Dear Editors, We are pleased to submit our manuscript entitled “A Unified NeuroSymbolic Architecture for Responsible Artificial Intelligence” for consideration in the special issue “Trustworthy Neurosymbolic AI in Regulated Domains: Advances, Challenges, and Applications” of Neurosymbolic Artificial Intelligence. This manuscript proposes a unified conceptual NeuroSymbolic architecture for operationalizing Responsible Artificial Intelligence in ethically sensitive and regulated domains. The framework integrates neural perception, symbolic reasoning, explicit ethical constraints, and fuzzy logic for uncertainty-aware reasoning. It is designed to address important challenges in trustworthy AI, including interpretability, constraint enforcement, transparency, compliance, and alignment with human values. The manuscript has direct relevance to the special issue through its examination of how NeuroSymbolic principles can support trustworthy decision-making and domain-specific compliance in high-stakes settings, including healthcare, finance, and autonomous systems. The proposed architecture provides a general reference framework that can be adapted to different regulatory, ethical, and operational requirements. We emphasize that this submission is intentionally presented as a conceptual position/vision paper. It does not claim an implementation or empirical evaluation; rather, it develops a unified reference architecture and discusses its potential applicability and research implications. The manuscript is intended to provide a conceptual foundation for future development and empirical validation of trustworthy NeuroSymbolic AI systems. We confirm that the manuscript is original, has not been published previously, and is not currently under consideration for publication elsewhere. Thank you for considering our manuscript for the special issue. We believe that it aligns closely with the special issue's focus on trustworthy, responsible, and practically relevant NeuroSymbolic AI. Sincerely, Abraham Itzhak Weinberg and Kelly Cohen