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arXiv — cs.AI

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Seq@K: Evaluating Language Models Under Sequential Feedback

arXiv:2610.37859v1 Announce Type: new Abstract: Language-model agents differ fundamentally from prior intelligent systems in their ability to learn from natural language in context. This capability enables agents to iteratively refine their answers in response to feedback, a…

arXiv — cs.AIBy: Thomson Yen, Gilbert Yang, Naimeng Ye, Minghui Chen, Dong Xia, Tianyi Shen, Yechan Jeon, Yaqin Chen, Daksh Mittal, Fangyu Wu, Hongseok NamkoongOctober 30, 2026 (13 hours ago)
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  • arXiv:2610.37859v1 Announce Type: new Abstract: Language-model agents differ fundamentally from prior intelligent systems in their ability to learn from natural language in context
  • This capability enables agents to iteratively refine their answers in response to feedback, a…
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Seq@K: Evaluating Language Models Under Sequential Feedback

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