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
Published: 2025-08-28 15:57:33

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.