How Can Input Reformulation Improve Tool Usage Accuracy in a Complex
  Dynamic Environment? A Study on τ-bench
Authors: name: Venkatesh Mishra, name: Amir Saeidi, name: Satyam Raj, name: Mutsumi Nakamura, name: Jayanth Srinivasa, name: Gaowen Liu, name: Ali Payani, name: Chitta Baral

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.