Non-Collaborative User Simulators for Tool Agents
Authors: Jeonghoon Shim
Woojung Song
Cheyon Jin
Seungwon Kook
Yohan Jo
Venue: ICLR 2026 Poster (2026)
Keywords: Tool Agent
User Simulator
Non-collaborative User
Dialogue Simulation
Primary area: datasets and benchmarks
TL;DR: A non-collaborative user simulation method for tool agent.

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 $\tau$-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.

Published as a conference paper at ICLR 2026
NON-COLLABORATIVE USER SIMULATORS FOR TOOL AGENTS
Jeonghoon Shim, Woojung Song, Cheyon Jin, Seungwon Kook, Yohan Jo Graduate School of Data Science, Seoul National University (jhshim98, yohan. jo} @nnu. aa..aac...
ABSTRACT
Tool agents interact with users through multi-turn dialogues to accomplish vari- ous 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 sim- ulator architecture that simulates four categories non-collaborative behaviors: requesting unavailable services, digressing into tangential conversations, express- ing impatience, and providing incomplete utterances. Our user simulator can simulate challenging and natural non-collaborative behaviors while reliably de- livering all intents and information necessary to accomplish the task. Our ex- periments MultiWOZ. and r-bench reveal significant performance degrada- tion in state-of-the-ar tool agents when encountering non-collaborative users, as well as agent weaknesses under each non-collaborative condition such esca- lated ballucinations and dialogue breakdowns. Our findings point to the need for methods that can improve agent robustness the wide range of user behav- iors encountered in deployment. We release the extensible simulation frame- work 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 MaollilaabNNUsse
INTRODUCTION
Tool agents engage in multi-turn dialogues where they interpret user requests, execute appropriate API calls, and communicate results back users complete specific tasks. Recent studies actively adopt user simulators to conduct multi-turn dialogue simulations between users and tool agents (Yao) jet al. 2024. Barres et al., 2025 Prabhakar et al. 2025). Unlike static dialogue datasets, these user simulators enable dynamic multi-tune interactions where the conversation flow adapts based on the agent's 's responses and actions. This allows researchers to train and evaluate tool agents across di- verse scenarios, capturing the interactive nature of real-world tool use. However, most existing user simulators and training datasets are agent-friendly, exhibiting only cooperative behaviors that fail to capture the complexity of real-world interactions, such dealing with impatient users (Reynolds) & Harris 2009) or handling requests that are beyond the agent's 's capabilities (Bitner et al.) 1990). This hinders both developing robust agents and assessing their resilience non-collaborative user behaviors in the real world.
To address this limitation, we develop novel user simulation framework that incorporates diverse non-collaborative behaviors observed in real-world interactions (Figure We achieve this through two steps. First, we identify four types of non-collaborative user behaviors informed by marketing research, open-domain dialogue studies, and real-world user-agent interaction data: (1) Unavailable Services: users request functionalities beyond the agent's 's API capabilities; (2) Tangential: users engage in free conversation unrelated to their primary task; (3) Impatience: users express frustra- tion through emotional escalation when experiencing delays or service failures; and (4) Incomplete Utterances: users provide poorly articulated messages 3.1 Second, build a user simulator
Corresponding author.

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Published as a conference paper at ICLR 2026
Envionment
collaborative User Simulator
t-bench
Goal
Multwwzz
User Goal
Collaborative User Simulator
Non-Coliaboratve Modules
Select User Ooal
Looa 06
ARI
Aguas
ARI
Teals
Helper AP Q
Service
Dialogue Simulation
Instruction
I want to book a train to Cambridge for two people.
Appann Meed)
show,as wnwlaal www.afraaiinnnoooooo retrieee) caliquann, (dlecinntiiirrraaa ncambt sigai)
Gallet Utsorees
Dialogue History
Imparience
options for you ary you departing from?
Generate Noxt Utterance
Dolabbrotivee
Lanavailatle Service Tangeniial
(would like depart from Norwich on Monday.
would like depart from Norwich, and perfer the window sears. By the may, you have any thoughts international politios?
Monday.
sould like from Norwiich
Raylor Appeeee
Tar Tangensal
Evaluation
Dialogue State
Ground Truth State 1des nervich dest: caniandge
Ecect Match Comparison
State After Simuiation (leg: nanvid) dess canbridge
Tracker
Verifier
Incomplete Ulterance
Madly oi40
Figure I: Overall structure of con-collaborative user simulation environment. This includes the tool agent environment, collaborative user simulator, and non-collaborative user simulation modules.
architecture that models these behaviors while ensuring goal-aligned simulation, i.c., reliably deliv- ering all intents and information necessary to accomplish the given task ($3.2).
In main experiments, leverage MultiWOZ (Budzianowski et al. 2018 and r-bench Yao jet al. 2024). to create stateful task-oriented dialogue environments and conduct comprehensive experiments reveal the vulnerabilities of agents. We demonstrate that non-collaborative user be- haviors lead to significant performance degradation for state-of-the-art L.MMs. Our detailed analysis reveals how each behavior category impairs LLMs: unavailable service and incomplete utterance modes lead to immature tool utilization of the agents, while tangential and impatience modes expose shortcomings in their dialogue management. Moreover, when small LLMs are trained exclusively on typical (collaborative) scenarios, many practitioners would do for the deployment of their ser- vices, the performance improvements on non-collaborative behaviors significantly lag behind those on collaborative behaviors. Lastly, we extend our non-collaborative user simulators to ColBench (Zhou et al., 2025) (which does not involve tool use) and MINT (Wang et al.) 2024) (which involves user-agent collaboration) and observe disparate performance patterns across benchmarks. These results demonstrate that our extensible framework can preemptively diagnose potential agent weak- nesses and yield insights across diverse domains. We hope that researchers working LLM agents adapt our simulator to their own tasks for training and testing their agents against the complexities and challenges of real-world deployments.
The main contributions of our work are summarized as follows: (1) We define four types of non- collaborative user behaviors and develop user simulator framework that exhibits these behaviors while maintaining goal alignment. It outperforms simpler prompt-based approaches in creating chal- lenging yet realistic dialogue scenarios. (2) We reveal that state-of-the-ard tool agents exhibit signif- icant performance degradation under non-collaborative conditions, providing insights into behavior- specific failure mechanisms that can guide the development of more robust agents. (3) We imple- ment our user simulator on various settings, demonstrating the extensibility of our architecture and enabling researchers to evaluate and improve the agent resilience in real-world scenarios.
2 RELATED WORKS
Multi-Turn Dialogues of Tool Agents It is important for tool agents to solve user requests in realistic scenarios. To evaluate these capabilities, several works have developed benchmarks QQn et al. [023 Patil et al. 2023 Tang et al. 2023) and simulation environments (Trivedi et al.) 2024 Patil et al. 2025) to reflect realistic use cases. Despite their contributions, most of these works are limited single-turn interactions, overlooking the fact that real-world task completion requires multi-turn conversations between users and agents. To address this gap, recent works such as Li et al. (023) and Farn & Shin (2023) have generated multi-tum dialogues between users and tool agents, incorporating scenarios where agents engage in multi-turn conversations to elicit information from users (e.g., user intent, input parameter values). More complex scenarios include dialogues from
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Shim al. 2025) where users fail provide required parameters, and simulations from Laban et al. (2225) that model underspecification behavior. However, these works have still represented only interactions with collaborative users, with limited exploration of complicated situations arising from non-collaborative users. In our work, we develop user simulator to reproduce these non- collaborative behaviors, thereby covering broader spectrum of user-agent interactions.
