AIBullisharXiv – CS AI · Mar 27/1016
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PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents
Researchers introduce PseudoAct, a new framework that uses pseudocode synthesis to improve large language model agent planning and action control. The method achieves significant performance improvements over existing reactive approaches, with a 20.93% absolute gain in success rate on FEVER benchmark and new state-of-the-art results on HotpotQA.