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arXiv — cs.AI

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标题为英语Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

At the start of every session, LLM agents load a fixed context file, such as $\texttt{AGENTS.md}$. Each loaded token in the file is charged again in every later round of the session, and these files can degrade performance as they grow in size. However, in practice, human or…

arXiv — cs.AI作者: Zexuan Liu, Yuning Yang, Tiancheng Zhao2026年10月9日 (10月9日)
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  • At the start of every session, LLM agents load a fixed context file, such as $\texttt{AGENTS.md}$
  • Each loaded token in the file is charged again in every later round of the session, and these files can degrade performance as they grow in size
  • However, in practice, human or…
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Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

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