EcoAct: Economic Agent Determines When to Register What Action
November 03, 2024 Β· Declared Dead Β· π arXiv.org
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Authors
Shaokun Zhang, Jieyu Zhang, Dujian Ding, Mirian Hipolito Garcia, Ankur Mallick, Daniel Madrigal, Menglin Xia, Victor RΓΌhle, Qingyun Wu, Chi Wang
arXiv ID
2411.01643
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CL
Citations
11
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Recent advancements have enabled Large Language Models (LLMs) to function as agents that can perform actions using external tools. This requires registering, i.e., integrating tool information into the LLM context prior to taking actions. Current methods indiscriminately incorporate all candidate tools into the agent's context and retain them across multiple reasoning steps. This process remains opaque to LLM agents and is not integrated into their reasoning procedures, leading to inefficiencies due to increased context length from irrelevant tools. To address this, we introduce EcoAct, a tool using algorithm that allows LLMs to selectively register tools as needed, optimizing context use. By integrating the tool registration process into the reasoning procedure, EcoAct reduces computational costs by over 50% in multiple steps reasoning tasks while maintaining performance, as demonstrated through extensive experiments. Moreover, it can be plugged into any reasoning pipeline with only minor modifications to the prompt, making it applicable to LLM agents now and future.
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