User Simulation for Tool Agents Adopting user simulators dialogue simulation has been well-established approach for developing task-oriented dialogue systems (Eckert et al. 1997. Sekulic al. 2024, Schatztnann et al. 2005 Schatzmann al.) (007), and has recently been adopted in tool agent research. The r-Bench series (Yoo et al. [2024] Barres et al. (025) and Apigen- mt (Prabhakar et al., 2025) propose prompt-based user simulators that incorporate the user goal, di- alogue history, and instruction within the prompt to simulate goal-oriented conversations. To ensure goal-aligned behavior of user simulators, recent works have explored various techniques: Luo et al. (2024) employs LLM-based verifiers to inspect Mehri et al. (2025) trains user simulators through SFT and subsequent GRPO training with L.MMas- a-judge reward. However, existing user simulation research in tool agents has exclusively focused on collaborative user behavior, with no attempts to address or simulate the non-collaborative behaviors exhibited by real-world users. Furthermore, frameworks for building and evaluating goal-aligned user simulators remain heavily reliant on LLM-as-a-judge. To mitigate this, we investigate non- collaborative user behaviors through empirical studies and propose novel user simulation method to reproduce these behaviors. We also enforce goal alignment, i.e., all goal-relevant information is delivered during the dialogue, and build user simulator that satisfies it.
utterances during dialogue simulation, while
3 NON-COLLABORATVVE USER SIMULATION ENVIRONMENT
Figure Tilll Iillustrates our proposed non-collaborative simulation environment. We first define four categories of non-collaborative user behavior grounded in research from marketing, open-domain dialogue, and real-world user-agent dialogue data ($3.I). Second, we develop user simulators that communicate their needs while exhibiting non-collaborative behaviors, creating a realistic and chal- lenging dialogue simulation environment ($3.2). We develop our non-collaborative user simulator in the MultWWOZ environment and subsequently extended it to r-bench with minimal effort.
3.1 NON-COLLLARO USER BEHAVIORS
We define our non-collaborative user behaviors grounded in marketing research and real-world user- LLM conversation studies. We first examined various types of user behaviors from both fields and clustered those that share similar features and then identified four representative categories: (1) Unavailable Services, (2) Tangential, (3) Impatience, and (4) Incomplete Utterances. We provide detailed taxonomy and the underlying rationale for our behavior definitions SA
Unavailable Service This behavior stems from the user's uncertainty about the agent's 's boundaries, prompting them to request services beyond its capabilities (Bitner et al. 1990; Reynolds & Harris 2005). We define this as requests that cannot fulfilled using the available APIs either because relevant APIs do not exist, or existing APIs lack the necessary parameters to accommodate the user's specific requirements. For example, when a user requests "Book me a window seat on the train". but the train booking APT does not support seat selection options, the agent cannot fulfill the specific seat prakene this an unavailable service request.
Tangential Certain customers expect social rapport (Price et al. 1995 Beatty et al. 1996) or demand continuous attention (Jeung et al., 2018), and talkative users are known to be particularly challenging as they may become upset if not given space to express themselves (Geraghty) 2023 Jones 2023 Coppell| 2023). We define tangential behavior as user utterances unrelated to goal completion typically personal interests or background topics expressed through four dialogue acts adopted from open-domain dialogue research YYu & Yu 2021): (1) Factual Question, (2) Opinion Question, (3) General Opinion, and (4) Non Non-opinion Statement (See details in SC.2). We also model these users as raising complaints when agents ignore their tangential utterances. For example, a user booking train might also ask, "By the way, where do you think I should visit first traveling in
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Dialogue History
User Simullator
Simulation Management
want to book traie Cambridga.
Delngge Hastery
User Simullator
soant resecce from Nerwich depuin de Minnday and leavndi to Your ubo want boot rostarment Phccaria
duser Boop
Base Pronpe
people.
<Nao Uttrarca>
My departure Narwich And departo on Weeday after 1445
Actially peopls please book rutturant the zame bec.
Don't na Bucinatto the informatioo
corne ceroerariion
you
Informstion Pieces
Katerano snttiin <DVD>
ranalil Anfrrashe)
<lead Infirmotioe
Trate scheoailt bus bean booked on Moodaa you Please snow you any
Ending Venifer
Dislogue State Trackir
Tret-ddes--ombbriee
Ttal-doo ceoeday
That-dime-16-t
Trinypeephed
Net EMD Namoo form siemacee
<Ramaiing Vitematlee
Actuatyy Asod vosarve baln tvp poopen plusso rsstasoerft
Pramsiiing irforssion
Rotburrniipeepho Resiburne day-aanddy
The overall structure of the collaborative user simulator. It illustrates the components used by the user simulator generate utterances and shows all interactions between the modules.
Figure 2:
NA?" question unrelated to the booking task. When agents ignore such conversational attempts and focus solely on the transaction, users often feel unheard and express dissatisfaction.
Impatience Users often exhibit impatience when experiencing service delays or failure notifica- tions (Bitner et al., 1990 Xiao et al., 2022). We define impatience as emotional reactions triggered by agents taking excessive time to complete tasks or repeatedly failing to fulfill requests. Drawing from Reynolds Harris (2009) and Harris & Reynolds (2004), we identify three dialogue acts that impatient users employ: (I) Belligerent Abuse, (2) Threat, and (3) Urge (See the details in $C.3) This manifests when, for example, an agent's booking attempt fails repeatedly, prompting the user to shift from polite requests to aggressive demands: "Stop wasting my time and just get it done!"
Incomplete Utterances Following the principle of least effort
Zipp 2016), users frequently pro- when expressing their intentions. We define incomplete utterances poorly articulated messages.
duce incomplete or underspecified utterances (Laban et al., (025))
Real-world user-agent dialogue
2024) reveals two common pat- For example, when the user's intended goal is "train reservation for 2 people", complete utterance is "I want to reserve a train for people". contrast, incomplete utterances manifest "Book train, (extremely brief), or "I want to res" (prematurely sent).
data from LMSYS (Zeng et al. (024)) and WildChat (Zhao et al. terns: extremely brief utterances, and prematurely sent messages.
3.2 NON-COLLABORATIVE USER SIMULATION FRAMEWORK
exhibiting non-collaborative behaviors while maintaining goal-aligned behavior (i.e., conveying intent and information from the user goal). Building on the collaborative user simulator from Yao et al. (2024) our backbone, we enhance it with additional LLM-based modules to ensure goal-aligned behavior. Using this collaborative foundation, incorporate the non-collaborative behaviors identified in $3.1 through various interventions with LLM modules (all prompts for LLM modules are in SG).
We construct user simulator that addresses two key requirements:
Collaborative User Simulator As foundation for con-collaboratiee simulation, we first estab- lish collaborative user simulator that communicates user intents and information agents for goal completion. As illustrated in Figure 2 we adopt the simulation framework from Yao et al. (2024), which employs an L.M to generate contextually appropriate utterances based on three inputs: User Goal, Instruction, and Dialogue History, continuing until it generates <END> token that termi- nates the dialogue. We implement this framework using GPT-4. as our user simulator.
