How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on tau-bench (2025)

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.

## introduction recent advancements in large language models (llms) @xcite have created the potential for them to be used as autonomous agents in complex real-world tasks like travel-booking, customer-support, and enterprise operations @xcite @xcite . however, such complex tasks require the need of reasoning and planning capabilities beyond just language processing: they require the ability on behalf of these agents to be able to invoke suitable tools 1 which * equal contribution 1 the terms 'tool-use', 'tool-calling' and 'function-calling' are used interchangeably in this paper figure 1 : comparison of the irma framework with other methods. the input reformulation framework takes the user query, domain policies, and available tools to generate a structured input for the assistant agent. this augmented input enables the assistant to make more accurate decisions from the first turn. figure 2 : overview of the tasks conducted for evaluating and improving tool-calling capabilities of language agents in τ -bench @xcite . stage 1) involved human evaluators manually evaluating simulated conversation trajectories to find common failure modes of the language agents. stage 2) employs a human-in-the-loop approach to experiment with various prompt reformulations to improve agent correctness. stage 3) automates this process through the irma framework, which leads to improved agent behavior. these shortcomings underscore the need for more fine-grained evaluations and methods that can diagnose and address the nuanced failure modes of llm agents in complex, real-world interactions that employ natural language as a form of communication. thus, our main focus in this work is to find and mitigate the causes of why language agents fail to solve simulations of real-world conversational requests that require complex reasoning and relevant information processing according to the situation at hand. to this end, we utilize τbench @xcite as an appropriate test-bed for such investigation as it emulates realistic airline and retail dialogues. we define the reasoning about actions of language agents as the ability to generate context-aware inference and decision-making tokens for selecting the next best action (a tool-call in this context). additionally, we define and evaluate the planning capabilities of the agents through decision-making for tool-calling over multiple toolcalls in the correct sequential manner to complete a goal. inspired by recent work in context engineering @xcite , we propose a three-pronged sequential approach. first, we develop a comprehensive error classification that categorizes common reasoning and planning mistakes in a multiturn tool-calling simulation. this taxonomy serves as a diagnostic guideline to systematically identify and understand the causes of failures for llm agents. second, we manually experiment with input reformulations of the user requests to evaluate whether the correct prompt reformula-tions can guide the tool-calling agents towards correct decision-making through appropriate toolcalling/response to the user. third, we automate this prompt-reformulation process by building a multi-agent llm framework ( §5.2), called input-reformulation multi-agent (irma), which further optimizes the input reformulation with augmentation of follow-up questions ( §5.1). before the tool-calling agent invokes or responds to any tool output, our automated framework supplies targeted guidance that ensures strict adherence to domain-specific rules and well-placed follow-up questions to extract accurate information, thereby enhancing its reasoning and planning capabilities in dynamic environments. our results show that the irma framework not only outperforms react @xcite , function calling, and self-reflection (renze and guven, 2024) on pass@1, but also achieves 20% and 22.4% higher accuracy on airline tasks compared to gemini 1.5 pro-fc and claude 3.5 haiku-fc, respectively. irma also demonstrates stronger reliability, with higher scores on pass^4 and pass^5 (figure 4 ). in addition, irma solves tasks in fewer turns than competing methods, highlighting its efficiency (figure 6 ). lastly, irma shows greater robustness, with an increased performance gap on pass^5 after removing tasks affected by ground truth and instruction errors in the airline and retail domains. the main contributions of our work are: tool-use conversational benchmark. 2. we propose the input-reformulation multi-agent framework (irma), a verificationloop-free approach that improves functioncalling agents by reformulating prompts with structured and contextually relevant information. irma guides the agent to better follow domain policies by enriching its input with key constraints and tool-related context. 3. we perform an in-depth evaluation of irma's performance across reliability, consistency, and accuracy. furthermore, our analysis of efficiency reveals that irma is able to solve tasks using fewer interaction turns than competing methods. tool-integration for llms the react framework, introduced by @xcite , is one of the first approaches to explore the potential of large language models (llms) as tool-using agents by integrating reasoning and acting within llms. toolformer @xcite presents a finetuning approach to teach llms to invoke tool calls. toolevo @xcite and toolllm @xcite employ tree search algorithms for integrating and evaluating tool-learning capabilities in llms. toolace @xcite , auto-tools @xcite , and apigen @xcite introduce automated frameworks designed to generate accurate, complex, and high-quality tool-learning data, with works like @xcite extending this to multi-turn interactive conversational settings. tool-use benchmarks llms have been extensively evaluated on invoking external functions in both single-turn and interactive multi-turn conversational test beds. @xcite and api-bank @xcite are two prominent benchmarks designed to evaluate the functioncalling capabilities of llms in single-turn scenarios. nestful @xcite focuses on evaluating llms' ability to handle nested sequences of api calls. toolqa-d @xcite gauges robustness in changing api specifications. τ -bench @xcite and toolsandbox @xcite