May 21, 2026 Professor Vaishak Belle Professor Daxin Liu Professor Devendra Singh Dhami Professor Ondrej Kuzelka Professor Efthymia Tsamoura Guest Editors, Special Issue on Neurosymbolic AI: First-Order Logic and Beyond Neurosymbolic Artificial Intelligence, IOS Press Dear Guest Editors, We are pleased to submit our manuscript entitled "A Trust-Centric Neuro-Symbolic Architecture for Verifiable Large Language Model Claims" for consideration in the Special Issue on Neurosymbolic AI: First-Order Logic and Beyond in Neurosymbolic Artificial Intelligence. We are notifying the guest editors at nai-fol-beyond@googlegroups.com as recommended by the call for papers. This paper addresses the growing concern over Large Language Model (LLM) hallucinations in safety-critical domains by formalising an architecture that integrates three previously separate capabilities: neural-to-symbolic grounding, provenance-weighted verification using Semantic Web trust mechanisms, and calibrated explanatory feedback. The key technical contribution is a five-signal trust weight that embeds digital signatures, reputation scores, and provenance metadata as first-class parameters inside a differentiable logic objective. To our knowledge, this is the first neuro-symbolic verification architecture in which provenance and trust signals modulate the satisfiability objective itself, rather than being applied as a post-hoc citation overlay. We believe the work fits naturally within the scope of this special issue. The core of our architecture is a reliable NL→FOL translation layer (Stage A) that grounds unconstrained LLM outputs into first-order logic under schema constraints and consistency guarantees, directly addressing the special issue's focus on first-order logic in neurosymbolic systems. Stage B then extends Logic Tensor Networks with a provenance-weighted satisfiability objective, operationalising trust and knowledge engineering within the logical reasoning loop. Stage C closes the loop with structured, evidence-linked explanations that support human oversight and auditability; topics the call identifies under explainability, safety, and human-in-the-loop systems. The paper additionally provides preliminary empirical evidence of the domain adaptation gap in NL→FOL translation across claim categories, contributing a benchmark observation relevant to scalability and expressiveness discussions in the special issue. The submission presents both an architectural specification and a publicly released reference implementation covering all three stages of the pipeline. We report two complementary preliminary evaluations: (i) a stage-level evaluation of NL→FOL grounding using LogicLLaMA on the FOLIO benchmark and a curated medical-claims dataset, and (ii) an end-to-end run of the implemented pipeline against a digitally signed pharmacological knowledge graph of 98 triples drawn from nine sources, producing 30 Verified, 1 Falsified, and 19 Indeterminate verdicts over the 50-claim gold standard. As discussed openly in the paper, full E3 and E4 evaluation against ground-truth verdicts and a learned trust-weighted LTN implementation are the subject of planned follow-up papers; the present manuscript is positioned as the framework paper in a programmatic three-paper research arc. In line with the journal's open data and software policy, the reference implementation, the curated medical-claims dataset, the signed knowledge graph, and the evaluation notebooks will be made publicly available upon acceptance. The source code and data are available to reviewers upon request. Neither the manuscript nor any substantial part of it has been published or is under consideration elsewhere. We have read and agreed to the IOS Press Author Copyright Agreement and the journal's open and transparent review policy. We thank you for considering our work and look forward to the review process. Sincerely, David Farrugia (corresponding author) Alexiei Dingli Department of Artificial Intelligence Faculty of ICT University of Malta Email: david.farrugia@um.edu.mt ORCID: 0000-0001-6542-6166