• Computer Science > Artificial Intelligence [Submitted on 20 Feb 2026] Title:Neurosymbolic Language Reasoning as Satisfiability Modulo Theory View PDF HTML (experimental)Abstract:Natural language understanding requires interleaving textual and logical reasoning, yet large language models often fail to perform such reasoning reliably. • Existing neurosymbolic systems combine LLMs with solvers but remain limited to fully formalizable tasks such as math or program synthesis, leaving natural documents with only partial logical structure unaddressed. • We introduce Logitext, a neurosymbolic language that represents documents as natural language text constraints (NLTCs), making partial logical structure explicit. • We develop an algorithm that integrates LLM-based constraint evaluation with satisfiability modulo theory (SMT) solving, enabling joint textual-logical reasoning. • Experiments on a new content moderation benchmark, together with LegalBench and Super-Natural Instructions, show that Logitext improves both accuracy and coverage. • This work is the first that treats LLM-based reasoning as an SMT theory, extending neurosymbolic methods beyond fully formalizable domains.
Article Summaries:
- A new neurosymbolic framework, Logitext, treats large‑language‑model (LLM) reasoning as a satisfiability‑modulo‑theory (SMT) problem. By encoding documents as natural‑language text constraints (NLTCs), the system makes partial logical structure explicit and couples LLM‑based constraint evaluation with an SMT solver. Experiments on a novel content‑moderation benchmark, as well as LegalBench and Super‑Natural Instructions, show that Logitext improves both accuracy and coverage compared with existing neurosymbolic methods that are limited to fully formalizable tasks such as math or code synthesis. This work is the first to integrate LLM reasoning into SMT, extending neurosymbolic AI beyond strictly formal domains.
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