choice to the decision model and uses its answer only when the confidence clears a threshold fitted on the agent's own trajectories. Below the threshold, the LLM decides as usual. With the fitted threshold, tool and argument cascades cut cost by 14.2% without changing the outcome of any of 500 tasks. A fixed threshold of 0.5 lost 21.5 points. They also train AgentDecision-4B on states the agent reaches after its own mistakes, with Jev's answers as labels. On tasks unlike its training data, the agent solved 91.5% with it and 82.0% with Jev. On familiar tasks, 4B models trained this way made agents 81.1 to 89.0% cheaper and up to 2.5 times faster while staying within 3 points of success. Paper: https://academy.dair.ai/papers/jevagentharness-benchmarking-deploying-and-training-decision-models-that-share-c-2610.21412