CADAQUES：用于高效查询自主发现的成本感知双架构
CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery
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中文总结 AI 辅助
研究自主发现系统，提出开源框架CADAQUES，基于成本是发现循环一级原语的原则，将循环分为预言机和驱动程序，按通用预算收费并记录成本差异，通过定位二维伊辛模型临界温度评估，效果良好且计量成本低。
中文摘要 AI 辅助
自主发现系统将回答查询的资源（模拟器、仪器或分析模型）与选择下一步查询内容的算法相结合。大多数此类循环的软件框架继承了数值优化的控制结构：活动运行固定次数的迭代，编程接口中没有查询成本，决策被视为免费。实际上，查询成本可能相差几个数量级，基于大语言模型或昂贵代理构建的规划器本身也会消耗资源。本文提出了CADAQUES，一个基于成本是发现循环的一级原语这一架构原则的开源Python框架。CADAQUES将循环分为两个结构协议，一个回答查询的预言机和一个提出查询的驱动程序，并根据跨越墙时间、CPU小时、货币成本和语言模型令牌的通用向量值预算对评估和决策收费。一个只追加的账本记录每次交易执行前声明的成本和之后结算的成本，使它们的差异成为活动的可观察属性。我们通过从有噪声的有限尺寸估计中定位二维伊辛模型的临界温度来评估该架构，与精确的热力学极限参考进行对比。在这种有噪声的设置下，围绕最佳观察结果集中的策略可能会被噪声诱导的峰值误导，而一种先用廉价的低保真查询进行探索，然后用高保真查询进行细化的策略，在研究的预算规模下，比始终使用高保真查询产生的误差更低且变化更小。计量每迭代增加几十微秒，比最便宜的预言机查询低三个数量级。该框架采用MIT许可并存档于Zenodo（doi：https://doi.org/10.5281/zenodo.21293589）。
英文摘要
Autonomous discovery systems couple a resource that answers queries (a simulator, instrument, or analytic model) to an algorithm that selects what to query next. Most software frameworks for this loop inherit the control structure of numerical optimization: campaigns run for a fixed number of iterations, query costs are absent from the programming interface, and decision-making is treated as free. In practice, queries may differ in cost by orders of magnitude, and planners built on large language models or expensive surrogates consume resources of their own. Here we present CADAQUES, an open-source Python framework built on one architectural principle: cost is a first-class primitive of the discovery loop. CADAQUES separates the loop into two structural protocols, an Oracle that answers queries and a Driver that proposes them, and charges both evaluations and decisions against a common vector-valued budget spanning wall time, CPU hours, monetary cost, and language model tokens. An append-only ledger records, for each transaction, the cost declared before execution and the cost settled afterwards, making their discrepancy an observable property of the campaign. We evaluate the architecture by locating the critical temperature of the two-dimensional Ising model from noisy finite-size estimates against the exact thermodynamic-limit reference. In this noisy setting, strategies that concentrate around the best observed result can be misled by noise-induced peaks, whereas a schedule that explores with cheap low-fidelity queries and refines with higher-fidelity ones yields lower and less variable errors than high fidelity throughout, at the studied budget scale. Metering adds tens of microseconds per iteration, three orders of magnitude below the cheapest oracle query. The framework is MIT-licensed and archived at Zenodo (doi:10.5281/zenodo.21293589).
