DAIR.AI推文
谷歌DeepMind新论文：将模型路由形式化为潘多拉魔盒问题
谷歌DeepMind提出潘多拉路由（Pandora's Router），将模型路由问题形式化为潘多拉魔盒问题，即检查成本高昂时的最优搜索问题。在高斯信号模型下，策略具有闭式解，可针对每个专家和输入判断细化估计是否值得。在多LLM基准、检索增强专家和可变推理时LLM上，该方法在匹配穷举估计质量的同时大幅减少昂贵估计器的调用次数。
译文
谷歌DeepMind的这篇论文堪称重磅，聚焦于当下极热门的模型路由话题。路由机制通常假设价值评估是免费的，但确定哪个专家模型最适合处理某个查询同样需要成本，而且这往往成为整个流程中开销更大的部分。这项新研究将模型路由形式化为“潘多拉魔盒”问题——即当检查成本高昂时如何最优搜索的经典难题。在高斯信号模型下，策略能以闭式解形式呈现，针对每个专家和每个输入，明确告诉你进一步细化评估是否值得付出代价。在多LLM基准测试、检索增强型专家以及推理时推理量可变的LLM中，潘多拉路由器的评估质量与穷举式评估相当，但调用昂贵评估器的频率却大幅降低。当竞争性评估存在噪声时，信息价值推理会以牺牲其他方利益为代价，提升策略性专家的效用。论文链接：https://arxiv.org/abs/2608.20316 在我们的学院中追踪更多热门AI论文：https://academy.dair.ai/
Banger paper from Google DeepMind. It's on the very hot topic of model routing. Routers assume the value estimate is free. Working out which specialist handles a query best costs money too, and it is often the larger cost in the pipeline. This new work formalizes model routing as Pandora's Box, the classic problem of optimal search when inspection is expensive. Under a Gaussian signal model the policies come out in closed form, telling you per specialist and per input whether refining the estimate is worth the price. Across a multi-LLM benchmark, retrieval-augmented specialists, and LLMs with variable inference-time reasoning, Pandora's Router matches exhaustive estimation quality while calling the expensive estimator far less often. When competing estimates are noisy, value-of-information reasoning raises the strategic specialist's utility at everyone else's expense. Paper: https://arxiv.org/abs/2608.20316 Track more trending AI papers in our academy: https://academy.dair.ai/
