[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117412-en":3,"doc-seo-117412-105":30,"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":4,"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":27,"seo_description":14,"update_tm":28,"read_time":29},117412,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Foundational Challenges in Assuring Alignment and Safety of Large Language Models","This work identifies 18 foundational challenges in assuring alignment and safety of large language models (LLMs). The challenges are organized into three categories: scientific understanding of LLMs, development and deployment methods, and broader sociotechnical challenges. For each challenge, the document proposes 200+ concrete research questions spanning evaluation, mechanistic interpretation, scaling effects, agentic behavior risks, and practical system design, aiming to guide sustained progress toward reliable, controllable LLMs.","Foundational Challenges in Assuring Alignment and Safety of Large Language Models  \nUsman Anwar 1  \nAbulhair Saparov∗2, Javier Rando∗3, Daniel Paleka∗3, Miles Turpin∗2, Peter Hase∗4 , Ekdeep Singh Lubana∗5, Erik Jenner∗6, Stephen Casper∗7, Oliver Sourbut∗8 , Benjamin L. Edelman∗9, Zhaowei Zhang∗10, Mario Günther∗11, Anton Korinek∗12 , Jose Hernandez-Orallo∗13  \nLewis Hammond8 , Eric Bigelow9 , Alexander Pan6 , Lauro Langosco 1 , Tomasz Korbak 14 , Heidi Zhang 15 , Ruiqi Zhong6 , Seán Ó hÉigeartaigh‡1, Gabriel Recchia 16 , Giulio Corsi‡1 , Alan Chan‡17, Markus Anderljung‡17, Lilian Edwards‡18, Aleksandar Petrov8 , Christian Schroeder de Witt8 , Sumeet Ramesh Motwani6  \nYoshua Bengio‡19, Danqi Chen‡20, Philip H.S. Torr‡8, Samuel Albanie‡1, Tegan Maharaj‡21 , Jakob Foerster‡8, Florian Tramer‡3, He He‡2, Atoosa Kasirzadeh‡22, Yejin Choi‡23  \nDavid Krueger‡1  \n∗ indicates major contribution.  \n‡indicates advisory role.  \n1 University of Cambridge 2 New York University 3 ETH Zurich 4 UNC Chapel Hill  \n5 University of Michigian 6 University of California, Berkeley 7 Massachusetts Institute of Technology  \n8 University of Oxford 9 Harvard University 10 Peking University 11 LMU Munich  \n12 University of Virginia 13 Universitat Politècnica de València 14 University of Sussex  \n15 Stanford University 16 Modulo Research 17 Center for the Governance of AI  \n18 Newcastle University 19 Mila - Quebec AI Institute, Université de Montréal 20 Princeton University  \n21 University of Toronto 22 University of Edinburgh 23 University of Washington, Allen Institute for AI  \nAbstract  \nThis work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs) . These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose 200+ concrete research questions.  \nCorresponding author: Usman Anwar «[usmananwar391@gmail.com](usmananwar391@gmail.com) »  \nContents  \n1 Introduction 7  \n1.1 Why This Agenda? ....................................... 7  \n1.2 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \n1.3 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \n2 Scientific Understanding of LLMs 10  \n2.1 In-Context Learning (ICL) Is Black-Box ....................... 12  \n2.1.1 Is ICL Sophisticated Pattern-Matching? ....................... 12  \n2.1.2 Is ICL Due to Mesa-Optimization?   13  \n2.1.3 What Behaviours Can Be Specified In-Context?   14  \n2.1.4 Scenario-Based Mechanistic Understanding of ICL ................. 14  \n2.1.5 Understanding the Effect of the Pre-training Data Distribution on ICL ...... 15  \n2.1.6 Understanding the Effect of Design Choices on ICL ................. 15  \n2.2 Capabilities Are Difficult to Estimate and Understand . . . . . . . . . . . . . . 17  \n2.2.1 LLM Capabilities May Have Different ‘Shape’ Than Human Capabilities ..... 17  \n2.2.2 Lack of a Rigorous Conception of Capabilities .................... 17  \n2.2.3 Limitations of Benchmarking for Measuring Capabilities and Assuring Safety .. 19  \n2.2.4 How Can We Efficiently Evaluate Generality of LLMs? ............... 20  \n2.2.5 Scaffolding Is Not Sufficiently Accounted for in Current Evaluations ....... 21  \n2.3 Effects of Scale on Capabilities Are Not Well-Characterized . . . . . . . . . . . 22  \n2.3.1 Understanding Scaling Laws .............................. 23  \n2.3.2 Effect of Scaling on Learned Representations .................... 25  \n2.3.3 Limits of Scaling .................................... 25  \n2.3.4 Formalizing, Forecasting, and Explaining Emergence . . . . . . . . . . . . . . . . 26  \n2.3.5 Better Methods for Discovering Task-Specific Scaling Laws . . . . . . . . . . . . 27  \n2.4 Qualitative Understanding of Reasoning Capabilities Is Lacking . . . . . . . . 29  \n2.4.1 Does Scaling Improve Re","cbCaiuzxvMdWVrPR","https://ap.wps.com/l/cbCaiuzxvMdWVrPR","pdf",1829828,1,182,"English","en",105,"# Introduction\n## Why This Agenda?\n## Terminology\n## Structure\n# Scientific Understanding of LLMs\n## In-Context Learning (ICL) Is Black-Box\n## Capabilities Are Difficult to Estimate and Understand\n## Effects of Scale on Capabilities Are Not Well-Characterized\n## Qualitative Understanding of Reasoning Capabilities Is Lacking\n## Agentic LLMs Pose Novel Risks\n## Multi-Agent Safety Is Not Assured by Single-Agent Safety\n## Safety-Performance Trade-offs Are Poorly Understood\n# Development and Deployment Methods\n## Pretraining Produces Misaligned Models\n## Existing Data Filtering Methods Are Insufficient","[{\"question\":\"What core problem does the document address for large language models?\",\"answer\":\"It targets foundational challenges in assuring both alignment and safety of large language models (LLMs), covering scientific, technical, and sociotechnical dimensions.\"},{\"question\":\"How are the 18 challenges organized?\",\"answer\":\"They are grouped into three categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges.\"},{\"question\":\"What outputs does the work provide beyond listing challenges?\",\"answer\":\"It proposes 200+ concrete research questions derived from the identified challenges to guide future investigation.\"}]","Foundational Challenges in Assuring Alignment and Safety of Large Language Models | PDF",1785675735,459,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"foundational-challenges-in-assuring-alignment-and-safety-of-large-language-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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/foundational-challenges-in-assuring-alignment-and-safety-of-large-language-models/117412/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What core problem does the document address for large language models?","Question",{"text":75,"@type":76},"It targets foundational challenges in assuring both alignment and safety of large language models (LLMs), covering scientific, technical, and sociotechnical dimensions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the 18 challenges organized?",{"text":80,"@type":76},"They are grouped into three categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges.",{"name":82,"@type":73,"acceptedAnswer":83},"What outputs does the work provide beyond listing challenges?",{"text":84,"@type":76},"It proposes 200+ concrete research questions derived from the identified challenges to guide future investigation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"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":106,"slug":138},19,"General","general"]