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This design-led qualitative comparative observation study examines how users interpret and evaluate transparency in multi-agent LLM interfaces. Participants interacted with five interface variants across information-seeking and logical-reasoning tasks, forming mental models from transparency and trustworthiness cues. Findings reveal a dynamic “Goldilocks” transparency level shaped by task demands, affordances, expertise, and dispositional AI trust, alongside tensions among visibility, sufficiency, and cognitive effort.",{"@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/sensemaking-in-multi-agent-llm-interfaces-how-users-interpret-transparency-and-trustworthiness-cues/201224/",{"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/sensemaking-in-multi-agent-llm-interfaces-how-users-interpret-transparency-and-trustworthiness-cues/201224.png","ImageObject",300,407,{"name":92,"@type":93},"\tJames","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What question does the study investigate about multi-agent LLM interfaces?","Question",{"text":113,"@type":114},"The study investigates how users interpret and evaluate transparency and trustworthiness cues in multi-agent LLM interfaces, and how different visibility choices affect user perceptions and cognitive tradeoffs.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are transparency conditions operationalized in the study?",{"text":118,"@type":114},"The study uses five interface variants (V1–V5) that differ in process visibility, including showing only the final answer, showing agents’ answers, adding rationales, introducing a critic agent, or enabling multi-turn agent debate.",{"name":120,"@type":111,"acceptedAnswer":121},"What factors determine the study’s “Goldilocks” transparency level?",{"text":122,"@type":114},"The ideal transparency level is shaped by task demands, interface affordances, and user characteristics such as task expertise and dispositional AI trust.","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},201224,1789978366,{"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":46,"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":144,"read_time":31,"preview_page_count":4},2336474466412,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Sensemaking in Multi-Agent LLM Interfaces: How Users Interpret Transparency and Trustworthiness Cues  \nSaumya Pareek  \nSchool of Computing and Information Systems University of Melbourne Melbourne, Victoria, Australia [saumya.pareek@student.unimelb.edu.au](saumya.pareek@student.unimelb.edu.au)  \nJarod Govers  \nSchool of Computing and Information Systems University of Melbourne Melbourne, Victoria, Australia [jarod.govers@unimelb.edu.au](jarod.govers@unimelb.edu.au)  \nNaja Kathrine Kollerup  \nDepartment of Computer Science Aalborg University, Aalborg Denmark [nkka@cs.aau.dk](nkka@cs.aau.dk)  \nEmily Wong  \nSchool of Computing and Information Systems University of Melbourne Melbourne, Victoria, Australia [emily.wong.1@unimelb.edu.au](emily.wong.1@unimelb.edu.au)  \nEduardo Velloso  \nSchool of Computer Science The University of Sydney Sydney, New South Wales, Australia [eduardo.velloso@sydney.edu.au](eduardo.velloso@sydney.edu.au)  \nJorge Goncalves  \nSchool of Computing and Information Systems University of Melbourne Melbourne, Victoria, Australia [jorge.goncalves@unimelb.edu.au](jorge.goncalves@unimelb.edu.au)  \nFigure 1: Diagrammatic overview of the five multi-agent Large Language Model (LLM) interface variants (V1–V5) used as exploratory probes in our study. Each variant instantiates a distinct combination of transparency-related design dimensions to surface multi-agent reasoning to the user. V1 (Final Answer) shows only the system’s final decision, with no agent-level visibility. V2 (Agents Answer) displays each agent’s individual answer alongside the majority outcome. V3 (Agents Explain) extends this by surfacing a brief rationale from each agent, before presenting the majority decision. V4 (Agents Critique) introduces a dedicated critic agent who evaluates each agent’s response, followed by a summarised final answer. V5 (Agents Debate) presents a multi-turn inter-agent discussion, where initially disagreeing agents deliberate and converge on a consensus answer. Together, these variants span a range of process visibility and aggregation mechanisms found in emerging multi-agent LLM systems. We examine how these differences shape user perceptions of transparency and trustworthiness, and the tensions that arise between increased system visibility and its cognitive costs, across different task types.  \nAbstract  \nAs multi-agent Large Language Models (LLMs) gain traction, designers must consider how to surface their internal reasoning in ways that foster appropriate trust. We present a design-led, qualitative,  \nThis work is licensed under a Creative Commons Attribution 4.0 International License. CHI’26, Barcelona, Spain  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2278-3/26/04  \n[https://doi.org/10.1145/3772318.3791157](https://doi.org/10.1145/3772318.3791157)  \ncomparative structured observation study, exploring how users interpret and evaluate transparency in multi-agent LLMs. Participants interacted with five interface variants, each instantiating different combinations of transparency-related design dimensions, across two task types: information-seeking and logical reasoning. We surface participants’ mental models, the cues they interpret as signals of transparency and trustworthiness, and how they weigh the costs and benefits of increasing process visibility. Transparency needs were dynamic and context-sensitive, with the ideal “Goldilocks”(i.e.,“just right” transparency) level shaped jointly by task demands,  \ninterface affordances, and user characteristics such as task expertise and dispositional AI trust. We highlight tensions between process visibility, information sufficiency, and cognitive effort, and synthesise these insights into design considerations for aligning transparency with user needs in future multi-agent LLM interfaces.  \nCCS Concepts  \n• Human-centered computing → HCI theory, concepts and models; Collaborative interaction.  \nKeywords  \ntransparency, trust, multi-agent chatbots, mul","cbCaiklo1RLV4Eh2","https://ap.wps.com/l/cbCaiklo1RLV4Eh2","pdf",1951373,"English","# Introduction\n## Background and motivation\n## Multi-agent LLM interfaces and transparency","[{\"question\":\"What question does the study investigate about multi-agent LLM interfaces?\",\"answer\":\"The study investigates how users interpret and evaluate transparency and trustworthiness cues in multi-agent LLM interfaces, and how different visibility choices affect user perceptions and cognitive tradeoffs.\"},{\"question\":\"How are transparency conditions operationalized in the study?\",\"answer\":\"The study uses five interface variants (V1–V5) that differ in process visibility, including showing only the final answer, showing agents’ answers, adding rationales, introducing a critic agent, or enabling multi-turn agent debate.\"},{\"question\":\"What factors determine the study’s “Goldilocks” transparency level?\",\"answer\":\"The ideal transparency level is shaped by task demands, interface affordances, and user characteristics such as task expertise and dispositional AI trust.\"}]","Sensemaking in Multi-Agent LLM Interfaces - How Users Interpret Transparency and Trustworthiness Cues | PDF",1788528167]