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原文摘要

Bayesian experimental design (BED) is a principled framework for intelligent data acquisition with applications across a wide range of scientific and industrial domains. However, the development and adoption of BED remains hindered by a lack of standardised software and benchmarks. Existing methods are typically implemented in bespoke codebases and evaluated on a small subset of tasks, making methods difficult to apply, compare, or extend. To address this, we present TestBED, a general-purpose, open-source toolkit for Bayesian experimental design. TestBED decomposes the BED process into modular, reusable components spanning tasks, objectives, training mechanisms, policy architectures, and evaluation procedures, together with scalable implementations of core primitives such as expected information gain estimators. We provide a wide range of these components, which can be freely composed, allowing existing methods to be reproduced within a common framework, while also enabling previously unexplored configurations and new methodologies to be easily implemented and evaluated. Using TestBED, we further conduct a large-scale benchmark of contemporary BED methods, including controlled studies of under-appreciated but surprisingly critical methodological choices, such as the importance of time embeddings in design policy architectures. By providing a modular, extensible software foundation and a reproducible benchmarking suite, we hope that TestBED will accelerate methodological research in BED and lower the barrier to its real-world application.