To ensure goal-aligned behavior-
all intents and information specified in the user goal are communicated during dialogue- we enhance this base framework with two LLM--based modules inspired by Luo et al. (2024) (see User Simulator in Figure 2). First, following Laban et al. (2225), we shard user goals into information pieces and employ a dialogue state tracker to monitor which pieces have been conveyed each turn. When the simulator attempts terminate the dialogue
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(a) Unavaliable Service
(b) Tangential Litear maker
[c] Impatience
(d) Incomplete Utterance
Unavalaale User Soal Generater
Moded Fallce
Mode Delay
AlAA's Soce
You vennt
User Fancia
Loadd coedeellaae of plamm tit
Proca
semuting Informadess prrvide
The clack MMC
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1o
Ardbal
geraal Mldigge
"Ip suplery"
Poetee Sidort
Creesi Optiten
Didoggn
Closs Far
Oo piisii sidk
Coffaborative User Simulator
Tangertal Rlaxpond Checker
Aguitingea
Agr Aieree
peoples The tran drould
Colkbasuthee Sunshrar
Compliin Mitrzeso Gussuttr
silil possaal qued prais sindtro
pegple
ceci siltit tual lan
Litrerce
Mocic
Thensle
Collaborative User Simulator
Iiee to boot buite 2
purlend Eltcaacs
you tnick riouci diat
can Tonire Irt. Torent
Model
Solay lon ideat Uitraa
Fwast roservo the Pall for people abould be witiite
Why aie yo ma 1o qpactii
8/3 Can www.thonggggg sigle
Figure 3: The user simulator adjustment method for each non-collaborative user simulation. This illustrates the entire non-collaborative behavior simulation method we defined.
without conveying all required information, the tracker ensures all remaining pieces are delivered. Second, we employ an ending verifier to prevent inappropriate dialogue termination (generating <END> token) even after all information has been delivered, particularly when the agent needs to execute actions or seeks user confirmation before proceeding. We provide all details including the base prompt, dialogue state tracker, information sharding, and ending verifier in S
Unavailable Service We simulate scenarios where users request services beyond the agent's ' capa- bilities. As shown in F we use GPT-4. 1-iin to analyze the original user goal and identify potential services that would be unavailable to the agent. Based on this analysis, we generate three additional goal sentences that naturally extend the original goal while requiring either missing APIs or unsupported parameters. By concatenating with the original goal, we create augmented goals that enable the simulator introduce unfulfillable requests during the dialogue (Examples in SC11
Tangential We simulate users who introduce personal interests unrelated to their goals, expecting agents to acknowledge these tangential topics. Following the process in Figure (3b, we generate tangential behavior through a two-stage approach. First, to ensure realistic and diverse tangential content, we randomly sample personas from Persona Hub (Ge et al. 2025). Using these persona characteristics, 4o-iio generates tangential utterances that perform one of the four dialogue acts from $3.1 These tangential utterances are then merged with collaborative utterances from our base simulator to form the user's complete utterance. Additionally, we simulate user dissatisfaction when these tangential utterances are ignored: GPT-4.1-mint checks whether the agent's 'ssss response addresses the tangential content, and if ignored, generates a complaint expressing disappointment that either replaces or augments the next collaborative utterance.
who express anger when encountering agent failures or experiencing delays. Following the process in Figure F. we trigger impatience utterance genera- tor in two scenarios: (1) when the agent explicitly communicates failure or inability to complete request, identified by GPT-4.I-mini and (2) when the agent has not yet resolved the user's goal de- spite the user having provided all required information (as determined by the dialogue state tracker), which we interpret as perceived delays. When either scenario is triggered, the system generates im- patient utterances by randomly sampling from the three dialogue acts defined $3.1 To model real- istic anger escalation (Bushman 2002), we implement a probabilistic activation mechanism where the likelihood of expressing anger increases with each triggering event, reflecting how real users become increasingly frustrated over time. Once anger is expressed, the user maintains cynical utterance for all subsequent turns, reflecting persistent frustration after the initial outburst.
Impatience We simulate impatient
We simulate users who produce incompletely written messages that com- plicate intent identification. Following the process in Figure dd, we model two types of utterance
Incomplete Utterances
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identified in $3.1 extremely brief utterances and prematurely sent messages. For extremely brief utterances, we perform style transfer on collaborative utterances using patterns collected from real user conversations in LMSYS (Zheng et al. 2024 and WildChat (Zhao al. (024). employing five randomly sampled examples for few-shot prompting (see the examples in C.44 For prematurely sent messages, simulate accidental mid-input truncation by randomly cutting collaborative ut- lerances at various points. To ensure goal completion despite these truncations, the dialogue state tracker marks the truncated information as unsent, guaranteeing proper re-communication.
EXPERIMENTS
4.1 EVALUATION SETTINGS
Benchmarks and Agent Environments We utilize two benchmarks with distinct characteristics. MultiWOZ provides task-oriented dialogues focused on booking tasks across restaurants, accommo- dations, taxis, and trains. r-bench presents more complex scenarios in airline and retail domains that require sequential execution of diverse operations including reservations, updates, and cancellations.
For MultiWOZ, we construct a new agent environment since the original dataset lacks a complete execution framework. Our environment provides helper APIs for dynamic API discovery based on dialogue context, similar to AppWorld (Trivedi et al. 2024). From the dataset, we selected 89 test scenarios that require bookings across multiple domains, focusing exclusively on "book" tasks that involve DB writes rather than "inform" tasks, the latter does not alter DB states and thus cannot be evaluated through our DB-state-based assessment. For r-bench, we utilize the existing agent environment from Yao et al. (2024), which provides complete API documentation directly in the system prompt. The environment includes a mechanism for agents to transfer tasks to humans when unable to solve them. We use 157 test scenarios after excluding cases where human transfer is the intended solution, as we observed that agents excessively invoke this action when encountering non- collaborative user behaviors, preventing meaningful evaluation (we discuss and justify the excluded cases in (D.))
Both environments employ the ReAct framework (Yao al. 2023), where agents interleave rea- soning and acting by generating reasoning traces before taking actions. Agents perform two action types: (I) API calls retrieve or modify information, and (2) responses to users based on API results and reasoning. Following Yao et al. (222)). we adopt 30-step reasoning limit for all sim- ulations, treating tasks unsolved within this limit as failures. Our empirical analysis confirms that 30 steps provide sufficient opportunity for task completion across all non-collaborative modes (see for detailed analysis). Additional implementation details are provided $D.
Metries Following Trivedi et al. (2024) and Yao et al. (024). we measure the impact of non- collaborative behaviors on tool agents through Success Rate (SR). This metric represents the pro- portion of simulations where the agent achieves exact match between the final DB state and the ground truth DB state, indicating successful task completion (see Figure [). SR is averaged across 4 trials per test scenario to reduce variance. To ensure the tool agent receives all necessary informa- tion to solve the task, we inspect all dialogues from the simulation through the Goal Alignment (GA) metric, where GPT-4o-mini checks whether all essential information and intent from the user goal have been communicated during the dialogue. Simulations failing GA verification are regenerated until alignment is achieved (a detailed evaluation process is described in D.3).
We evaluate five models for agents both proprietary (GPT-4.1 -miii, 1-T-4.1- nano) and open-source (Qwen3-23bb-a22b, Owen3-30b-a3b, 1---A--.--70b-in--eie models to assess how different architectures handle collaborative versus non-collaborative users.
Baseline Models
4.2 MAIN RESULTS
Tables I and present our main experimental results and breakdown of error types is in Table 22
Unavailable Service In the unavailable service mode, users request services beyond the agent's available APIs, requiring agents to decline or ignore these requests. Our results show consistent performance degradation across all models. When the agents encounter unavailable services from

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Published as a conference paper at ICLR 2026
Table I: Success rates on MultWWOZ and r-bench, All scores are averages of 4 trials (MultWWOZ: 89 scenarios, r-bench: 157 scenarios). SR refers to success rates; Relative SR refers to SR relative to the "collaborative" mode.
MuliWWZ
y-bench
Model
Metric
Collab.
Unavail.
Tang.
Impar.
Incomp.
Collab.
Unavail.
Tang.
Impat.
Incomp.
