# Non-Collaborative User Simulators for Tool Agents

arXiv: https://arxiv.org/abs/2509.23124 · HTML: https://mlsanity.com/paper/2509.23124/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 73 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 20 of 20)

- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://mlsanity.com/paper/2407.18901/) (2024)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback](https://mlsanity.com/paper/2309.10691/) (2023)
- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks](https://mlsanity.com/paper/2503.15478/) (2025)
- [ToolTalk: Evaluating Tool-Usage in a Conversational Setting](https://mlsanity.com/paper/2311.10775/) (2023)
- [ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models](https://mlsanity.com/paper/2503.00564/) (2025)
- [Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2402.13374/) (2024)
- [Goal Alignment in LLM-Based User Simulators for Conversational AI](https://mlsanity.com/paper/2507.20152/) (2025)
- [MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations](https://mlsanity.com/paper/1908.10023/) (2019)
- [Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy](https://mlsanity.com/paper/2204.07433/) (2022)

## Built on by (most cited first, 18 of 18)

- [Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents](https://mlsanity.com/paper/2601.20144/) (2026)
- [Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants](https://mlsanity.com/paper/2605.09808/) (2026)
- [Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents](https://mlsanity.com/paper/2605.12894/) (2026)
- [Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models](https://mlsanity.com/paper/2510.07248/) (2025)
- [One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL](https://mlsanity.com/paper/2608.12253/) (2026)
- [Efficient Agent Evaluation via Diversity-Guided User Simulation](https://mlsanity.com/paper/2604.21480/) (2026)
- [SWE-Touch: Benchmarking Coding Agents When Users Touch the Code](https://mlsanity.com/paper/2608.02499/) (2026)
- [Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding](https://mlsanity.com/paper/2510.18289/) (2025)
- [SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science](https://mlsanity.com/paper/2605.18630/) (2026)
- [SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue](https://mlsanity.com/paper/2603.16783/) (2026)
- [Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions](https://mlsanity.com/paper/2606.03318/) (2026)
- [Agents Trust Tools Too Much: Measuring Reliance on Unreliable Tools](https://mlsanity.com/paper/2609.05587/) (2026)
- [When the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented Dialogue](https://mlsanity.com/paper/2606.31307/) (2026)
- [HoosierHelp: Benchmarking LLM Agents for Social Service Navigation](https://mlsanity.com/paper/2608.09946/) (2026)
- ["Act Like a 5th Grader" is Not Enough: Bounding Knowledge in LLM-Based User Simulators](https://mlsanity.com/paper/2608.30033/) (2026)
- [Investigating Assistant Bias in LLM User Simulators Using a Role Vector](https://mlsanity.com/paper/2609.00608/) (2026)
- [MIMESIS: Learning User Simulators as Training Environments for Interactive Agents](https://mlsanity.com/paper/2610.09484/) (2026)
- [Uncovering Robustness Weaknesses in LLM Agents via Persona Evolution with Task-Integrity Constraints](https://mlsanity.com/paper/2610.16155/) (2026)

## Abstract (arXiv)

Tool agents interact with users through multi-turn dialogues to accomplish various tasks. Recent studies have adopted user simulation methods to develop these agents in multi-turn settings. However, existing user simulators tend to be agent-friendly, exhibiting only cooperative behaviors, failing to train and test agents against non-collaborative users in the real world. We propose a novel user simulator architecture that simulates four categories of non-collaborative behaviors: requesting unavailable services, digressing into tangential conversations, expressing impatience, and providing incomplete utterances. Our user simulator can simulate challenging and natural non-collaborative behaviors while reliably delivering all intents and information necessary to accomplish the task. Our experiments on MultiWOZ and τ-bench reveal significant performance degradation in state-of-the-art tool agents when encountering non-collaborative users, as well as agent weaknesses under each non-collaborative condition such as escalated hallucinations and dialogue breakdowns. Our findings point to the need for methods that can improve agent robustness to the wide range of user behaviors encountered in deployment. We release the extensible simulation framework to help the community develop and stress-test tool agents under realistic conditions within their own service domains. Our code is available at https://github.com/holi-lab/NCUser.