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arXiv AI By Jinfeng Xu, Zheyu Chen, Ziyue Peng, Zheng Lin, Shuo Yang, Jinze Li, Zheng Xing, Mengran Li, Victor C. M. Leung

Learning What to Skip: Counterfactual Credit Assignment for Efficient Multi-Agent LLM Workflows

Read the original on arXiv AI →

The paper introduces Learning What to Skip (LW2S), a method that learns when to omit components in multi‑agent LLM workflows by treating omission as counterfactual credit assignment. LW2S builds action‑specific safety models from controlled skip interventions and uses calibration plus domain‑native guards to decide which steps to skip. Experiments on mathematical reasoning, multiple‑choice QA, and code generation show that LW2S cuts token cost while maintaining or improving overall accuracy, and further studies reveal component redundancy and limitations of agreement‑based skip selection.

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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