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Non-Collaborative User Simulators for Tool Agents

Jeonghoon Shim, Woojung Song, Cheyon Jin, Seungwon KooK, Yohan Jo · Sep 27, 2025 · Citations: 0

How to use this paper page

Coverage: Stale

Use this page to decide whether the paper is strong enough to influence an eval design. It summarizes the abstract plus available structured metadata. If the signal is thin, use it as background context and compare it against stronger hub pages before making protocol choices.

Best use

Background context only

Metadata: Stale

Trust level

Provisional

Signals: Stale

What still needs checking

Structured extraction is still processing; current fields are metadata-first.

Signal confidence unavailable

Abstract

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.

Use caution before copying this protocol

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Structured extraction is still processing; current fields are metadata-first.

HFEPX Relevance Assessment

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

Structured extraction is still processing; current fields are metadata-first.

Trust level

Provisional

Eval-Fit Score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Provisional (processing)

Extraction confidence: Provisional

What This Page Found In The Paper

Each field below shows whether the signal looked explicit, partial, or missing in the available metadata. Use this to judge what is safe to trust directly and what still needs full-paper validation.

Human Feedback Types

provisional

None explicit

Confidence: Provisional Best-effort inference

No explicit feedback protocol extracted.

Evidence snippet: Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Evaluation Modes

provisional

Simulation environment

Confidence: Provisional Best-effort inference

Includes extracted eval setup.

Evidence snippet: Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Quality Controls

provisional

Not reported

Confidence: Provisional Best-effort inference

No explicit QC controls found.

Evidence snippet: Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Benchmarks / Datasets

provisional

Not extracted

Confidence: Provisional Best-effort inference

No benchmark anchors detected.

Evidence snippet: Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Reported Metrics

provisional

Not extracted

Confidence: Provisional Best-effort inference

No metric anchors detected.

Evidence snippet: Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Rater Population

provisional

Unknown

Confidence: Provisional Best-effort inference

Rater source not explicitly reported.

Evidence snippet: Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Human Data Lens

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Lens

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: Simulation environment
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

Tool agents interact with users through multi-turn dialogues to accomplish various tasks.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

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