用扩散模型建模符号间依赖，提升神经符号系统的推理准确性和可信度。
Neurosymbolic Diffusion Models
- 通过离散扩散过程捕捉符号间的依赖关系，突破传统独立假设限制。
- 在视觉路径规划与自动驾驶等任务中达到当前最佳精度，且预测更可信。
- 适合需要高可靠性推理的复杂决策场景，如自动驾驶与机器人导航。
神经符号（NeSy）预测器结合神经感知与符号推理，用于视觉推理等任务。但标准方法假设提取符号间条件独立，限制了对交互和不确定性的建模，常导致过度自信预测及分布外泛化能力差。为此，我们提出神经符号扩散模型（NeSyDM），利用离散扩散建模符号间依赖。该方法在扩散每一步仍保留符号独立性假设，实现可扩展学习的同时，捕捉符号依赖并量化不确定性。在合成与真实世界基准上，包括高维视觉路径规划与基于规则的自动驾驶任务中，NeSyDM在所有NeSy预测器中表现最优，且具有良好的校准性。
原文摘要 · Abstract (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.
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