[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82883-en":3,"doc-seo-82883-105":30,"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":13,"seo_description":14,"update_tm":28,"read_time":29},82883,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","A Comprehensive Study of Implementation Bugs in Multi-modal Agents","Multi-Modal Agents (M-agents) powered by Large Language Models (LLMs) operate in dynamic, high-dimensional multi-modal environments, creating implementation risks beyond those of traditional agents. A systematic investigation addresses the lack of M-agent-specific implementation bug research by collecting 34 representative systems and extracting 158 bugs from 1,268 issue reports. A top-down taxonomy organizes bugs by end-user global symptoms, component-level functionality symptoms, and root causes. MATester then detects bugs via runtime inter-component outputs, covering 61.4% of known issues and discovering 31 new ones.","A Comprehensive Study of Implementation Bugs in  \nMulti-modal Agents  \nSuwan Li  \nDepartment of Computer Science Nanjing University Nanjing, China [lisuwan@smail.nju.edu.cn](lisuwan@smail.nju.edu.cn)  \nLei Bu  \nDepartment of Computer Science Nanjing University Nanjing, China  \nShangqing Liu  \nDepartment of Software Engineering Nanjing University Nanjing, China  \narXiv :2607 .04974v 1 [ cs . SE] 6 Jul 2026  \nYile Wang  \nDepartment of Computer Science Nanjing University Nanjing, China  \nGuangdong Bai  \nDepartment of Computer Science The City University of Hongkong Hongkong, China  \nFuman Xie  \nSchool of Electrical Engineering and Computer Science The University of Queensland Brisbane, Australia  \nKai Chen  \nInstitute of Information Engineering Chinese Academy of Science Beijing, China  \nChang Yue  \nInstitute of Information Engineering Chinese Academy of Science  \nBeijing, China  \nAbstract—Multi-Modal Agents (M-agents), empowered by Large Language Models (LLMs), excel in various complex, open-world scenarios such as autonomous driving and robotics. However, their unique requirements to interact with dynamic and diverse multi-modal environments introduce novel implementation challenges beyond those faced by traditional agents. Outdated perception, untrustworthy planning and inapplicable execution could cause traffic accident and financial loss. Despite growing study on agent issues, there has not been a systematic study focusing on M-agent-specific implementation bugs.  \nTo address this gap, we conducted the first systematic study of implementation bugs in M-agents. We collected 34 representative M-agents from diverse sources and, through meticulous filtering, identified 158 M-agent-specific bugs from 1,268 issue reports. Using a top-down strategy, we developed a comprehensive taxonomy that classifies bugs by global symptoms, functionality component-level symptoms, and root causes. We then implemented MATester, an automatic proofof-concept bug identifier by analyzing runtime inter-component outputs. When applied to 12 extra M-agents, MATester successfully covered 61.4% of known open issues and discovered 31 additional bugs, demonstrating the practical usefulness of our study. Our work provides a comprehensive reference and guideline for classification, prevention and fix of M-agent bugs.  \nIndex Terms—Multi-modal Agent, Large Language Model, Implementation Bug, Empirical Study  \nI. Introduction  \nOwing to the rapid advancement of large language models (LLMs), intelligent agents have emerged as a prominent paradigm with capabilities including reasoning [1],  \n[2], program synthesis [3]–[5], and counseling [6] . Multimodal agents (M-agents) further extend this paradigm by interacting with high-dimensional, real-time and heterogeneous environments, enabling deployment in openworld safety-critical scenarios such as autonomous driving [7]–[9], robotics [10]–[13], and GUI automation [14]–[17] . However, implementation flaws in M-agents’ functional components may lead to severe consequences. For instance, outdated environmental perception in autonomous driving can result in traffic accidents [18] . Unconstrained execution of unverified plans in GUI automation manifestsas unexpected behaviors, potentially leading to financial losses [19] and privacy violations [20] .  \nNevertheless, existing empirical studies primarily focus on single-modal agents, emphasizing aspects such as security [21], compliance [22], code-level implementation defects [23] and general module-level issues [24] . Many works concentrate on specific application domains, like software engineering [25], [26], code generation [27] and search [28], or agent architectures like multi-agents [29] and platform-orchestrated agent [30] . Although they study bugs from multiple perspectives, there is still a lack of systematic investigation dedicated to M-agents, particularly with respect to multi-modal environment interaction bugs. Compared with single-modal agents, M-agents exhibit","cbCairckdBPCB5hZ","https://ap.wps.com/l/cbCairckdBPCB5hZ","pdf",949394,5,1,13,"English","en",105,"# Introduction\n## Motivation and Background\n## Distinct Characteristics of M-Agents\n## Study Method and Contributions","[{\"question\":\"Why do implementation bugs in multi-modal agents pose higher risks than in single-modal agents?\",\"answer\":\"M-agents must fuse real-time multi-modal perception for LLM processing, adapt execution dynamically to heterogeneous environments, and reconcile cross-modal representations that may conflict. These factors can turn implementation flaws into safety-critical or financial-impacting failures.\"},{\"question\":\"How was the dataset of M-agent bugs constructed in the study?\",\"answer\":\"The study identified 86 candidate M-agents from GitHub, top-tier publications, and surveys, then used coarse-grained text filtering and fine-grained manual code inspection to keep systems with complete functionality components. From 1,268 raw reports, it manually labeled 130 M-agent-specific reports containing 158 distinct bugs.\"},{\"question\":\"What does the proposed taxonomy classify, and how does MATester use it?\",\"answer\":\"The taxonomy classifies bugs across three dimensions: global symptoms observable by end users, functionality-component-level symptoms for developers, and root causes. MATester leverages runtime inter-component outputs to automatically identify both symptom categories, covering 61.4% of known open issues and finding 31 additional bugs on 12 extra M-agents.\"}]",1784183641,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-comprehensive-study-of-implementation-bugs-in-multi-modal-agents","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-comprehensive-study-of-implementation-bugs-in-multi-modal-agents/82883/",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":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",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},"Why do implementation bugs in multi-modal agents pose higher risks than in single-modal agents?","Question",{"text":76,"@type":77},"M-agents must fuse real-time multi-modal perception for LLM processing, adapt execution dynamically to heterogeneous environments, and reconcile cross-modal representations that may conflict. These factors can turn implementation flaws into safety-critical or financial-impacting failures.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the dataset of M-agent bugs constructed in the study?",{"text":81,"@type":77},"The study identified 86 candidate M-agents from GitHub, top-tier publications, and surveys, then used coarse-grained text filtering and fine-grained manual code inspection to keep systems with complete functionality components. From 1,268 raw reports, it manually labeled 130 M-agent-specific reports containing 158 distinct bugs.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the proposed taxonomy classify, and how does MATester use it?",{"text":85,"@type":77},"The taxonomy classifies bugs across three dimensions: global symptoms observable by end users, functionality-component-level symptoms for developers, and root causes. MATester leverages runtime inter-component outputs to automatically identify both symptom categories, covering 61.4% of known open issues and finding 31 additional bugs on 12 extra M-agents.","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},[94,98,102,106,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]