{"ID":2825494,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22260","arxiv_id":"2512.22260","title":"ReVEAL: GNN-Guided Reverse Engineering for Formal Verification of Optimized Multipliers","abstract":"We present ReVEAL, a graph-learning-based method for reverse engineering of multiplier architectures to improve algebraic circuit verification techniques. Our framework leverages structural graph features and learning-driven inference to identify architecture patterns at scale, enabling robust handling of large optimized multipliers. We demonstrate applicability across diverse multiplier benchmarks and show improvements in scalability and accuracy compared to traditional rule-based approaches. The method integrates smoothly with existing verification flows and supports downstream algebraic proof strategies.","short_abstract":"We present ReVEAL, a graph-learning-based method for reverse engineering of multiplier architectures to improve algebraic circuit verification techniques. Our framework leverages structural graph features and learning-driven inference to identify architecture patterns at scale, enabling robust handling of large optimiz...","url_abs":"https://arxiv.org/abs/2512.22260","url_pdf":"https://arxiv.org/pdf/2512.22260v1","authors":"[\"Chen Chen\",\"Daniela Kaufmann\",\"Chenhui Deng\",\"Zhan Song\",\"Hongce Zhang\",\"Cunxi Yu\"]","published":"2025-12-24T13:01:55Z","proceeding":"cs.LO","tasks":"[\"cs.LO\",\"cs.AI\"]","methods":"[\"Graph Neural Network\"]","has_code":false}