@inproceedings{qian-etal-2025-smart,
title = "{SMART}: Self-Aware Agent for Tool Overuse Mitigation",
author = {Qian, Cheng and
Acikgoz, Emre Can and
Wang, Hongru and
Chen, Xiusi and
Sil, Avirup and
Hakkani-T{\"u}r, Dilek and
Tur, Gokhan and
Ji, Heng},
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.239/",
doi = "10.18653/v1/2025.findings-acl.239",
pages = "4604--4621",
ISBN = "979-8-89176-256-5",
abstract = "Current Large Language Model (LLM) agents demonstrate strong reasoning and tool use capabilities, but often lack self-awareness, failing to balance these approaches effectively. This imbalance leads to \textbf{Tool Overuse}, where models unnecessarily rely on external tools for tasks solvable with parametric knowledge, increasing computational overhead. Inspired by human metacognition, we introduce \textbf{SMART} (Strategic Model-Aware Reasoning with Tools), a paradigm that enhances an agent{'}s self-awareness to optimize task handling and reduce tool overuse. To support this paradigm, we introduce \textbf{SMART-ER}, a dataset spanning three domains, where reasoning alternates between parametric knowledge and tool-dependent steps, with each step enriched by rationales explaining when tools are necessary. Through supervised training, we develop \textbf{SMARTAgent}, a family of models that dynamically balance parametric knowledge and tool use. Evaluations show that SMARTAgent reduces tool use by 24{\%} while improving performance by over 37{\%}, enabling 7B-scale models to match its 70B counterpart and GPT-4. Additionally, SMARTAgent generalizes to out-of-distribution test data like GSM8K and MINTQA, maintaining accuracy with just one-fifth the tool calls. These highlight the potential of strategic tool use to enhance reasoning, mitigate overuse, and bridge the gap between model size and performance, advancing intelligent and resource-efficient agent designs."
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<abstract>Current Large Language Model (LLM) agents demonstrate strong reasoning and tool use capabilities, but often lack self-awareness, failing to balance these approaches effectively. This imbalance leads to Tool Overuse, where models unnecessarily rely on external tools for tasks solvable with parametric knowledge, increasing computational overhead. Inspired by human metacognition, we introduce SMART (Strategic Model-Aware Reasoning with Tools), a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse. To support this paradigm, we introduce SMART-ER, a dataset spanning three domains, where reasoning alternates between parametric knowledge and tool-dependent steps, with each step enriched by rationales explaining when tools are necessary. Through supervised training, we develop SMARTAgent, a family of models that dynamically balance parametric knowledge and tool use. Evaluations show that SMARTAgent reduces tool use by 24% while improving performance by over 37%, enabling 7B-scale models to match its 70B counterpart and GPT-4. Additionally, SMARTAgent generalizes to out-of-distribution test data like GSM8K and MINTQA, maintaining accuracy with just one-fifth the tool calls. These highlight the potential of strategic tool use to enhance reasoning, mitigate overuse, and bridge the gap between model size and performance, advancing intelligent and resource-efficient agent designs.</abstract>
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%0 Conference Proceedings
%T SMART: Self-Aware Agent for Tool Overuse Mitigation
%A Qian, Cheng
%A Acikgoz, Emre Can
%A Wang, Hongru
%A Chen, Xiusi
%A Sil, Avirup
%A Hakkani-Tür, Dilek
%A Tur, Gokhan
%A Ji, Heng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F qian-etal-2025-smart
%X Current Large Language Model (LLM) agents demonstrate strong reasoning and tool use capabilities, but often lack self-awareness, failing to balance these approaches effectively. This imbalance leads to Tool Overuse, where models unnecessarily rely on external tools for tasks solvable with parametric knowledge, increasing computational overhead. Inspired by human metacognition, we introduce SMART (Strategic Model-Aware Reasoning with Tools), a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse. To support this paradigm, we introduce SMART-ER, a dataset spanning three domains, where reasoning alternates between parametric knowledge and tool-dependent steps, with each step enriched by rationales explaining when tools are necessary. Through supervised training, we develop SMARTAgent, a family of models that dynamically balance parametric knowledge and tool use. Evaluations show that SMARTAgent reduces tool use by 24% while improving performance by over 37%, enabling 7B-scale models to match its 70B counterpart and GPT-4. Additionally, SMARTAgent generalizes to out-of-distribution test data like GSM8K and MINTQA, maintaining accuracy with just one-fifth the tool calls. These highlight the potential of strategic tool use to enhance reasoning, mitigate overuse, and bridge the gap between model size and performance, advancing intelligent and resource-efficient agent designs.
%R 10.18653/v1/2025.findings-acl.239
%U https://aclanthology.org/2025.findings-acl.239/
%U https://doi.org/10.18653/v1/2025.findings-acl.239
%P 4604-4621
Markdown (Informal)
[SMART: Self-Aware Agent for Tool Overuse Mitigation](https://aclanthology.org/2025.findings-acl.239/) (Qian et al., Findings 2025)
ACL
- Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-Tür, Gokhan Tur, and Heng Ji. 2025. SMART: Self-Aware Agent for Tool Overuse Mitigation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4604–4621, Vienna, Austria. Association for Computational Linguistics.