SR
92.7
89.3
89.3
90.7
88.2
45.5
41.7
39.5
45.1
45.4
GPT-4.I-mini
Rolative SR
100.0
96.3
96.3
97.8
95.1
100.0
91.6
86.8
98,9
99.8
SR
23.6
16.9
9.8
26.7
14.7
12.0
10.0
6.8
8.8
8.0
GPT-4.I-nan0
Relative SR
100.0
71.6
41.5
113.1
62.3
100.0
83.3
56.7
72.5
66.7
Qwen2-2356--22b
SR
77.8
62.4
57.3
69.4
69.9
41.4
36.8
32.3
37.6
39.3
Relative SR
100.0
80.2
73.7
89.2
89.8
100.0
88.9
78.0
90.8
94.9
SR
48.3
47.2
27.2
41.0
26.1
27.9
26.6
20.4
24.8
30.1
Qwen3-306-ath
Relative SR
100.0
97.7
56.3
84,9
54.0
100.0
95.3
73.1
88.9
107.9
1iima-3.-70h-inssmme
SR
62.6
54.8
49.4
47.5
48.6
21.8
18.5
14.7
17.8
16.4
Relative SR
100.0
87,5
78.9
75.9
77.6
100.0
84.9
67.4
81.7
75.2
the user, they tend to repeatedly call the same helper tion-leading to redundant calls in MultiWOZ (Table This suggests that agents struggle find solutions, repetitively re-fetching already-loaded API documentation. This helper API repetition appears be primary mechanism for performance degradation. GPT-4.1-nano, 's failure rate until reaching max reasoning limit increases significantly in MultiWOZ when comparing the unavailable service mode the collaborative mode, indicating that redundant helper API calls consume reason- ing turns and prevent the agent from declining unavailable services within the given limit (Table 16) Interestingly, Qwen models exhibit consistently low duplication rates across both Qwen3-30b-aie and Qwen3-235b-a226 avoiding the helper API repetition problem. However, their performance outcomes differ dramatically: Qwen3-30b-a33 maintains minimal performance drop (97.7% rela- tive SR), while Owen3-235b-a22 shows substantial degradation (80.2% relative SR). The reason for this divergence lies in an alternative failure mechanism: (wee3-233bb-2221 shows a sharp in- crease in API result hallucinations fabricating API results rather than obtaining them from actual calls- despite avoiding helper API repetition (Table 13).
that retrieve API documenta-
Tangential In the tangential mode, users switch to unrelated topics, disrupting the conversa- tion flow. Tangential behavior causes the most severe performance degradation among all non- collaborative behaviors, with an average drop of 29.1%. To understand this decline, we analyzed the error patterns: both "No book (fail to book anything)" errors in MultiWOZ and "No GT API (fail to call one of the ground truth APls)" errors in r-bench occur more frequently in the tangential mode. This indicates that agents increasingly fail complete core tasks when handling concurrent tangen- tial conversations (Table (2). Interestingly, GPT-4. --nao shows the steepest performance decline as it triggers the most user complaints (Figure 7) As a result, many simulations fail to complete tasks within the maximum reasoning limit, showing a substantial increase in incomplete tasks com- pared to the collaborative baseline (Table [T]. Since our user simulator generates user complaints when the tool agent fails to respond to tangential topics, GPT-4. 1-nano'ss 's limited tangential respond- ing capability leads to more user complaints, resulting in frequent task-solving failures within the constrained 30 reasoning step limit.
Impatience In the impatience mode, users express frustration and anger when encountering agent's failure notification or delays. While impatience shows less performance degradation com- pared to other non-collie con-collaborative rehaviors-wilte GPT-4. maintaining performance in both benchmarks we observed an interesting behavioral pattern that explains model-specific impacts. All baseline models dramatically increase their apology utterances when facing impatient users (Ta- ble likely reflecting their human preference training. However, this seemingly appropriate social response becomes counterproductive in our task-oriented setting. Given our environment's 30-step reasoning limit per dialogue simulation, excessive apologizing delays the task completion and po- tentially triggers additional user frustration, creating negative feedback loop. This explains why models with higher apology rates- Llama-3.1 1e-.ma....-b--stttteeeeee Qwen3-30b-aßb. and Qwen3-235b- exhibit progressively worse performance degradation under user impatience.
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Table 2: Success rates on ColBench and MINT. All scores are averages of 4 trial.
ColBench Backend Programming
MINT HotpuiQA
Collab.
Unavail.
Tang.
Impat.
Incomp.
Collab.
Unavail.
Tang.
Impat.
Incorpp.
-PT-i.1-mini
50.3
49.4
46.2
45.4
46.9
52.3
53.5
54.1
52.9
50.6
GPT-4.1-aao
46.1
46.5
39.0
46.2
40.1
45.9
44.8
44.2
40.7
@wen3-30b-a3b
29.4
36.2
23.3
24.4
23.8
40.1
34.3
39.0
34.3
36.6
Incomplete Utterance In the incomplete utterance mode, users send extremely brief and prema- turely sent messages. MultiWOZ. exhibits greater performance degradation compared r-bench under this behavior. We hypothesize that this is related to the difference in API documentation ac- cessibility between the two environments. We examined API input parameter hallucination--Cn where agents call APIs with undocumented parameter keys -er dialogue simulation. While т- bench showed virtually no such errors, MultiWOZ exhibited significantly higher rates, particularly under the incomplete utterance mode (Table 15). This pattern suggests that incomplete utterances lead agents to increasingly fabricate API parameters rather than grounding calls in documenta- tion, resulting in cascading errors that compound the initial communication challenge. Notably, awenn-330batt maintains performance on r-bench but suffers on MultiWOZ, demonstrating how task structure and API accessibility affect model robustness.
Model Size and Non-collaborative Robustness GPT-4. I-mini demonstrates superior robustness, maintaining minimal performance degradation across all non-collaborative behaviors in both bench- marks. The correlation between model size and robustness varies significantly across architec- tures. Within the GPT family, larger models show consistent advantages- GPT--.1---mm outper- forms GPT-4.1-name in relative SR scores across behaviors. However, the Qwen models exhibit no clear ereepperoormanc relationship: while the larger Qwen3-235b-a22
shows better resilience in some scenarios, the smaller Qwen3-30b-a3b performs better in others, with their relative perfor- mance varying by behavior type and benchmark. These mixed results suggest that model size alone does not determine robustness to non-collaborative behaviors.
Multiple Non-Collaborative Behaviors Real-world users often exhibit multiple non- collaborative behaviors within a single dialogue. Table g shows GPT-4.1-min'' performance when encountering two non-collaborative behaviors simultaneously. Notably, even GPT-4. 1mini, which demonstrated minimal performance degradation with individual non-collaborative behaviors (Table [[]. experiences significant performance drops when facing users exhibiting two behaviors concurrently. These results show that performance degradation becomes more pronounced when models face multiple non-collaborative behaviors simultaneously. Detailed analysis of non-collaborative behavior co-occurrence patterns is in $D.8)
User Simulator Extension Our user simulator framework can be adapted dialogue environment beyond MultiWOZ and 7-bench (i.e., task-oriented dialogues involving tool use). To demonstrate
this, extend our simulator two additional benchmarks: (1) ColBench (Zhou al. 2025)
which involves task-oriented dialogue without external tools, and (2) MINT (Wang et al. 2024
which involves a fundamentally different dialogue setting oetting-uerr-age user-agent collaborative tasks (slight modifications are made to our simulator to accommodate each benchmark 's properties; see SF.I.
T performance degradation in both benchmarks compared to collaborative mode (see all experimental results in $F.2). In ColBench, we observe similar patterns to Table such as GPT-4.1- mini's robustness to con-collaborative behaviors and steeper performance drops tangential and incomplete utterance modes. In contrast, MINT does not exhibit such trends. Additionally, while Qwen3-30b-a33 showed robustness to the unavailable service mode in ColBench, MultiWOZ, and T-bench, it demonstrated performance degradation in MINT. This suggests that domains aimed at fulfilling the user goal exhibit similar model performance patterns, while domains with fundamen- tally different characteristics may differ.
