Dear Editors, I am pleased to submit "Learning While Reasoning: Pattern-Based Crystallization and the Gap-Detect-Crystallize-Retrieve Loop for Neurosymbolic AI" for consideration in Neurosymbolic Artificial Intelligence. This paper presents a continuous learning architecture that enables neurosymbolic AI systems to acquire knowledge during conversation — identifying gaps in their symbolic knowledge graph, extracting answers from source prose, and crystallizing them into permanent RDF triples in real time. The key finding is that triple extraction requires no generative LLM: deterministic pattern extraction achieves 97.6% precision while the neural component is confined to gap detection. Once crystallized, knowledge is retrieved symbolically with zero LLM calls and zero forgetting. The paper fits squarely within NAI's scope. It addresses a core neurosymbolic challenge — how symbolic and neural components should divide labor during learning — and demonstrates that the boundary can be drawn more sharply than prior work suggests. The architecture is evaluated through a controlled 8-hour experiment with six pre-registered hypotheses, all validated. Nine limitations are discussed openly, including predicate monoculture and single-domain validation. The work has been published as an open-access preprint on Zenodo (DOI: 10.5281/zenodo.19788337) under CC BY 4.0. The NuSy Brain architecture is proprietary; however, several supporting components are open source (yurtle-rdflib, yurtle-kanban, acf-framework on GitHub under MIT License), and experimental data will be made available upon publication for research use. Code: https://github.com/hankh95 — Papers: https://congruentsys.com/papers/ I am comfortable with the journal's transparent review process and welcome public commentary. Thank you for your consideration. Sincerely, Henry (Hank) Head ORCID: 0000-0002-3912-0080 Congruent Systems LLC papers@congruentsys.com