[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84239-en":3,"doc-seo-84239-105":29,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84239,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models","Hallucinations in vision–language models (VLMs) are often treated as semantic mistakes, yet they frequently stem from partial or ambiguous visual evidence. Existing research emphasizes detection or suppression during generation, leaving the later reasoning phase underexplored. This work studies Post-Hallucination Reasoning (PHR), where hallucinated semantics enter inference context and affect downstream predictions. The HIVE evaluation infrastructure enables controlled comparisons across faithful and hallucinated captions.","arXiv :2607 .07507v 1 [ cs .CV] 8 Jul 2026  \nHIVE: Understanding Post-Hallucination Reasoning in Vision Language Models  \nFeng He 1 * , Zhenting Wang2 , Qifan Wang3 , Qiang Guan4 , Dongfang Liu 1†, Ruixiang Tang2 , and Qiankun Li5†  \n1 Purdue University  \n2 Rutgers University  \n3 Meta AI  \n4 Kent State University  \n5 Imperial College London  \nAbstract. Hallucinations in vision–language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses on detecting or suppressing hallucinations at generation time, leaving the subsequent reasoning stage largely unexplored. In this work, we study Post-Hallucination Reasoning (PHR), the stage in which hallucinated semantics enter the model’s inference context and influence downstream predictions. To systematically investigate PHR, we introduce the HIVE (Hallucination Inference and Verification Engine), an evaluation infrastructure that enables controlled comparisons between faithful and hallucinated captions.  \nAcross nine tasks and nine models, we observe structured modalitydependent patterns: hallucinated captions often improve accuracy on vision–language tasks, while text-only tasks exhibit limited or unstable effects. Further analyses show that hallucinated cues broaden semantic coverage and reshape reasoning dynamics while preserving stable inference. These findings highlight that hallucinated semantics may influence downstream reasoning once they enter the model’s inference context. Understanding this post-hallucination stage is important for improving the reliability and interpretability of multimodal reasoning systems. Code is publicly available at [https://github.com/hefengcs/HIVE](https://github.com/hefengcs/HIVE).  \nKeywords: VLMs · Hallucination Analysis · Multimodal Reasoning  \n1 Introduction  \nVision–language models (VLMs) often operate under partial observability [13, 27 , 28 ,37 ,48 ,67] . Occlusion, low resolution, and motion blur frequently lead them to produce speculative semantic completions [20, 56 , 66] . According to community standards, such unverifiable content is considered hallucination. However, hallucinations arise naturally from incomplete visual evidence rather than isolated system failures [8, 10 , 11 , 18 , 70] . Consequently, most existing studies focus  \n*  \nWork done while visiting Purdue University.†Corresponding authors.  \n2 F. He et al.  \nFig. 1: Post–hallucination reasoning in VLMs. (a) Prior work mainly focuses on detecting or suppressing hallucinations, whereas we study reasoning that occurs after hallucinations appear. (b) A medical example illustrates how hallucinated cues (e.g. , lesion size) can alter the model’s observation and reasoning process. (c) Across models and vision tasks, hallucinations can systematically change task performance, revealing structured post–hallucination dynamics, especially in vision–language tasks.  \non detecting [15, 27 , 32 , 34 , 39 , 51 , 69] or mitigating hallucinations [45, 46 , 57 , 64 , 72] at the moment they appear. Far less attention has been paid to what happens after hallucinated semantics enter the reasoning process of a VLM, leaving this post-hallucination stage largely unexplored.  \nMeanwhile, studies of LLMs show that chain-of-thought reasoning [5, 26 , 35 , 44 , 45 , 61 , 62] does not strictly follow human causal logic: incorrect intermediate steps can still lead to correct conclusions. This observation suggests that reasoning may continue to evolve in latent space even when intermediate representations are imperfect or partially incorrect. By analogy, hallucinations in VLMs may also interact with downstream reasoning processes. However, whether such speculative cues help, hinder, or simply perturb reasoning remains unclear.  \nWe refer to this phenomenon as Post-Hallucination Reasoning (PHR) , which describes the reasoning behavior that emerges after hallucinated semantics become part of the model’s inference conte","cbCaid9gMUAIjDaD","https://ap.wps.com/l/cbCaid9gMUAIjDaD","pdf",2964089,1,30,"English","en",105,"# Introduction\n## Post-Hallucination Reasoning (PHR)\n## HIVE Evaluation Infrastructure\n## Mechanisms and Experimental Observations","[{\"question\":\"什么是 Post-Hallucination Reasoning（PHR）？\",\"answer\":\"PHR 指的是当视觉语言模型把幻觉语义纳入推理上下文后产生的推理行为。它会进一步影响后续预测，而不仅仅停留在生成阶段的语义错误。\"},{\"question\":\"本文为什么要研究“幻觉之后”的推理阶段？\",\"answer\":\"以往工作多关注在幻觉出现时进行检测或抑制，但较少讨论幻觉语义进入推理过程之后会发生什么。作者认为这一阶段仍缺乏系统理解。\"},{\"question\":\"HIVE（Hallucination Inference and Verification Engine）如何帮助研究 PHR？\",\"answer\":\"HIVE 构建成对的忠实字幕与幻觉字幕，验证其有效性，并在匹配条件下测量它们对下游预测的影响。通过对比原始输入、忠实与幻觉字幕，能够隔离幻觉语义诱发的推理效应。\"}]",1784194278,76,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"hive-understanding-post-hallucination-reasoning-in-vision-language-models","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/hive-understanding-post-hallucination-reasoning-in-vision-language-models/84239/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"什么是 Post-Hallucination Reasoning（PHR）？","Question",{"text":75,"@type":76},"PHR 指的是当视觉语言模型把幻觉语义纳入推理上下文后产生的推理行为。它会进一步影响后续预测，而不仅仅停留在生成阶段的语义错误。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本文为什么要研究“幻觉之后”的推理阶段？",{"text":80,"@type":76},"以往工作多关注在幻觉出现时进行检测或抑制，但较少讨论幻觉语义进入推理过程之后会发生什么。作者认为这一阶段仍缺乏系统理解。",{"name":82,"@type":73,"acceptedAnswer":83},"HIVE（Hallucination Inference and Verification Engine）如何帮助研究 PHR？",{"text":84,"@type":76},"HIVE 构建成对的忠实字幕与幻觉字幕，验证其有效性，并在匹配条件下测量它们对下游预测的影响。通过对比原始输入、忠实与幻觉字幕，能够隔离幻觉语义诱发的推理效应。","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]