# How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on $\tau$-bench

## TL;DR

This paper introduces IRMA, a framework that reformulates user inputs with domain rules and tool suggestions, significantly improving tool usage accuracy of large language models in complex, dynamic environments like $ au$-bench.

## Contribution

The paper presents IRMA, an automatic input reformulation method that enhances LLM tool use accuracy in multi-turn, dynamic environments, outperforming existing approaches.

## Key findings

- IRMA outperforms ReAct, Function Calling, and Self-Reflection in overall pass scores.
- Input reformulation improves reasoning and decision-making in LLM agents.
- The approach increases reliability and consistency in tool usage in complex environments.

## Abstract

Recent advances in reasoning and planning capabilities of large language models (LLMs) have enabled their potential as autonomous agents capable of tool use in dynamic environments. However, in multi-turn conversational environments like $\tau$-bench, these agents often struggle with consistent reasoning, adherence to domain-specific policies, and extracting correct information over a long horizon of tool-calls and conversation. To capture and mitigate these failures, we conduct a comprehensive manual analysis of the common errors occurring in the conversation trajectories. We then experiment with reformulations of inputs to the tool-calling agent for improvement in agent decision making. Finally, we propose the Input-Reformulation Multi-Agent (IRMA) framework, which automatically reformulates user queries augmented with relevant domain rules and tool suggestions for the tool-calling agent to focus on. The results show that IRMA significantly outperforms ReAct, Function Calling, and Self-Reflection by 16.1%, 12.7%, and 19.1%, respectively, in overall pass^5 scores. These findings highlight the superior reliability and consistency of IRMA compared to other methods in dynamic environments.