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    <abstract>Language Agent could be endowed with different mechanisms for autonomous task accomplishment. Current agents typically rely on a fixed mechanism or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. To this end, this paper proposes Adaptive Language Agent Mechanism Activation Learning with Self-Exploration (ALAMA), which focuses on optimizing mechanism activation adaptability without reliance on expert models. Initially, it builds a harmonized agent framework (UniAct) to Unify different mechanisms via Actions. Then it leverages a training-efficient optimization method based on self-exploration to enable the UniAct to adaptively activate the appropriate mechanisms according to the potential characteristics of the task. Experimental results demonstrate significant improvements in downstream agent tasks, affirming the effectiveness of our approach in facilitating more dynamic and context-sensitive mechanism activation.</abstract>
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%0 Conference Proceedings
%T Towards Adaptive Mechanism Activation in Language Agent
%A Huang, Ziyang
%A Zhao, Jun
%A Liu, Kang
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F huang-etal-2025-towards
%X Language Agent could be endowed with different mechanisms for autonomous task accomplishment. Current agents typically rely on a fixed mechanism or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. To this end, this paper proposes Adaptive Language Agent Mechanism Activation Learning with Self-Exploration (ALAMA), which focuses on optimizing mechanism activation adaptability without reliance on expert models. Initially, it builds a harmonized agent framework (UniAct) to Unify different mechanisms via Actions. Then it leverages a training-efficient optimization method based on self-exploration to enable the UniAct to adaptively activate the appropriate mechanisms according to the potential characteristics of the task. Experimental results demonstrate significant improvements in downstream agent tasks, affirming the effectiveness of our approach in facilitating more dynamic and context-sensitive mechanism activation.
%U https://aclanthology.org/2025.coling-main.194/
%P 2867-2885

Markdown (Informal)

[Towards Adaptive Mechanism Activation in Language Agent](https://aclanthology.org/2025.coling-main.194/) (Huang et al., COLING 2025)

• Towards Adaptive Mechanism Activation in Language Agent (Huang et al., COLING 2025)

ACL

• Ziyang Huang, Jun Zhao, and Kang Liu. 2025. Towards Adaptive Mechanism Activation in Language Agent. In Proceedings of the 31st International Conference on Computational Linguistics, pages 2867–2885, Abu Dhabi, UAE. Association for Computational Linguistics.