AI RESEARCH
Modal Logical Neural Networks for Financial AI
arXiv CS.LG
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ArXi:2603.12487v1 Announce Type: new The financial industry faces a critical dichotomy in AI adoption: deep learning often delivers strong empirical performance, while symbolic logic offers interpretability and rule adherence expected in regulated settings. We use Modal Logical Neural Networks (MLNNs) as a bridge between these worlds, integrating Kripke semantics into neural architectures to enable differentiable reasoning about necessity, possibility, time, and knowledge.