[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-201195-105":59,"doc-detail-201195-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","logical-reasoning-in-large-language-models-a-survey-comprehensive-review","Logical Reasoning in Large Language Models - A Survey - Comprehensive Review","","With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical reasoning within LLMs, covering its scope, theoretical foundations, and evaluation benchmarks. It analyzes deductive, inductive, abductive, and analogical paradigms and reviews techniques such as tuning, reinforcement learning, decoding methods, and neuro-symbolic approaches.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/logical-reasoning-in-large-language-models-a-survey-comprehensive-review/201195/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/logical-reasoning-in-large-language-models-a-survey-comprehensive-review/201195.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",21,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What gap does the survey target in existing literature?","Question",{"text":113,"@type":114},"It addresses the lack of a dedicated literature review on logical reasoning in LLMs and formal symbolic logic, rather than conflating it with general-purpose heuristic methods like Chain-of-Thought.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which logical reasoning paradigms are analyzed?",{"text":118,"@type":114},"The survey analyzes deductive, inductive, abductive, and analogical reasoning paradigms within LLMs.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the survey evaluate reasoning capabilities?",{"text":122,"@type":114},"It summarizes benchmarks and evaluation methodologies used to assess reasoning proficiency and highlights gaps related to symbolic inference, consistency, and robustness.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},201195,1788526889,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Logical Reasoning in Large Language Models: A Survey  \nHanmeng Liu 1†, Zhizhang Fu 1†, Mengru Ding 1 , Ruoxi Ning 1 Chaoli Zhang2 , Xiaozhang Liu3 and Yue Zhang 1 ∗  \n1 Westlake University  \n2 Zhejiang Normal University  \n3 Hainan University  \n{liuhanmeng, [zhangyue](zhangyue}@westlake.edu.cn)[}](zhangyue}@westlake.edu.cn)[@westlake.edu.cn](zhangyue}@westlake.edu.cn), {fuzhizhang.fzz, [dingmengru2021](dingmengru2021}@gmail.com)[}](dingmengru2021}@gmail.com)[@gmail.com](dingmengru2021}@gmail.com),  \n[ruoxining@outlook.com](ruoxining@outlook.com), [chaolizcl@zjnu.edu.cn](chaolizcl@zjnu.edu.cn), [lxzh@hainanu.edu.cn](lxzh@hainanu.edu.cn)  \narXiv :2502 .09100v1 [ cs .AI] 13 Feb 2025  \nAbstract  \nWith the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical reasoning within LLMs, a critical area of AI research. It outlines the scope of logical reasoning in LLMs, its theoretical foundations, and the benchmarks used to evaluate reasoning proficiency. We analyze existing capabilities across different reasoning paradigms—deductive, inductive, abductive, and analogical—and assess strategies to enhance reasoning performance, including data-centric tuning, reinforcement learning, decoding strategies, and neuro-symbolic approaches. Thereview concludes with future directions, emphasizing the need for further exploration to strengthen logical reasoning in AI systems.  \n1 Introduction  \nLogical reasoning is a fundamental challenge to artificial intelligence (AI) and natural language processing (NLP) [Newell and Simon, 1956; McCarthy and Hayes, 1981; McCarthy, 1959] . While early formal logic-based reasoning approaches faced limitations in scalability and adaptability [Pereira, 1982; Cann, 1993], data-driven models became the dominant method since the 1980s [McCarthy, 1989] . Recently, pre-trained Large Language Models (LLMs) and their emergent logical reasoning abilities have attracted increasing attention [Liu et al., 2023b; Xu et al., 2023] . Logical reasoning integrates LLMs with inference structuring, enabling multistep deduction and abstraction, and improving interpretability and reliability [Shi et al., 2021; Stacey et al., 2022; Rajaraman et al., 2023] . It also strengthens generalization, helping models handle novel scenarios beyond their training data [Haruta et al., 2020] . As LLMs become integral to domains like legal analysis and scien  \ntific discovery, ensuring the correctness and verifiability of * Corresponding author.  \n† Equal contribution.  \ntheir reasoning is increasingly vital. As a result, post-training LLM for reasoning has garnered a surge of interest in both industry and research [OpenAI, 2024; DeepSeek-AI, 2025; Muennighoff et al., 2025] .  \nDespite growing research, existing surveys [Plaat et al., 2024; Sun et al., 2023; Yu et al., 2024] often conflate logical reasoning with general-purpose heuristic strategies like Chain-of-Thought (CoT) [Xia et al., 2024] . There has been a lack of a literature review dedicated to LLM and formal symbolic logic. This survey provides a comprehensive review of logical reasoning in large language models (LLMs), with a focus on formal and symbolic logic-based reasoning rather than general heuristic approaches. We begin by defining logical reasoning in AI, distinguishing it from generalpurpose reasoning, and categorizing key paradigms, including deductive, inductive, abductive, and analogical reasoning. Additionally, we analyze existing benchmarks and evaluation methodologies, identifying gaps in assessing symbolic inference, consistency, and robustness. We further explore state-of-the-art techniques for enhancing logical reasoning, such as instruction fine-tuning, logic-informed pre-training, reinforcement learning, inference-time decoding strategi","cbCaim0ScfVGQYEB","https://ap.wps.com/l/cbCaim0ScfVGQYEB","pdf",1059714,"English","# Introduction\n## History of Logic Reasoning Research\n# Logic in Artificial Intelligence\n## Core Role of Logical Reasoning","[{\"question\":\"What gap does the survey target in existing literature?\",\"answer\":\"It addresses the lack of a dedicated literature review on logical reasoning in LLMs and formal symbolic logic, rather than conflating it with general-purpose heuristic methods like Chain-of-Thought.\"},{\"question\":\"Which logical reasoning paradigms are analyzed?\",\"answer\":\"The survey analyzes deductive, inductive, abductive, and analogical reasoning paradigms within LLMs.\"},{\"question\":\"How does the survey evaluate reasoning capabilities?\",\"answer\":\"It summarizes benchmarks and evaluation methodologies used to assess reasoning proficiency and highlights gaps related to symbolic inference, consistency, and robustness.\"}]","Logical Reasoning in Large Language Models - A Survey - Comprehensive Review | PDF",23]