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arXiv Computation and Language By Davide Baldelli, Ali Parviz, Amal Zouaq, Sarath Chandar

LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agents

Read the original on arXiv Computation and Language →

The paper argues that large language models (LLMs) lack a private working memory, which is essential for tasks that require hidden state, such as interactive games. It introduces Private State Interactive Tasks (PSITs) and proves that chat-based LLMs cannot maintain a secret state while producing consistent public responses. Empirical tests confirm that standard LLMs and retrieval-based baselines fail, while a new architecture with explicit private memory succeeds.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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