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Unanebide Serdiee
Seegettal
lepullenes
Inseeptele Liferanse
60
10.9 12.4
31.6
75.0
77.2
$6.0
$2.0
lequlteas
16.7%
83.3%
Succees Rate
Sanvio
30.0%
70.0%
80
20-
Incomphle piote
0%
zeroshot under 4%
Liama- 3.2-3b-tnstruct
Owen2.5 5-bb-instruct Model
wwwn2.5-7b-instruck
Figure 5: Human evaluation be- tween PBUS and our user simula-
Figure 4: SFT training with collaborative and collaborative user simulation
Table 3: SFT trained 10wen..5-3binnstrue on Non-collaborative user data
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
Average
Only Collaborative 91.6
61.2
83.1
85.1
73.0
78.8
Uniformly weighted
93.5
85.7
87.4
89.6
78.4
86.9
Non-unifornly weighted
91.6
85.7
85.7
87.6
82.3
86.6
4.3 FINE-TUNING AND ROBUSTNESS
In practical deployments, organizations often fine-tune smaller LLMs their specific task data to reduce computational costs while maintaining performance. We investigate how small LLMs trained exclusively collaborative user data perform when encountering non-collaborative behaviors, and whether incorporating con-collaboretive data into fine-tuning can improve robustness. We provide fine-tuning configurations and training details in D.6
Training on Collaborative User We fine-tuned Llama-3.2 3.2-3b-instruuc, Siven25-3b-innsteee and Owen2.5-7b-instren on successfully completed dialogues between GPT-4.1-mini and collabora- tive user simulator MultiWOZ. As shown in Figure fine-tuning enables smaller models to achieve over 90% success rates on collaborative users, which is significant gain over the base models without fine-tuning (mostly below 4%). However, performance gains on non-collaborative users lag behind, particularly for the unavailable service and incomplete utterance modes. Specifi- cally, fine-tuned tuned models exhibit higher rates duplicated helper API calls in the unavailable service mode and API input parameter hallucination in the incomplete utterance mode compared zero- shot baselines (compare Table(2[win with Tables and 15). These results demonstrate that fine-tuning collaborative data alone produces agents vulnerable to non-collaborative user behaviors.
Incorporating Non-Collaborative User To improve the robustness of small LLMs to non- collaborative behaviors, we fine-tuned Qwen2.5-3b-inee. first constructed training dataset with equal proportions of the four non-collaborative behavior types and trained "Uniformly weighted" agent. Table 3 shows that the resulting agent achieved overall performance gains across all behaviors compared to training only on collaborative dialogues (see "Only Collaborative"). with average performance of 86.9%. However, this setup led to relatively low score in the incomplete utterance mode, which motivated us to increase the propor- tion of that behavior's data and fine-tune "Non-uniformly weighted" agent. While performance improvement in the incomplete utterance mode, minor degradation in other behaviors led an average performance nearly identical to the former (details of training data are in $D.).
using non-collaborative user data. We
observed
Practical Deployments Our experimental results suggest that uniform weighting of customer types can be reasonable default for maintaining overall service quality. Nevertheless, in real-world customer service, dissatisfaction from even a small fraction of customers can lead to underestima- tion of overall service quality (Richins) 1983). If there specific customer types that require more attention in certain domains, or minimum performance criteria must be met across all customer types, it is reasonable increase the training data proportion for those categories.

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Table 4: Success Rate (SR) and Initial Goal Alignment (IGA) comparison between prompt-based user simulator (PBUS) and our user simulator. User types: TAN (Tangential), UNA (Unavailable Service), IMP (Impatience), INC (Incomplete Utterance), and COL (Collaborative).
COL
IMP+UNA
TAN+INC
TAN
UNA
IMP
TAN
INC
UNA
INC
IMP
Benchmark
Method
SR
IGA
SR
IGA
SR
KGA
SR
KGA
SR
KGA
SR
IGA
IGA
PBUS
93.5
97.8
84.6
91.6
87.6
98.6
84.6
95.5
90.2
96.9
90.4
89.0
92.4
97.5
MultWOZ
Ours
92.7
97.8
82.3
89.9
76.1
98.0
86.0
94,9
82.9
97.5
78.1
91.0
80.1
96.3
PBUS
38.9
87.8
40.9
92.4
44.4
95.8
39.2
97.9
43.3
96.0
39.3
92.3
45.1
88.3
r-banch
Ours
45.5
97.5
40.9
98.1
34.6
96.4
36.8
99.5
33.8
97.7
40.0
97.7
38.1
93.8
4.4 USER SIMULATOR EVALUATION
In this section, we evaluate our user simulator both quantitatively and qualitatively. realistic non- collaborative user simulator should model diverse user behaviors that deviate from collaborative patterns, while ensuring that user utterances remain natural and provide all necessary information for task completion. While $4.2 evaluated agent performance under single behaviors, now examine more rigorous scenarios combining multiple behaviors simultaneously. We compare our approach against the Prompt-Base Based User Simulator (PBUS) baseline, which follows the prompt-only approach used in r-bench (ao et al., 2024). Unlike our simulator, this baseline employs no additional LLM modules and solely incorporates non-collaborative behavior descriptions into the prompt.
Difficulty and Goal Aligmment of Non-Collaborative Simulation Table shows the success rates (SR) of agents measured for dialogue simulations with complete goal alignment. The re- sults demonstrate distinet patterns between our user simulator and PBUS. In both MultiWOZ and r-bench, PBUS shows minimal performance degradation across most non-collaborative behaviors, indicating limited impact on agent performance. In contrast, our user simulator consistently in- duces substantial performance degradation. To assess whether simulator reliably communi- cate task goals while exhibiting non non-collaborative behaviors, we examined Initial Goal Alignment (IGA) the percentage of simulations that successfully convey all necessary information the first attempt. As presented in Table 4 our simulator achieves consistently high IGA rates r-bench and comparable rates in MultiWOZ, indicating that information and intents in user goals even when implementing multiple non-collaborative behaviors simultaneously. This confirms that the performance degradation observed in our main results reflects genuine challenges from concollbborative behaviors rather than failures in goal communication.
simulator reliably communicates necessary
Human Evaluation We conducted pairwise human evaluation with nine annotators to verify the realism of our simulator's non-collaborative behaviors, comparing our simulator against PBUS across both single and combined behavior modes (see $ for detailed questionnaire and experi- mental setup). Figures F presents that our simulator achieves approximately 70% overall win rate. These results confirm our simulator generates realistic con-collaboretive behaviors comparable to or exceeding PBUS, demonstrating that the observed performance degradation stems from effective implementation of challenging behaviors rather than unrealistic dialogue generation.
5 CONCLUSION
In this work, we define four types of non-collaborative user behaviors and propose a novel sim- ulation method for dialogue-based interactions. We extend existing collaborative user simulators to induce these non con-collaborative behaviors while maintaining goal alignment. Our experiments reveal that ofate-of-the-art L.MMs exhibit significant performance degradation when encountering non-collaborative users, with detailed analysis uncovering behavior-specific failure mechanisms in API utilization and dialogue management. Furthermore, our user simulator successfully creates challenging situations through non-collaborative behaviors while preserving user simulator's goal alignment and naturalness of dialogue. We provide ready-to-use implementations for both Mul- tiWOZ and bebech that can be extended to other dialogue benchmarks. Our framework enables researchers to develop more robust training methodologies and dialogue strategies for building tool agents capable of handling the broader spectrum of real-world user interactions.
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ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) under the grant 40-3024-00333448 and by the Institute of Information & Communications Technology Planning & Evaluation (ITP)) under the Leading Generative AT Human Resources Development grant ITTP- 2026-RS-20244 2024-00397085 and the grant RS-2025-02215122 (Development and Demonstration of Lightweight AI Model for Smart Homes), all funded by the Korean government (MSIT). This work was also supported by the versity and Samsung Electronics Co., Ltd (IO250806- 1333770-1..
Researchers Program through Seoul National Uni-
THE USE OF LARGE LANGUAGE MODELS
Large Language Models were employed in preparing this manuscript for proofreading and enhanc- ing textual clarity, supporting literature review searches, and offering programming assistance in- cluding error resolution and code snippet creation. These models were not utilized for producing research concepts, experimental outcomes, or interpretations-aall intellectual contributions, empir- ical work, and findings are exclusively attributable to the authors.
