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  "path": "/abs/2605.31421v1",
  "publishedAt": "2026-06-01T00:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Fabio Massimo Zanzotto",
    "Federico Ranaldi",
    "Giorgio Satta"
  ],
  "textContent": "**Authors:** Fabio Massimo Zanzotto, Federico Ranaldi, Giorgio Satta\n\nIn 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 neural network architecture for encoding the CYK algorithm in trainable matrix-vector multiplications.We experimented with a very simple grammar with 4 variations showing that our approach outperforms existing LLMs with more than 20B parameters with an in-context learning setting and smaller LLMs of the Qwen family fine-tuned with LoRA. Our attempt paves the way to a different approach to neuro-symbolic methodologies.",
  "title": "Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm"
}