LATIDIA · Ciberseguridad

ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks

arXiv:2508.12035v3 Tipo de anuncio: reemplazar Resumen: En los últimos años, la inteligencia artificial generativa (GenAI) ha demostrado capacidades notables en dominios de alto riesgo como la ciencia molecular. Sin embargo,

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arXiv:2508.12035v3 Announce Type: replace Abstract: In recent years, generative artificial intelligence (GenAI) has demonstrated remarkable capabilities in high-stakes domains such as molecular science. However, challenges related to the verifiability and structural privacy of its outputs remain largely unresolved. This paper focuses on the task of molecular toxicity repair. It proposes a structure-private verification framework - ToxiEval-ZKP - which, for the first time, introduces zero-knowledge proof (ZKP) mechanisms into the evaluation process of this task. The system enables model developers to demonstrate to external verifiers that the generated molecules meet multidimensional toxicity repair criteria, without revealing the molecular structures themselves. To this end, we design a general-purpose circuit compatible with both