{"id":4479,"date":"2025-06-10T07:02:31","date_gmt":"2025-06-10T07:02:31","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/06\/10\/2506-06382\/"},"modified":"2025-06-10T07:02:31","modified_gmt":"2025-06-10T07:02:31","slug":"2506-06382","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/06\/10\/2506-06382\/","title":{"rendered":"On the Fundamental Impossibility of Hallucination Control in Large Language Models"},"content":{"rendered":"<p>    On the Fundamental Impossibility of Hallucination Control in Large Language Models<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2506.06382v1 Announce Type: new<br \/>\nAbstract: This paper explains textbf{why it is impossible to create large language models that do not hallucinate and what are the trade-offs we should be looking for}. It presents a formal textbf{impossibility theorem} demonstrating that no inference mechanism can simultaneously satisfy four fundamental properties: textbf{truthful (non-hallucinatory) generation, semantic information conservation, relevant knowledge revelation, and knowledge-constrained optimality}. By modeling LLM inference as an textbf{auction of ideas} where neural components compete to contribute to responses, we prove the impossibility using the Green-Laffont theorem. That mathematical framework provides a rigorous foundation for understanding the nature of inference process, with implications for model architecture, training objectives, and evaluation methods.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Micha{l} P. Karpowicz<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2506.06382\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>On the Fundamental Impossibility of Hallucination Control in Large Language Models arXiv:2506.06382v1 Announce Type: new Abstract: This paper explains textbf{why it is impossible to create large language models that do not hallucinate and what are the trade-offs we should be looking for}. It presents a formal textbf{impossibility theorem} demonstrating that no inference mechanism can simultaneously [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,187,408,1876,113,112],"tags":[2912,2910,2911],"class_list":["post-4479","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-ai","category-cs-cl","category-cs-gt","category-cs-lg","category-stat-ml","tag-fundamental","tag-impossibility","tag-textbf"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4479"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=4479"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4479\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=4479"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=4479"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=4479"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}