[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82366-en":3,"doc-seo-82366-105":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},82366,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Ceci n’est pas une pipe: AI systems as semantic abstractions","AI system outputs are engineered representations rather than direct facts or world states. The framework distinguishes what is justified by accepted domain knowledge, what reference sources state, and what the system can access at a given time, enabling precise definitions of failures such as unsupported assertions, source-knowledge mismatches, stale refutations, added hypotheses, and unsupported uses. The approach provides a vocabulary for checking outputs, citations, tool calls, and world-changing actions using reliable authority instead of fluency.","arXiv :2607 .09489v 1 [ cs .AI] 10 Jul 2026  \nCeci n’est pas une pipe: AI systems as semantic abstractions  \nJADE ALGLAVE and PATRICK COUSOT  \nAn AI system’s output is not the fact or world state it appears to describe, but rather an engineered representation. We propose a semantic framework to describe AI systems, to be able to examine the correctness of such representations. To do so, we distinguish what is justified by accepted domain knowledge, what reference sources say, and what the system can currently use. This allows us to give precise definitions to common failures: extrapolation, refuted or unsupported assertion, sources versus knowledge mismatch, stale or refuted source, added hypotheses, unsupported use…We hope our framework gives a useful vocabulary for specifying and checking AI systems whose outputs, citations, tool calls, and world-changing actions must be justified by reliable claims and explicit authority rather than apparent fluency.  \n1 Introduction  \nMany deployed AI systems are used either as assistants that answer questions or as agents that perform actions. Both kinds may proceed through a series of iterations.  \nSuch systems are often discussed in two ways that we find misleading. As magic systems: as if their behaviour could not be decomposed and understood, akinto machine learning as alchemy [77] . As oracles: as if an answer produced by the system were already the fact or world state of interest; other works warn against conflating fluent generated text with factuality [14, 15, 49, 103] .  \nThus important questions are difficult to answer with precision: what is accepted knowledge ina given domain? What are the reference sources? What do they say? What can the system use at this point in time? Does this differ from accepted domain knowledge or reference sources? What part of the world can the system observe or change? Is the system allowed to? Can we record, examine and check what led to a consequential step such as an observation of or a change to the world?  \nWe start from the stance that AI systems should be treated as engineered semantic abstractions, not as magic or oracles: an AI system’s output is not the object it appears to describe, but rather, like Magritte’s pipe, a representation of it. At a high level, we consider a system to consist of:  \n• Knowledge Bases: possibly erroneous material available to the system, e.g. prose or code;  \n• Prompts: the user’s queries, refinements, and steering during an interaction;  \n• Compute Components: e.g., one model or a cohort of models that transform prompts and reference sources into messages, artefacts, or actions, as well as Agent Services such as cameras;  \n• Actuations: the responses, artefacts, actions, or world modifications produced by the system;  \n• an Orchestrator: a possibly empty orchestration layer around the Compute Components that may call tools, retrieve files, or iterate before actuation.  \nWe use this example throughout: Oma needs to renew her passport using an AI-aided application. The system may need to inspect her old passport, check current guidance on what pictures are acceptable, take or evaluate a picture, fill in a form, handle an interruption such as the doorbell, save progress, check that everything is ready for submission, and ask Oma for confirmation to submit.  \nThe Knowledge Bases need to contain official passport requirements; the Prompts may be Oma’s requestsand corrections; Compute Components may be an LLM procured by the government, and Oma’s phone camera; the Orchestrator may be a software stack around the LLM that decides whether to retrieve, ask, check, save, or mark the application ready, and Actuation may consist of a local PDF export.  \nA system does not manipulate the semantic objects of interest directly: retrieving sources, calling tools, proposing updates, is done through interfaces. This interface layer embodies the core  \nContact Information: Jade Alglave, [jade.alglave@arm.com](jade.alglave@arm.com),","cbCaitJkhAg6u1Wb","https://ap.wps.com/l/cbCaitJkhAg6u1Wb","pdf",612916,1,36,"English","en",105,"# Introduction\n## Interpreting AI as semantic abstractions\n## System components and interfaces\n# Distinction of semantics, authority, and effects\n## Traces, checks, and readiness\n## Outline","[{\"question\":\"Why isn’t an AI system’s output the same as the fact or world state it seems to describe?\",\"answer\":\"Because the output is an engineered representation that must be interpreted and checked against underlying semantics, not taken as the world itself. The meaning, source, authority, and effect require validation.\"},{\"question\":\"What categories of information does the proposed framework distinguish to assess correctness?\",\"answer\":\"It distinguishes what is justified by accepted domain knowledge, what reference sources say, and what the system can currently use. This separation supports precise identification of different failure modes.\"},{\"question\":\"How does the framework treat a “readiness” event in an AI-assisted application?\",\"answer\":\"A readiness event is not proof; the system must interpret its message, check it against sources, and ensure it is supported by the current trace. Reliance is conditional on freshness and authority being satisfied.\"}]",1784179946,91,{"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},"ceci-nest-pas-une-pipe-ai-systems-as-semantic-abstractions","",{"@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/ceci-nest-pas-une-pipe-ai-systems-as-semantic-abstractions/82366/",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},"Why isn’t an AI system’s output the same as the fact or world state it seems to describe?","Question",{"text":75,"@type":76},"Because the output is an engineered representation that must be interpreted and checked against underlying semantics, not taken as the world itself. The meaning, source, authority, and effect require validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What categories of information does the proposed framework distinguish to assess correctness?",{"text":80,"@type":76},"It distinguishes what is justified by accepted domain knowledge, what reference sources say, and what the system can currently use. This separation supports precise identification of different failure modes.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the framework treat a “readiness” event in an AI-assisted application?",{"text":84,"@type":76},"A readiness event is not proof; the system must interpret its message, check it against sources, and ensure it is supported by the current trace. 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