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Neurosymbolic AI and its Taxonomy: a survey

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arxiv 2305.08876 v2 pith:OR7TCWI6 submitted 2023-05-12 cs.NE cs.AIcs.LG

Wandemberg Gibaut , Leonardo Pereira , Fabio Grassiotto , Alexandre Osorio , Eder Gadioli , Amparo Munoz , Sildolfo Gomes , Claudio dos Santos This is my paper · ORCID
classification cs.NEcs.AIcs.LG
keywords modelsareaneurosymbolicsurveyalternativeapplicationsartificialbrings
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Neurosymbolic AI deals with models that combine symbolic processing, like classic AI, and neural networks, as it's a very established area. These models are emerging as an effort toward Artificial General Intelligence (AGI) by both exploring an alternative to just increasing datasets' and models' sizes and combining Learning over the data distribution, Reasoning on prior and learned knowledge, and by symbiotically using them. This survey investigates research papers in this area during recent years and brings classification and comparison between the presented models as well as applications.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs

    cs.AI 2025-08 reject novelty 3.0 of 10

    The paper claims a symbolic orchestration layer, CoreThink, achieves state-of-the-art results on seven coding and reasoning benchmarks with no training, but provides no verifiable implementation or method details.

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