emulate realistic dialogues requiring policy-compliant tool use over multi-turn useragent interactions, where each step modifies an external environment. while these existing multiturn benchmarks evaluate the overall success of tool-calling agents, they lack fine-grained analysis of reasoning errors while following complex domain rules-a gap our work addresses through the construction of a fine-grained error classification by evaluating τ -bench. improving llm tool-use recent research has explored diverse strategies to enhance the tool-use capabilities of llms, focusing on api calling and web-environment interaction-by leveraging techniques such as synthetic data generation, reinforcement learning, and memory augmentation. liu et al. (2024c) introduces apigen, an automated pipeline that generates high-quality, verifiable single-turn function-calling datasets, enabling small models to outperform gpt-4 on the bfcl (patil et al., 2025). apigen-mt (prabhakar et al., 2025) extends the framework to show improvement in models on multi-turn scenarios through blueprintdriven simulation of human-agent dialogues. re-tool (feng et al., 2025) integrates dynamic code execution within the reasoning process and training via outcome-driven rl, which significantly improves multi-step reasoning. nemotron-tool-n1 (zhang et al., 2025) uses an rl framework to teach precise tool invocation and explicit reasoning, achieving state-of-the-art on api-bank (li et al., 2023) and bfcl. artist (singh et al., 2025) integrates agentic reasoning with rl, enabling llms to decide autonomously when and how to call tools. memento (zhou et al., 2025) employs a memoryaugmented, case-based planner for continual adaptation without retraining, achieving strong generalization on gaia @xcite and deepresearcher @xcite benchmarks. while these works mark a shift toward adaptive, planningdriven, and memory-augmented llm agents by leveraging training methods, our proposed irma framework explores tool-use improvement from the perspective of context engineering @xcite principles. ## problem statement to evaluate the tool-usage capabilities of current large language models (llms), we adopt the benchmark provided by τ -bench @xcite . this benchmark is specifically designed to assess language agents in realistic, multi-turn interaction settings. τ -bench includes tasks from two domains: a key challenge in τ -bench arises from the dynamic nature of user-agent interactions, where both user inputs and agent responses can vary across runs. this variability requires the agent to consistently execute correct action sequences, regardless of the conversational path. however, current results indicate that even state-of-the-art llms struggle to reliably complete these tasks as the number of trials increases. to address this limitation, we conduct a root-cause analysis of common agent errors ( §4) and introduced irma, a multi-agent framework ( §5) designed to improve agent reliability in this challenging setting. ## error-classification to identify the failure modes of llms, human evaluators conducted experiments using gpt-4o @xcite as the base model for both the user and the assistant agent across all tasks in τbench @xcite . both react and functioncalling agent configurations were used to generate up to five trials per task in each domain. evaluators manually reviewed the resulting multi-turn conversation trajectories from the retail and airline domains. while prior studies @xcite @xcite have examined failures related to tool availability, definition errors, or tool set complexity, our analysis focuses specifically on the contextual reasoning limitations of llms in generating tool calls within dynamic, multi-turn interactions. although τ -bench provides a general taxonomy of failure types for the retail domain, our classification is more cause-oriented than effect-oriented. by framing errors in terms of their underlying causes, we can more effectively inform the design of targeted interventions, such as retrieval-augmented memory to mitigate context retention issues or follow-up question generation ( §5.1) to reduce hallucinations from context drift. the following subsections ( §4.1- §4.4) provide a detailed breakdown of the identified error types. ## method as outlined in the previous sections, complex dynamic environments such as τ -bench present reliability challenges. specifically, the user simulator may hallucinate during interactions, generating questions that do not adhere to the provided instructions. in this study, we aim to improve the assistant agent's tool-calling performance in τ -bench by enabling more accurate decision-making. unlike prior approaches that monitor and correct agent actions through verification or reflection, our method focuses on enhancing the quality of the agent's input before any action is taken. to achieve this, we first introduce a novel prompting strategy: follow-up question acting (fact), designed to support decision-making in dynamic settings. we then present the input reformulation multi-agent (irma) framework that reformulates the agent's input to guide more effective and context-aware decisions. ## conclusion in this work, we investigate the limitations of stateof-the-art llm-based tool-calling agents in complex, multi-turn environments, focusing on the retail and airline domains of τ -bench. through a detailed analysis of conversation trajectories, we identify four major failure modes: user instruction hallucination, agent hallucination, domain-policy violations, and contextual misinterpretation, all of which stem from limitations in memory retention, contextual reasoning, and adherence to domain constraints across extended interactions. to address these challenges, we propose the input reformulation multi-agent (irma) framework, designed to enhance the structure of the assistant agent's input. our results show that irma not only outperforms other methods in pass^1 but also demonstrates significantly higher reliability, achieving an overall score of 43% pass^5 in τ -bench. moreover, by leveraging the fact agent, irma exhibits greater efficiency in task completion. in conclusion, irma shows robust and consistent behavior in the unreliable and dynamic environment of τ -bench, highlighting its effectiveness in real-world tool-use scenarios.