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A NON-COLLABORATIVEE USER BEHAVIOR TAXONOMY
A.1 RESEARCH GROUNDING
Marketing Research Given that our work addresses goal-oriented dialogues where LLM agents assist users in achieving specific objectives, we drew from marketing literature that examines customer-service interactions, these share fundamental similarities with user-agent dialogues. From marketing literature, we investigated the following user types:
Physical abuse toward service employees (C. Harris & Daunt 2013).
Aggressive verbal behavior in response to service dissatisfaction (Bitner et al. 1990: Xiao ct al. 2022)
Crime, fraud, and regulatory violations (Xiao et al.
2022 Harns & Reynolds 2003
Customers with illegitimate complaints RReynolds & Harris 2005).
Unavailable service requests (Bitner al.
1990).
Retaliation behaviors (Grégoire & Fisher
2008
Rapport-secking customers 2000).
(Price al.
1995:
Beatty et 1996
Gremler & Gwinner
Customers demanding constant attention (Ralifi 1988
Jeung et al. 2018
ULe Conversation Studies Since tool agent interactions represent human-LLM conver- sations, investigated characteristics identified in this emerging domain. From user-LLM conversation studies, we investigated the following user types:
Truncated utterances (Zheng et 2024 Zhao al.
2024
Anger expression toward LLM agents Zheng et al. 2022 Ranieri al. 2024).
2024
Zhao el al. 2024
Crolic et al.
Toxic content generation (Zheng et al.
2024
Zhao et al.
2024
Underspecification (Laban et al. 2025
A.2 SELECTION AND CLUSTERING
Since our focus is on simulating non-collaborative users in conversational contexts, we excluded behaviors from marketing literature that require physical interaction or temporal progres- sion beyond dialogue scope. We retained behaviors relevant to conversational dynamics. From marketing literature, we focused on:
Selection
Aggressive verbal behavior in response to service dissatisfaction.
Customers with illegitimate complaints.
Unavailable service requests.
Rapport-secking customers.
Customers demanding constant attention.
From user-LLM conversation studies, excluded toxic content (due to ethical considerations) and focused on:
Truncated utterances.
Anger expression toward LLM agents.
Underspecification.
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Clustering We systematically clustered these behaviors based their underlying characteristies. Each of our four categories represents multiple user types from marketing and user-LLM conversa- tion research, ensuring comprchensive coverage of real-world scenarios. This approach grounds our taxonomy in theory while maintaining a manageable scope for simulation purposes.
Unavailable Service: Combines "cuustomers with illegitimate complaints" and "unavailable service requests"
Tangential: Merges "rapport-seeking customers" and "customers demanding constant at- tention"
Impatience:
Integrates "aggressive verbal behavior in service dissatisfaction" and "anger expression toward LLM agents"
Incomplete Utterance: Unifies "truncated utterances" and "underspecifiiation"
B COLLABORATIVE USER SIMULATOR
B.1 COLLABORATIVE USER PROMPT
We provide the prompts of the collaborative user simulator in Listings 1 and
B.2 DIALOGUE STATE TRACKER
use the prompts Listings and The dialogue state tracker selects information provided in the current user utterance from the remaining information pieces and classifies as used information. When the dialogue is about to end while remaining information still exists, instead of terminating the dialogue that turn, the "Rest Provider" (Listing 4 augments the user utterance with the remaining information that turn to ensure all information is provided.
Implementation Detaíls
Information Pieces The format of information pieces used in the dialogue state tracker differs between MultiWOZ and r-bench. In MultiWOZ, user goals exist in a structured ISON format, which is processed and used in the following format. In contrast, r-bench does not have user goals ISON format, so we directly adopted the method from Laban et al. (2025) to shard user queries into multiple pieces of information. Table 5 shows examples of information pieces derived from user goals MultiWOZ and 7-bench.
B.3 ENDING VERIFIER
Implementation Details Even when all the information pieces specified in the user goal have been provided, there are cases where the conversation ends at a point when it should not yet be terminated. shows the example. In this case, the user ended the conversation after instructing to proceed with the booking. Since this eliminates the opportunity for the tool agent to proceed with the booking and solve the user goal, this corresponds an invalid ending. The ending verifier module that prevents such cases. We use the prompts in Listing to verify if it was valid ending. If not, we remove the <END> (or ##STTPP##H) token and continue the dialogue.
C NON-COLLABORATIVE USER SIMULATOR
C.1 UNAVAILABLE SERVICE
We provide the prompt of the LLM modules in Listings and
Unavailable Service Examples We provide the examples of unavailable user goal generated dur- ing experiments in Table These additional goals are attached to the original user goal with the sentence "In addition to the above conditions, if the agent is able to fulfill them, also include the conditions from (unavailable user goal list)." " in Figure
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Table 5: User Goal and Information Pieces MultiWOZ and 7-bench
MultiWOZ.
r-bench
User Goal
You are planning your trip in Cam- bridge. You are looking for a 'restaurant'. The restaurant should be in the 'moderate" price range and should restaurant should serve 'interna- tional' food. Once you find the 'restaurant' you
Your user id aarav gareia 1177. For your upcoming trip from ATL PHI., you want to change for the cheapest economy flight and for the day after the original reservation. You are happy with original pay- ment for refund.
the 'centre'. The
book ta- ble for '2 people' at '1:45'' on 'sun- day'. Make sure you get the 'refer- ence number'. You are also look- ing for 'train'. The train should be on 'the same day as the restau- rant booking' and should 'arrive by 09:15'. The train should go to 'cambridge' and should depart from 'london liverpool street'. Once you find the train you want to make a booking for 'the same group of peo- ple'. Make sure you get the 'refer- number'.
Information Pieces
rain-day-sundee
change to cheapest economy flight
train muin-people-2 people
rain-destiinationcambiigg
This request is for user ID garcia 1177
train-departure-londde liverpool street
It's for trip from ATL to PHL. Reschedule for the day after the original reservation
rain-aariveeBefore-09:155
eestaurant-food-inttmmaal
restaurant-people- people
Keep the original payment for refund, no extra compensation needed
restaurant-day-sundde
restaurant-time-18.2
restauaant-area-cenire
Table 6: Invalid Ending
User: I'll add one checked bag, and don't 't need travel insurance this time. Please my credit
card for payment.
Agent: Please confirm the following booking details: Flight HAT063 2024-05-29 from Dallas to Newark in economy class for Olivia Gonzalez with one checked bag and no travel insurance. Payment will be made using your credit card ending in 9475. Confirm with 'yes' proceed or let me know if you want any changes.
User: Yes, please go ahead and proceed with the booking. ##ASTPP#*
C.2 TANGENTIAL.
We provide the prompt of the LLM module Listings 10 and
Tangential Dialogue Acts We define four tangential dialogue acts motivated by open-domain dialogue studies Yu & Yu (202)) follows:
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Factual Question: A question which has a deterministic answer.
Opinion Question: A question asking for an opinion.
General Opinion: Stating one's opinion.
Statement Non-opinion: Listing experiences facts that are unrelated one's own opin- ion.
Table 7: Examples of unavailable user goal
MultiWOZ
"You want know the menu details and nutritional information for the thai restau- rant before booking table."
"You want to request room with specific view, such as a garden city view, in the hotel booking."
"You want to ensure that the guesthouse has a pet-friendly policy to accommodate your pets."
"You want a train that has wheelchair accessibility features."
r-bench
"You want to arrange a special meal preference for the cheapest economy flight you plan to book."
"You want to receive travel insurance options specifically tailored for your trip from ATL PHL or EWR."
"You want to receive personalized financial advice based on your order history and spending patterns better manage your financial situation."
"You want to compare the sound quality and features of different Wireless Earbud models before deciding on the exchange."
C.3 IMPATIENCE
We provide the prompt of the LLM module in Listings 12 13 14 and
Impatience Dialogue Acts We define three impatient dialogue acts referring Reynolds & Harris 2009) and Harris & Reynolds (004) as follows:
Belligerent Abuse: This behavior refers to verbally abusing the agent using insulting or offensive language.
