LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
A preprint by Subbarao Kambhampati and 7 co-authors, arXiv preprint 2402.01817, dated 2 February 2024. arXiv v1, 2 February 2024.
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- No authorship claim recorded yet. The source record lists Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Kaya Stechly, Mudit Verma, Siddhant Bhambri, Lucas Saldyt and Anil Murthy.
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- LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks arXiv preprint 2402.01817 (position paper) · published 2024-02-02 · Retrieved for the candidate pack on 2026-10-29 (SHA-256 c7f74530b2e1f5410935b2f89fd078e1cb3561939fb670302ac015f0cfbf6dea). arXiv v1 of 2 February 2024. The cross-family check retrieved it again on 2026-10-31, with the same bytes.
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- Arguments about catastrophic risk from advanced AI →1 record on this document
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- Language models lack agency or understandingKambhampati and co-authors' position paper of February 2024 argues that auto-regressive LLMs cannot by themselves do planning or self-verification, describing them as approximate knowledge sources and as n-gram models on steroids trained on web-scale corpora.2024-02-02 · publication · Stated in source · Supported Inspect claim & evidence ↗Language models lack agency or understanding: propositions page →
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