Dear editors, We are pleased to submit our manuscript “ReasonX: Declarative Reasoning on Explanations” for consideration in the “Special Issue on Explainable Neurosymbolic AI (X-NeSy)” in Neurosymbolic Artificial Intelligence. Explanations play a crucial role in understanding complex machine learning (ML) models. However, current eXplanation in AI (XAI) methods suffer from several limitations, including insufficient abstraction, limited user interactivity, and inadequate integration of background knowledge. To address these issues, we propose ReasonX, an explanation framework based on expressions in an algebra of operators over theories of linear constraints. ReasonX provides declarative, interactive explanations for decision trees, either directly as the models under analysis or as surrogate models for arbitrary black‑box predictors. Users can express background knowledge using linear constraints and Mixed-Integer Linear Programming optimization over features of factual and contrastive instances, and interact with the answer constraints at various levels of abstraction, from fully specified instances to under-specified ones. We present the architecture of ReasonX, which consists of a Python layer, closer to the user, and a Constraint Logic Programming (CLP) layer, which implements a meta-interpreter of the query algebra. The capabilities of ReasonX are demonstrated through qualitative examples, and compared to other XAI tools through quantitative experiments. By using CLP as a core component of our approach, we are “leveraging symbolic methods as a core component of the modeling and interpretation process” (CfP of the special issue), for which we believe the contribution fits well within the scope of the special issue. We look forward to hearing from you. Sincerely, Laura State, Salvatore Ruggieri and Franco Turini