神經符號擴散模型

Neurosymbolic Diffusion Models

May 19, 2025

作者: Emile van Krieken, Pasquale Minervini, Edoardo Ponti, Antonio Vergari

cs.AI

摘要

神經符號（NeSy）預測器結合了神經感知與符號推理，以解決如視覺推理等任務。然而，標準的NeSy預測器假設其提取的符號之間條件獨立，這限制了它們建模交互和不確定性的能力，常常導致過度自信的預測和較差的分布外泛化性能。為克服獨立性假設的局限，我們引入了神經符號擴散模型（NeSyDMs），這是一類新的NeSy預測器，利用離散擴散來建模符號間的依賴關係。我們的方法在擴散過程的每一步重用了NeSy預測器的獨立性假設，從而實現了可擴展的學習，同時捕捉了符號依賴性和不確定性量化。在包括高維視覺路徑規劃和基於規則的自動駕駛在內的合成與現實世界基準測試中，NeSyDMs在NeSy預測器中達到了最先進的準確度，並展現出強大的校準能力。

English

Neurosymbolic (NeSy) predictors combine neural perception with symbolic reasoning to solve tasks like visual reasoning. However, standard NeSy predictors assume conditional independence between the symbols they extract, thus limiting their ability to model interactions and uncertainty - often leading to overconfident predictions and poor out-of-distribution generalisation. To overcome the limitations of the independence assumption, we introduce neurosymbolic diffusion models (NeSyDMs), a new class of NeSy predictors that use discrete diffusion to model dependencies between symbols. Our approach reuses the independence assumption from NeSy predictors at each step of the diffusion process, enabling scalable learning while capturing symbol dependencies and uncertainty quantification. Across both synthetic and real-world benchmarks - including high-dimensional visual path planning and rule-based autonomous driving - NeSyDMs achieve state-of-the-art accuracy among NeSy predictors and demonstrate strong calibration.