Scaling Neuro-symbolic Problem Solving: Solver-Free Learning of Constraints and Objectives
Thomas Schiex (thomas.schiex@inrae.fr)
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Abstract
Background: In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimisation problems from natural inputs, a task that Large Language Models seem to struggle with.
Objectives: We introduce a differentiable neuro-symbolic architecture and a loss function dedicated to learning how to solve NP-hard reasoning problems.
Methods: Our new...