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arXiv AI By Yiruo Cheng, Shen Huang, Xiaoshuai Song, Jiejun Tan, Guanting Dong, Pengjun Xie, Ji-Rong Wen, Zhicheng Dou

When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation

Read the original on arXiv AI →

Large language model agents often reason before every action, but this may be unnecessary if earlier reasoning still supports later actions. The paper introduces RACE, a method that uses likelihood-based signals to detect when reasoning can be omitted, called LoGiC. Experiments on four benchmarks show RACE cuts reasoning cost while keeping or improving performance.

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 AI.

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