arXiv:2606. 20227v1 Announce Type: new Abstract: Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making.
The paper investigates whether large language models (LLMs) truly grasp logical structure or merely rely on surface semantics. By evaluating models on logically equivalent inputs, the authors find that many LLMs fail to produce consistent predictions and struggle with unseen logical forms. They introduce LoGIST, a framework that maps inputs to decision diagrams and uses these logical embeddings to guide LLM reasoning, thereby reducing inconsistency and improving generalization.
arXiv:2603. 05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood.
arXiv:2604. 27960v2 Announce Type: replace Abstract: Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp performance degradation on high-complexity problems.
arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.
The paper introduces a neuro‑symbolic framework for scientific reasoning that separates symbolic validity and semantic groundedness. A deterministic symbolic verifier acts as a hard filter to guarantee syntactic and arithmetic correctness, while a Process Reward Model (PRM) is trained on verifier‑accepted steps to assess contextual grounding. The authors propose Counterfactual Symbolic Perturbation (CSP) to generate hard negative examples that pass the verifier but are logically flawed, enabling efficient PRM training and a verifier‑first constrained search at inference.
arXiv:2607. 20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formulations as interchangeable and conflates reasoning errors with interface failures.
The paper investigates when intermediate reasoning traces benefit machine translation by examining models, languages, domains, and datasets. It finds that the optimal reasoning language depends on the model, reasoning length has a non‑monotonic effect on quality, and traces display recurring functional patterns. Using Hierarchical Meta‑Summarization, the authors uncover a shared structure of understanding/planning, translating/drafting, and refining/verifying, while also noting domain‑specific variations, suggesting that reasoning should be tailored rather than uniformly applied.
arXiv:2601. 22642v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid.
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.
The paper investigates whether large language models (LLMs) follow Occam's Razor when performing inductive and abductive reasoning. It introduces a synthetic framework for generating questions that require both types of reasoning and a new automated metric to evaluate the simplicity and correctness of generated hypotheses. Experiments show that while LLMs can handle simple scenarios, they struggle with complex world models and producing high‑quality, simplest hypotheses, even when using advanced reasoning techniques.
arXiv:2509. 00930v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities.