Exact inference in complex probabilistic models often incurs prohibitive computational costs. This challenge is particularly acute for autonomous agents in dynamic environments that require frequent, real-time belief updates. Existing methods are often inefficient for ongoing reasoning, as they re-evaluate the entire model upon any change, failing to exploit that real-world information streams have heterogeneous update rates. To address this, we approach the problem from a reactive, asynchronous, probabilistic reasoning perspective. We first introduce Resin (Reactive Signal Inference), a probabilistic programming language that merges probabilistic logic with reactive programming. Furthermore, to provide efficient and exact semantics for Resin, we propose Reactive Circuits (RCs). Formulated as a meta-structure over Algebraic Circuits and asynchronous data streams, RCs are time-dynamic Directed Acyclic Graphs that autonomously adapt themselves based on the volatility of input signals. In high-fidelity drone swarm simulations, our approach achieves several orders of magnitude of speedup over frequency-agnostic inference. We demonstrate that RCs' structural adaptations successfully capture environmental dynamics, significantly reducing latency and facilitating reactive real-time reasoning. By partitioning computations based on the estimated Frequency of Change in the asynchronous inputs, large inference tasks can be decomposed into individually memoized sub-problems. This ensures that only the specific components of a model affected by new information are re-evaluated, drastically reducing redundant computation in streaming contexts.

翻译：复杂概率模型中的精确推理通常会产生难以承受的计算成本。这一挑战对于动态环境中的自主智能体尤为严峻，因为它们需要频繁进行实时信念更新。现有方法在持续推理中往往效率低下，因为它们在发生任何变化时都会重新评估整个模型，未能利用现实世界信息流具有异构更新速率这一特性。为解决此问题，我们从反应式、异步、概率推理的视角处理该问题。我们首先介绍了Resin（反应式信号推断），这是一种将概率逻辑与反应式编程相融合的概率编程语言。此外，为给Resin提供高效且精确的语义，我们提出了反应式电路。RC被构建为代数电路与异步数据流之上的元结构，是一种时间动态有向无环图，能够根据输入信号的波动性自主调整自身结构。在高保真无人机集群仿真中，我们的方法相比频率无关推理实现了数个数量级的加速。我们证明RC的结构调整能成功捕捉环境动态，显著降低延迟并促进反应式实时推理。通过基于异步输入中估计的变化频率对计算进行划分，大型推理任务可被分解为独立记忆化的子问题。这确保了仅重新评估受新信息影响的模型特定组件，从而在流式场景中极大减少了冗余计算。