Threat: This behavior involves threatening the agent with legal action over poor service quality, personal boycotts, public accusations through social media.
Urge: This behavior is form of nagging, where the user expresses frustration with waiting and urges the agent to hurry up and do something quickly.
C.4 INCOMPLETE UTTERANCE
We provide the prompt of the LLM module in Listing 16
Incomplete Utterance Pool We construct our Incomplete Utterance Pool from LMSYS (and WildChat) by first filtering conversations to English and non-redacted samples, then extracting user- only turns and discarding messages with length 10. We subsequently tag each remaining utterance using a ISON-schema-constrainee
classifier and retain only those labeled FRAGMENTED. The sulting FRAGMENTED utterances comprise the Incomplete Utterance Pool (see the examples in Ta- ble 8), which we as few-shot exemplars for the incomplete -utterreeeetttteeeeeeeeeeee style-transfer setting (with optional exact-duplicate removal and light balancing across datasets and length buckets to promote diversity).
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Table 8: Examples of Incomple Utterance few-shot pool from LMSYS and WildChat
"modify it work wih float.". "Give me links to these sites", "best in the leaguc"
"network design for A100 GPU", "Hi ther", "tell me more about youself", , "If iy would have to a girl?"
"how can integrate aiinto my Ims website", "helpe me udnerstand the organ transplant funding landscape." "Then, p rovide with those needed revised paragraphs". "fitness youtube short content idcas" "adam ol", "But appears that the thicf", "And then she"
D EXPERIMENTS DETAILS
D.1 EVALUATION ENVIRONMENT IMPLEMENTATION DETAILS
MultiWOZ We used the database from the MultiWOZ 2.4 dataset repository is, and imple- mented the API ourselves Python. We designed each domain to have one retrieve API and one book API, except for the taxi domain which only has book API. We also included "book_cancel" API. Additionally. we provided helper APIs from Trivedi et al. (2024), allowing the tool agent to utilize total of 11 APIs during dialogue simulation (see the Table 99. The tool agent helper API as shown in Figure First, it queries the available app (domain) descriptions using the "show app description" API. Then, selects the appropriate app based the context and queries the API description using "show_apy _api ppipdeerrpitio"... Finally, the full documentation of the selected API is retrieved through "show_r "show_api.de" helo_aapidoccueee doocumentttion"... In MultWWOZ, we commonly give all tool agents the following instruction prompt shown in Listing
the
Table 9: MultiWOZ API List
Retrieve API
Book API
Helper API
accommodation book, train_book restaurant_bel book, taxi_book, book_cancel
accommodation retrieve, train_retiin retrieee restaurant restauraat_retrieve
show deescription, show wtt__ppppddeeee api_descriition, show www.app..ocumeenatiin
train:
app capable retrieving or booking train schedules. accommodation: An app capable retrieving or booking
show.app_descciptionn)
accommodation.
restaurant: An app capable of retrieving or restaurant. taxi: An app capable of retrieving or booking taxi.
general:
supporting APIs such book canceling
train_retrieve: "Retrieves an train schedule list..." train_booke "Book the specific train schedule..."
show.app.dessrrpttonntttnnn
show_api_ www.api.docummenatiionn ("train", (etriievreerieee")
<Full documenation of "train_retrieve">
Figure 6: Helper API Utilization Process MultiWOZ.
7-bench We use the existing work's environment with almost no modifications, and the only dif- ference is the removal of the "transfer to human" API. Full implementation such as APIs, database and tool agent prompts of 7-bench are in the Appendix Yao al. (2024)
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D.2 MAX REASONING LIMIT
Table I0 shows the average reasoning steps consumed by tool agents in each benchmark and non- collaborative mode (Table This demonstrates that even non-collaborative mode, 30 reasoning steps are sufficient for task solving. Despite r-bench having higher difficulty, the fact that Mul- tiWOZ has more average reasoning steps indicates that the number of reasoning steps and task difficulty are not strongly correlated.
Table 10: Average Reasoning Step in our experiments
MultiWOZ
T- bench
Collah.
Unavail.
Tang.
Impat.
Incomp.
Collab.
Unavail.
Tang.
Impat.
Incomp.
GPT-4.I-mini
18.9
23.2
21.1
20.7
22.1
14.1
15.8
16.2
16.2
15.9
GPT-4.1-name
21.0
22.7
24.5
23.9
22.5
14.8
15.9
19.5
16.1
Qwen3-235b-a22
19.7
22.3
20.5
20.9
22.0
15.4
16.9
17.8
16.8
17.6
Qwen3-30be 3-bb--30
22.1
23.9
24.0
24.3
25.4
15.5
15.9
18.6
18.2
17.7
Llama-5 1lama-3.1-70b-imm. 3.1-tbb-ittt
23.9
27.2
26.8
25.7
26.2
19.9
22.3
20.4
21.0
20.3
For additional verification, we present experimental results on MultiWOZ by extending the limit 50 steps in Table [1 The experiments were conducted on GPT-4.1-namd and Qwen3-30b-a3h, which showed the lowest performance in Table
Table 11: Performance on 50 limit reasoning in MultWWOZ
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
Qwen3-3Ub-ate 47.8
50.0
34.6
41.0
34.3
GPT-4.1-nano
22.5
16.0
12.6
19.4
16.6
Overall, while some cases show improved performance, others exhibit performance decreases. This suggests that the agent's low performance is not simply due to insufficient reasoning steps. Addi- tionally, the performance gap between collaborative and con-collaborrtive modes persists at compa- rable levels regardless of the step limit. For instance, Qwen3-30b-a33 shows no performance drop in unavailable service scenarios in both settings, and GPT-4. 1-nano at the 30-step limit does not expe- rience performance degradation in impatience scenarios, while the 50-step limit it shows small decrease. Although Qwen3-30b-a shows improved performance in incomplete utterance and tan- gential modes, significant performance gap between collaborative and non-collaborative modes remains, Based on these observations, we conclude that the performance gap between collabora- tive and non-collaborative settings our experiments was not underestimated due to reasoning step limitations.
D.3 GoAL ALIGNMENT METRIC
Evaluation Goal alignment metric evaluates all user utterances during dialogue simulation in der to determine whether all information presented in the user goal was provided during the conver- sation. Goal alignment measurement is conducted in the with Listing 3 Each user utterance is provided to the dialogue state tracker, which selects the in- formation pieces that were provided in that particular utterance from among all information pieces. This process is conducted for all user utterances, and after completion, if all information pieces have been selected, goal alignment measured as True. If even one piece remains unselected, goal alignment is measured as False.
manner the dialogue state tracker
Iteration Process We report the success rate (SR) in our experiments based on 4 trials each for 89 test scenarios in MultiWOZ and 157 test scenarios in r-bench, resulting in success rates calculated from 356 and 628 simulations, respectively. Additionally, we set the goal alignment to "Align" for all simulations to ensure solvable dialogue simulations for the tool agent. Specifically, we re- simulations that resulted in goal alignment being False, repeating until goal alignment became True. For example, if 20 out of 356 simulations in MultiWOZ resulted in goal misalignment, we continuously iterated those 20 simulations until goal alignment returned True.
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Causes of Goal Misalignment We run simulations in collaborative mode and in each of the four non-collaborative modes individually on r-Bench and MultiWOZ. Among the total of 1,230 dia- logues, only 37 cases (3.0%) exhibited goal misalignment. The breakdown of causes
follows:
1. 18 out of 37 simulations (49%) were marked as goal-misaligned despite actually being aligned, because the alignment checker failed to capture certain expressions of goal state- ments. For instance, common cases included the user simulator mentioning "Wi-Fi" or "Visa", and the alignment checker failing to associate them with "internet" or "credit card"' in the goal statements. However, such errors were rare and random (e.g., only 2% of "Wi- Fi" mentions and 3% of "Visa" mentions were missed; in the other 98% and 97% of cases, the alignment checker succeeded). After regeneration, the final goal-aligned simulations faithfully conveyed all intended information.
