SMART: Self-Aware Agent for Tool Overuse Mitigation
Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-T\"ur, Gokhan Tur, Heng Ji

TL;DR
This paper introduces SMART, a self-aware reasoning paradigm for LLM agents that reduces tool overuse by balancing parametric knowledge and external tool reliance, leading to improved efficiency and performance.
Contribution
We propose SMART, a novel self-awareness framework for LLM agents, along with SMART-ER dataset and SMARTAgent models, to optimize tool use and enhance reasoning capabilities.
Findings
SMARTAgent reduces tool use by 24%.
Performance improves by over 37% with SMART.
Models match larger counterparts and generalize well.
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…
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Taxonomy
TopicsSecurity and Verification in Computing
