[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160435-en":3,"doc-seo-160435-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},160435,687207024643,"Rhys","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Revisiting the Uniform Information Density Hypothesis in LLM Reasoning","Uniform Information Density (UID) links effective communication to maintaining a stable flow of information. This work reexamines UID for Large Language Model (LLM) reasoning by testing whether uniformity at the level of individual reasoning steps reflects reasoning quality. The study proposes an entropy-based stepwise density metric to quantify local and global uniformity of information flow, then validates it on seven reasoning benchmarks. Results show high-quality reasoning has smooth local transitions but non-uniform trajectories globally, and these signals outperform alternative internal predictors while the divergence reflects different objectives from human communication.","Revisiting the Uniform Information Density Hypothesis in LLM Reasoning  \nMinju Gwak 1 ,2 Guijin Son2 Jaehyung Kim 1  \n1 Yonsei University 2 OneLine AI  \n[mjgwak@yonsei.ac.kr](mjgwak@yonsei.ac.kr) , [jaehyungk@yonsei.ac.kr](jaehyungk@yonsei.ac.kr)  \nAbstract  \nThe Uniform Information Density (UID) hypothesis proposes that effective communication is achieved by maintaining a stable flow of information. In this work, we revisit this principle in the context of Large Language Model (LLM) reasoning, asking whether step-level uniformity reflects reasoning quality. To this end, we introduce a novel framework to quantify uniformity of information flow at both local and global levels, using an entropy-based stepwise density metric. Across experiments on seven reasoning benchmarks, we see a counterintuitive pattern: while high-quality reasoning exhibit smooth step-by-step transitions (local uniformity) and structured, non-uniform information flow at the trajectory level (global nonuniformity) . The results demonstrate that these uniformities outperform alternative internal signals as predictors of reasoning quality, and such divergence with human communication is not a model deficiency, but a byproduct of distinct objectives between human communication and LLM reasoning. 1  \n1 Introduction  \nChain-of-Thought (CoT) reasoning has become a central technique for enhancing large language models (LLMs) on complex reasoning tasks (Wei et al., 2023 ; Kojima et al., 2023 ; Chae et al., 2023) . By generating step-by-step rationales, CoT enables models to decompose problems into simpler subproblems and thereby improve accuracy (Golovneva et al., 2023 ; Prasad et al., 2023 ; Yao et al., 2023) . Despite these successes, recent studies have highlighted the fragility of this approach (Zhao et al., 2025a) . For example, the intermediate rationales are often logically inconsistent or incoherent, and hence models fail to generalize out-of domain tasks even when producing lengthy reasoning traces (Shojaee et al., 2025) . This raises a  \n1Code is released at: [https://github.com/](https://github.com/)[ ](https://github.com/)talzoomanzoo/uid-reasoning  \nFigure 1: Reasoning as information flow. Human communication distributes information smoothly to respect channel capacity, enabling successful understanding. LLM reasoning transmits information across reasoning steps; failures arise from local overload (sharp spikes) and underuse (flat trivial trajectory) .  \ncritical question: how can we determine whether LLMs are reasoning effectively, rather than merely generating superficially coherent text?  \nClues may lie in human communication itself; the psycholinguistic hypothesis of Uniform Information Density (UID) proposes that speakers distribute information as evenly as possible to balance clarity and efficiency (Fenk and Fenk-Oczlon, 1980 ; Genzel and Charniak, 2002 ; Clark et al., 2023 ; Jaeger and Levy, 2006) . Namely, a relatively uniform flow of information is necessary for effective communication (Meister et al., 2021 ; Aylett and Turk, 2004), aligned with the limits of human cognitive processing. When this balance is disrupted by too much or too little information, communication deteriorates. Motivated by this, we ask whether a similar principle governs reasoning in LLMs. As human speakers maintain balanced information flow to support comprehension, effective reasoning traces may require comparable uniformity across steps. Recent findings in cognitive science support this view: Bhambri et al. (2025) shows that reasoning paths interpretable to humans  \n31304  \nFindings of the Association for Computation a1l Linguistics: ACL 2026, pages 31304–31333 July 2-7, 2026 ©2026 Association for Computational Linguistics  \nare also easier for models to generate and learn, suggesting a shared structure between human cognition and machine reasoning.  \nTo investigate this, we focus on analyzing the information flow of LLM-generated reasoning traceson challenging mathema","cbCaib2oUXjMVpvU","https://ap.wps.com/l/cbCaib2oUXjMVpvU","pdf",9921513,2,1,30,"English","en",105,"# Introduction\n## Chain-of-Thought reasoning and the fragility problem\n## Uniform Information Density (UID) as a communication hypothesis\n# Exploring the Uniform Information Density Hypothesis in LLM Reasoning\n## Background: the UID hypothesis","[{\"question\":\"What does the Uniform Information Density (UID) hypothesis claim, and how is it applied here to LLM reasoning?\",\"answer\":\"UID proposes that effective communication maintains a balanced information flow. This paper tests whether similar step-level balance is linked to how well LLMs reason.\"},{\"question\":\"How are local and global uniformity of information flow quantified in the proposed framework?\",\"answer\":\"The method uses an entropy-based stepwise density metric and adds complementary metrics to measure uniformity at local (per-step) and global (trajectory-level) levels.\"},{\"question\":\"What counterintuitive pattern does the paper find across reasoning benchmarks?\",\"answer\":\"High-quality reasoning shows smooth local step transitions (high local uniformity) but a structured yet non-uniform information flow over the full trajectory (low global uniformity).\"}]","Revisiting the Uniform Information Density Hypothesis in LLM Reasoning | PDF",1788063378,76,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"revisiting-the-uniform-information-density-hypothesis-in-llm-reasoning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/revisiting-the-uniform-information-density-hypothesis-in-llm-reasoning/160435/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-04","2026-08-30",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the Uniform Information Density (UID) hypothesis claim, and how is it applied here to LLM reasoning?","Question",{"text":76,"@type":77},"UID proposes that effective communication maintains a balanced information flow. This paper tests whether similar step-level balance is linked to how well LLMs reason.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are local and global uniformity of information flow quantified in the proposed framework?",{"text":81,"@type":77},"The method uses an entropy-based stepwise density metric and adds complementary metrics to measure uniformity at local (per-step) and global (trajectory-level) levels.",{"name":83,"@type":74,"acceptedAnswer":84},"What counterintuitive pattern does the paper find across reasoning benchmarks?",{"text":85,"@type":77},"High-quality reasoning shows smooth local step transitions (high local uniformity) but a structured yet non-uniform information flow over the full trajectory (low global uniformity).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":22,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]