NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
arXiv:2606.19279v1 Announce Type: new Abstract: Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules. NeSyCat, extending ULLER, subsumes them under a single inductive definition of truth, parametric in a strong monad and an aggregation structure on truth-values. NeSyCat has so far lacked an account of predicates and functions learned by neural networks. We provide NeSyCat Torch as the missing link and interpret computational symbols via neural networks, implementing the framework in probabilistic programming an
The development of NeSyCat Torch represents the ongoing effort to unify neurosymbolic AI, driven by the increasing need for more robust and interpretable AI systems.
This development offers a unified approach to neurosymbolic learning, potentially leading to more generalized and explainable AI that integrates diverse reasoning paradigms.
The fragmented landscape of neurosymbolic semantics could begin to converge under a single, unified inductive definition of truth, incorporating neural network learning.
- · AI researchers
- · Neurosymbolic AI developers
- · Probabilistic programming communities
- · Companies seeking interpretable AI
- · Fragmented AI approaches
It provides a concrete, differentiable tensor implementation for integrating neural network learning into categorical semantics.
This could accelerate the creation of AI systems that combine the strengths of neural networks with symbolic reasoning.
The unification may lead to new breakthroughs in AI safety and generalization by offering a more coherent theoretical and practical framework.
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Read at arXiv cs.AI