Publikation
Reactive Knowledge Representation and Asynchronous Reasoning
Simon Kohaut; Benedict Flade; Julian Eggert; Kristian Kersting; Devendra Singh Dhami
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2602.05625, Pages 1-18, arXiv, 2026.
Zusammenfassung
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.