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arXiv Computation and Language By Mochamad Rizal Fauzan, Naila Nursyifa, Irgi Surya

Halal-NeSyNet: Clause-Scoped Neuro-Symbolic Cross-Attention for Automated Food Ingredient Auditing and Risk-Aware Halal Logistics Triage

Read the original on arXiv Computation and Language →

Halal-NeSyNet is a neuro‑symbolic model that uses a DistilBERT encoder and a 16‑dimensional rule‑derived symbolic vector to audit food ingredient clauses for Halal compliance. It was evaluated on a benchmark of 3,199 items, achieving over 97 % accuracy and 99.7 % counterfactual consistency, far surpassing text‑only baselines. The model’s performance demonstrates the importance of clause‑scoped symbolic features for accurate risk‑aware Halal logistics triage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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