The paper introduces LinguaArray, a framework that uses large language models (LLMs) to suggest a small set of focal ground terms or constraints—called a Semantic Focus Set—in quantified array SMT solving. The LLM’s suggestions are then verified by a conventional SMT solver, which remains the sole source of proof. Experiments on 1,015 SMT-LIB instances show that LinguaArray solves up to 69.1% of cases and increases the number of solved instances by 105%–397% compared to the backends alone.
arXiv:2606. 00671v1 Announce Type: new Abstract: We present AXIOM, a trust-first neuro-symbolic execution architecture for natural-language mathematical reasoning.
The paper introduces DRAGO, a framework that repairs formal specifications generated by large language models (LLMs) using deterministic, formalism‑specific diagnosis instead of ungrounded LLM critiques. DRAGO selectively rewrites specifications only when the diagnosed error weight decreases, reducing regressions from 11.8% to 1.3% and improving answer accuracy by 12.9 percentage points over zero‑shot formalization and 8.7 percentage points over LLM‑diagnostic repair across five benchmarks. Additionally, the authors identified and corrected rendering defects in Planetarium that caused half of its floor‑tile split instances to be inconsistent with reference formalizations.
arXiv:2606. 19588v1 Announce Type: new Abstract: Formal tools such as SAT and SMT solvers are increasingly embedded in language model reasoning pipelines when a safety or security critical question can be formulated in logic.
arXiv:2607. 13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises.
The paper introduces SMTrap, a cost‑effective denial‑of‑service attack framework for large reasoning models that does not rely on model feedback or GPU resources. It uses conflict counts from an SMT solver to guide the creation of inference‑heavy constraint satisfaction problem queries, exploiting the models’ backtracking search to induce long output trajectories. Experiments on seven state‑of‑the‑art models show SMTrap achieves DoS effects several times stronger than existing methods, and the authors also present a mitigation tool that reduces token usage.
arXiv:2607. 07391v1 Announce Type: new Abstract: Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue.
arXiv:2601.20055v3 Announce Type: replace-cross Abstract: Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamenta...
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.
arXiv:2603. 05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood.
arXiv:2607. 16372v1 Announce Type: cross Abstract: Interactive theorem proving (ITP) underpins program verification and formalized mathematics, but its manual effort limits scalability.
arXiv:2606. 18557v1 Announce Type: new Abstract: A rule-based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.
arXiv:2606. 23238v2 Announce Type: replace Abstract: Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction over fixed predicates.