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Neuro-symbolic AI integrates CYK algorithm into neural networks

Researchers have developed a novel neuro-symbolic approach by integrating the CYK algorithm directly into a neural network architecture, named CYKNN. This method encodes the CYK algorithm within trainable matrix-vector operations. Experiments show that CYKNN outperforms larger LLMs in in-context learning and surpasses smaller, fine-tuned Qwen models on specific grammar parsing tasks. AI

IMPACT This approach could lead to more efficient and interpretable AI models by directly embedding algorithmic reasoning into neural networks.

RANK_REASON The cluster contains an academic paper detailing a new AI research methodology.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Fabio Massimo Zanzotto, Federico Ranaldi, Giorgio Satta ·

    Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm

    arXiv:2605.31421v1 Announce Type: cross Abstract: In this paper, we show the possibility of a direct injection of algorithms into neural network architecture. We focus on a complex algorithm, that is, Cocke-Youger-Kasami (CYK) for parsing context-free grammars in Chomsky Normal F…

  2. arXiv cs.AI TIER_1 English(EN) · Giorgio Satta ·

    Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm

    In this paper, we show the possibility of a direct injection of algorithms into neural network architecture. We focus on a complex algorithm, that is, Cocke-Youger-Kasami (CYK) for parsing context-free grammars in Chomsky Normal Form and we propose CYKNN, a simple recurrent neura…