[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-273652-105":59,"doc-detail-273652-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","remember-verify-or-ask-cross-family-evaluation-of-memory-commitment-in-llm-agents","Remember, Verify, or Ask - Cross-Family Evaluation of Memory Commitment in LLM Agents","","Persistent memory can personalize an LLM agent, yet an incorrect durable update may silently distort future behavior. The study investigates the memory–clarification boundary: whether interaction-derived information should be persisted, used only in context, re-verified, or clarified with the user. The MCB benchmark includes 140 primary scenarios (70 development, 70 held-out) plus a contrast set, and evaluates both action labels and structured tool-call selection with cross-family model testing.",{"@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/remember-verify-or-ask-cross-family-evaluation-of-memory-commitment-in-llm-agents/273652/",{"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/remember-verify-or-ask-cross-family-evaluation-of-memory-commitment-in-llm-agents/273652.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the memory–clarification boundary address in LLM agents?","Question",{"text":113,"@type":114},"It addresses how an agent decides whether an acquired information update should be committed for future behavior, used only temporarily, verified against the world, or clarified with the user.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does the MCB benchmark evaluate?",{"text":118,"@type":114},"MCB evaluates both the agent’s stated decisions (among persist/ephemeral use/verify/clarify) and a structured tool-call selection variant that requires concrete choices and arguments.",{"name":120,"@type":111,"acceptedAnswer":121},"How is model performance assessed across different model families?",{"text":122,"@type":114},"Held-out primary and contrast items are independently labeled by non-authors with high agreement, then disagreements are resolved by a blind third party; the same adjudicated items are used to test models across Claude and Qwen with matched interventions.","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},273652,1789472380,{"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":24,"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},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","Remember, Verify, or Ask? Cross-Family Evaluation of Memory Commitment in LLM Agents  \nBaichuan Li  \nDepartment of Operations Research and Engineering Management Southern Methodist University  \nDallas, USA  \n[baichuanl@smu.edu](baichuanl@smu.edu)  \nJunyi Yao Zihao Zheng  \nDepartment of Computer Science & Engineering Washington University in St. Louis St. Louis, USA  \n[j.yao@wustl.edu](j.yao@wustl.edu) [z.zihaogary@wustl.edu](z.zihaogary@wustl.edu)  \narXiv : 2608 . 19564v 1 [cs .CL] 20 Aug 2026  \nAbstract—Persistent memory can personalize an LLM agent, but an incorrect durable update can silently distort future behavior. We study the memory–clarification boundary: whether interaction-derived information should be persisted, used only in the current context, re-verified, or clarified with the user. MCB contains 140 primary scenarios, split into 70 development and 70 held-out items, plus a separate 70-item contrast set. It evaluates both action labels and structured tool-call selection. Two non-authors independently label the 70 held-out primary and 70 contrast items (97.1% agreement, Cohen’s κ = 0 .962); a blind third resolves four disagreements, replacing eight author labels by non-author majority. Across Claude and Qwen, models verify changing facts more reliably than they ask users to resolve ambiguity. Bare Qwen asks on 0/12 clarification items while verifying 12/18 freshness items. Few-shot prompting raises accuracy from 0.557 to 0.771 (paired ∆ = +0 .214, Holm-adjusted exact McNemar pH = 0 .002), yet clarification recall remains 0.333. The policy prompt reduces erroneous persistence from 0.243 to 0.100 (pH = 0 .038), although its accuracy gain is not significant. Label–tool agreement is 57% for each Claude model and 23% for Qwen; Qwen accuracy falls from 0.557 to 0.343 (pH = 0 .047). Memory evaluation must test both stated decisions and tool-call choices.  \nIndex Terms—LLM agents, long-term memory, clarification, benchmark, tool use, reproducibility  \nI. INTRODUCTION  \nLLM agents increasingly retain interaction history to personalize later behavior and support long-horizon tasks [1], [2] . Memory formation, however, is not uniformly beneficial. A temporary request should not become a standing preference; a service status may become stale; one tool failure may be noise; and an underspecified correction may require a question before it is generalized. The critical capability is therefore not only recall, but commitment: deciding what may safely influence future behavior.  \nWe call this decision the memory–clarification boundary. Given an acquired candidate update and a later reuse context, an agent chooses among four operationally distinct actions: persist, ephemeral use, verify against the world, or clarify with the user. Verification and clarification are not interchangeable: the world is the source of truth for changing facts, whereas the user is the source of truth for intent and scope.  \nWe introduce the Memory–Clarification Boundary benchmark (MCB) . It contains 140 primary scenarios, deterministically split into 70 development and 70 held-out items,  \nplus a separate 70-item contrast set for robustness analysis. MCB tests both stated choices and structured tool-call selection. Four reference systems, two pinned Claude models, and a local Qwen3.5-9B model are evaluated on the same 70 independently adjudicated held-out items. Every model is tested with a bare prompt, an explicit five-rule policy, and four development-set demonstrations. Shared items permit exact paired tests instead of conclusions drawn from overlapping independent intervals.  \nWe test whether the observed failure generalizes beyond a single model provider. We also separate two claims often conflated as “prompt sensitivity”: a prompt can improve total accuracy, or can reshape a safety-relevant behavior such as erroneous persistence without changing total accuracy. Qwen exhibits both after multiplicity correction. Few-shot prompting improves accuracy; th","cbCaioWzd38XYNkP","https://ap.wps.com/l/cbCaioWzd38XYNkP","pdf",227651,"English","# Introduction\n## Memory–Clarification Boundary\n## Memory–Clarification Boundary Benchmark (MCB)\n# Related Work\n## Long-term memory evaluation benchmarks","[{\"question\":\"What problem does the memory–clarification boundary address in LLM agents?\",\"answer\":\"It addresses how an agent decides whether an acquired information update should be committed for future behavior, used only temporarily, verified against the world, or clarified with the user.\"},{\"question\":\"What does the MCB benchmark evaluate?\",\"answer\":\"MCB evaluates both the agent’s stated decisions (among persist/ephemeral use/verify/clarify) and a structured tool-call selection variant that requires concrete choices and arguments.\"},{\"question\":\"How is model performance assessed across different model families?\",\"answer\":\"Held-out primary and contrast items are independently labeled by non-authors with high agreement, then disagreements are resolved by a blind third party; the same adjudicated items are used to test models across Claude and Qwen with matched interventions.\"}]","Remember, Verify, or Ask - Cross-Family Evaluation of Memory Commitment in LLM Agents | PDF",13]