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Consequence-based reasoning can be used to construct proofs that explain entailments of description logic (DL) ontologies. In the literature, one can find multiple consequence-based calculi for reasoning in the $\mathcal{EL}$ family of DLs,…

计算机科学中的逻辑 · 计算机科学 2025-07-30 Christian Alrabbaa , Stefan Borgwardt , Philipp Herrmann , Markus Krötzsch

Reinforcement Learning (RL) traditionally relies on scalar reward signals, limiting its ability to leverage the rich semantic knowledge often available in real-world tasks. In contrast, humans learn efficiently by combining numerical…

Inspired by the efficient proof procedures discussed in {\em Computability logic} \cite{Jap03,Japic,Japfin}, we describe a heuristic proof procedure for first-order logic. This is a variant of Gentzen sequent system and has the following…

计算机科学中的逻辑 · 计算机科学 2019-12-24 Keehang Kwon

Reasoning over natural language is a challenging problem in NLP. In this work, we focus on proof generation: Given a hypothesis and a set of supporting facts, the model generates a proof tree indicating how to derive the hypothesis from…

计算与语言 · 计算机科学 2022-10-25 Kaiyu Yang , Jia Deng , Danqi Chen

Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which enables thorough exploration and deliberation. Despite these…

计算与语言 · 计算机科学 2026-04-09 Junnan Liu , Hongwei Liu , Songyang Zhang , Kai Chen

We introduce a sequent calculus for the propositional team logic with both the split disjunction and the inquisitive disjunction consisting of a Gentzen-style system (G3-like) for classical propositional logic together with two…

逻辑 · 数学 2025-08-12 Aleksi Anttila , Rosalie Iemhoff , Fan Yang

Cut-introduction is a technique for structuring and compressing formal proofs. In this paper we generalize our cut-introduction method for the introduction of quantified lemmas of the form $\forall x.A$ (for quantifier-free $A$) to a method…

计算机科学中的逻辑 · 计算机科学 2014-02-12 Stefan Hetzl , Alexander Leitsch , Giselle Reis , Janos Tapolczai , Daniel Weller

Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks and exhibited impressive reasoning abilities by applying zero-shot Chain-of-Thought (CoT) prompting. However, due to the evolving nature of sentence…

计算与语言 · 计算机科学 2024-02-09 Feihu Jin , Yifan Liu , Ying Tan

Cut-elimination is the bedrock of proof theory. It is the algorithm that eliminates cuts from a sequent calculus proof that leads to cut-free calculi and applications. Cut-elimination applies to many logics irrespective of their semantics.…

计算机科学中的逻辑 · 计算机科学 2022-03-04 Agata Ciabattoni , Timo Lang , Revantha Ramanayake

Most modal logics such as S5, LTL, or ATL are extensions of Modal Logic K. While the model checking problems for LTL and to a lesser extent ATL have been very active research areas for the past decades, the model checking problem for the…

人工智能 · 计算机科学 2012-07-11 Abdallah Saffidine

In the field of large language model (LLM)-based proof generation, despite extensive training on large datasets such as ArXiv, LLMs still exhibit only modest performance on proving tasks of moderate difficulty. We believe that this is…

We propose a cut-free cyclic system for Transitive Closure Logic (TCL) based on a form of hypersequents, suitable for automated reasoning via proof search. We show that previously proposed sequent systems are cut-free incomplete for basic…

计算机科学中的逻辑 · 计算机科学 2022-05-19 Anupam Das , Marianna Girlando

We introduce SATBench, a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SAT) problems. Unlike prior work that focuses on inference…

人工智能 · 计算机科学 2025-09-23 Anjiang Wei , Yuheng Wu , Yingjia Wan , Tarun Suresh , Huanmi Tan , Zhanke Zhou , Sanmi Koyejo , Ke Wang , Alex Aiken

Legal syllogism is a form of deductive reasoning commonly used by legal professionals to analyze cases. In this paper, we propose legal syllogism prompting (LoT), a simple prompting method to teach large language models (LLMs) for legal…

计算与语言 · 计算机科学 2023-07-18 Cong Jiang , Xiaolei Yang

Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Augmented Thoughts often fall short in complex tasks due to…

计算与语言 · 计算机科学 2024-12-24 Jinghan Zhang , Xiting Wang , Weijieying Ren , Lu Jiang , Dongjie Wang , Kunpeng Liu

Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensive reasoning processes, its emphasis on intermediate…

计算与语言 · 计算机科学 2023-10-05 Zhan Ling , Yunhao Fang , Xuanlin Li , Zhiao Huang , Mingu Lee , Roland Memisevic , Hao Su

Recent advancements have significantly augmented the reasoning capabilities of Large Language Models (LLMs) through various methodologies, especially chain-of-thought (CoT) reasoning. However, previous methods fail to address reasoning…

计算与语言 · 计算机科学 2024-10-22 Tinghui Zhu , Kai Zhang , Jian Xie , Yu Su

Extending and generalizing the approach of 2-sequents (Masini, 1992), we present sequent calculi for the classical modal logics in the K, D, T, S4 spectrum. The systems are presented in a uniform way-different logics are obtained by tuning…

计算机科学中的逻辑 · 计算机科学 2020-01-08 Simone Martini , Andrea Masini , Margherita Zorzi

As LLMs make their way into many aspects of our lives, one place that warrants increased scrutiny with LLM usage is scientific research. Using LLMs for generating or analyzing data for research purposes is gaining popularity. But when such…

人机交互 · 计算机科学 2024-05-13 Chirag Shah

Satisfiability checking for Linear Temporal Logic (LTL) is a fundamental step in checking for possible errors in LTL assertions. Extant LTL satisfiability checkers use a variety of different search procedures. With the sole exception of LTL…

计算机科学中的逻辑 · 计算机科学 2014-04-30 Jianwen Li , Geguang Pu , Lijun Zhang , Moshe Y. Vardi , Jifeng He