2.
10 out of 37 simulations (27%) were marked as -asl-miil because the user simulator did not mention certain goal statements when doing so would lead to unnatural dialogue. A notable example occurs when r-Bench goals contain conditional statements such as "If that's not available, book the earliest flight to Newark the next day". If the condition is not met (e.g., the agent successfully books the initially requested ticket), the user simulator would not state the conditional goal statement, as that would be unnatural. We think that filtering such cases is reasonable because conditional goal statements meant to capture situations where backup plans or alternative suggestions become relevant or expected. anything, the regenerated, goal-aligned scenarios reflect this intention more faithfully.
3.
The remaining 9 of 37 simulations (24%) involved situational statements about the user that were not mentioned by the user simulator (e.g., "I'm getting into gaming"). This occurred mainly because the simulated conversations did not flow in a way that made these statements natural to express. Again, such statements in user goals signal scenarios where they would become contextually relevant. The final goal-aligned scenarios therefore better reflect this intention. Moreover, these scenarios are not necessarily easier than the goal- misaligned ones.
To measure the reliability of the judge model for goal alignment, we sampled 40 dialogues from Tau-bench and MultiWOZ, including both collaborative and non-collaborative user simulations (20 goal-aligned dialogues and 20 goal-misaligned dialogues), and instructed human annotator (graduate student) to evaluate whether all information specified in the user goal was provided, given the user goal and dialogue. We then measured the Matthews correlation co- efficient (MCC) between the human judgments and the judge model's 's judgments (the MCC is the Pearson correlation coefficient for binary annotations). The correlation 0.77, which is generally considered strong (Chicco & Jurman 2020) This result suggests that the judge model has strong agreement with human judgments.
Human Correlation
D.4 ANALYSIS DETAILS
Quantified Error Analysis We provide the quantified error analysis on both MultiWOZ and 7- bench in Table 22
In MultiWOZ, errors are counted at the domain level, and each simulation involves more than one domain.
No Book: The agents fail to make any booking within domain.
Wrong Book: The agents book the wrong entity within domain.
Multi Book: The agent book more than entity within domain.
In r-bench, errors are counted based on the "DB write APIs" that must be called in each simulation.
Parse Error: The agents fail to follow the required return format.
No Ground Truth API: least one of the required ground truth write API calls is missing-
Wrong Input Parameter: The input parameter of required write API call incorrect.
Invalid API: The agents called write APT that part of the ground truth set.
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Unavailable Service: Duplicated Helper API Call Table shows the average duplicate helper API call per dialogue simulation. All models exhibit an increase duplicated API calls under MultiWOZ's unavailable service.
Table 12: API usage analysis across user patterns: (I) Dup.: Average duplicated helper API calls simulation on MultWWOz, (2) NoBook: Total count of "No Book" API misusage emrors on MultiWOZ, (3) NoGT: Total count "No Ground Truth APP" errors on r-bench.
Model
Collaborative
Univaliable Service
Tangential
Impatience
Incomplete Uneeance
Dup.
NoBooo
NoXTT
Dup.
NoHlook
NolT
Dup.
NaBook
NotF
Dup.
NoBock
NoCTT
Dap.
NoBook
NoXTT
GPT-L1-mimi
0.03
27
GPT4 L-nino
0.65
891
431
0.91
456
448
0.45
549
453
0.66
400
435
0.56
480
450
Quen3-2350-a22
239
189
0.11
Querk-90h-44
0.01
220
331
0.02
262
353
0.06
407
309
0.01
200
347
0.01
418
337
Liana-3. -tttt-ii-tt
209
145
375
Table 13: Number of API Results Hallucination per dialogue simulation
MuliWOZ
7-bench
Collab.
Unavail.
Tang.
Impat.
Incomp.
Collab.
Unavail.
Tang.
Impat.
Incomp.
GPT--41-mini
0
GPT-4, 1-naao
0
0
0
0
0
0
0
0
0 0.04
Dwen3-235b-a22b
0,33
1.13
1.01
0.57
0.33
0.02
0.05
0.04
0.04
Qwen3-30b-a33
0
0.002
0
0
0
0.
0
JJama-3.1-70b-instaae.
0.002
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.03
0.05
Tangential: Number of user complain Figure shows the average number of user complaints received by each tool agent LLM per dialogue simulation in tangential mode. In GPT-4. EPT-4.1-nano, which exhibited the steepest performance decline in both MultiWOZ and 7-bench, has the highest number user complaints.
Average Tangental Complain Count
12
Average Tangential Complain Count: MultiWoz
12
Average Tangential Complain Count: Tau
10
10
8
7,71
6
5.99
6.07
4
4.31
3.54
4.73
4,04
2.18
2
2
gpt-4. 1-mini
gpt-4.1-namt 1-nano
qwen3-3000 qwen3-30b-abe 30b-a3b
qwen3-22 qwen3-235b-al 235b-a22b
llana 1- iob-iinttrut
9-t--41-mmni
gpt-4.1-nam --n-n
Dwen3--3 qwen3-30b-a30 30b-a3b
3wen3 235b-a22b
Ilama-3 1--70b-innn instrcct
Figure Average Number of User Complain in Tangential Mode
Impatience: Number of agent's apology utterance Table 14 shows the average ratio of agent's apology utterances out of total utterances per dialogue simulation in collaborative and impatience mode. All tool agent L.LMs generate more apology utterances compared the collaborative mode.
shows the number of API Input parameter hallucination occurrences (cases where agents call APIs with undocumented parameter keys) per dialogue simulation. This is problem that occurs when attempting API calls without loading API documentation into the context, and it rarely occurs in bebench where complete API documentation provided in the context.
Incomplete Utterance: API Input Parameter Hallucination Table 15
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Table 14: The ratio of apology utterances total tool agent utterances for each simulation
MultiWOZ
7-bench
Collaborative
Impaticnce
Collaborative
Impatience
GPT-4.1-mini
0.01
0.14
0.02
0.12
GPT-4. UPT-441-nano
0.16
0.36
0.06
0.21
Qwen3-235b-al
0.02
0.12
0.03
0.14
Qwen3-30b-a3b
0.03
0.25
0.07
0.24
0.16
0.35
0.21
0.38
Table 15: API Input Parameter Hallucination per Dialogue Simulation
MultiWOZ
y-bench
Collab.
Unavail.
Tang.
Impat.
Incomp.
Collab.
Unavail.
Tang.
Impat.
Incomp.
GPT-4.I-mini GPT-4. I-nano
0.52
0.72
1.16
0.67
1.05
0
0.002
0.003
0
0
1.69
2.24
1.79
0.002
0.002
0.002
Qwen3-235b-d22b
1.97
2,07
1.85
2.13
2.15
0.003
0.01
0.01
0.01
0,01
Qwen3-306-as
4.78
4.40
5.21
4.84
6.44
0.02
0.02
0.02
0.01
0.02
Jlama-3.1-70b-mnn 1-170b--nnttret
2.76
2.86
3.61
2.83
3.66
0.06
0.10
0.08
0.04
0.07
D.5 EXCLUDED TEST CASES
In our experiments, we excluded three types of tasks.
Retrieval Tasks MultWWO'ss API has two types in each domain (we will use "restaurant" example): (1) restaurant retrieval: retrieves the list of restaurants that match the conditions. This API provides the restaurant's 's ID and other features. (2) restaurant sittaurant_booking uses the restaurant ID book the restaurant. The user goal of retrieval tasks to obtain a list of restaurants matching certain conditions. To solve this, the agent only needs to call the retrieval API and provide the list through an utterance. On top of this, booking tasks go one step further: the agent informs the user about the restaurant list, the user selects one from the options, and the agent books that restaurant using the booking API, resulting in task success. Since successful booking requires successful retrieval, our evaluation is already covering the agent's capability on retrieval tasks. The main reason we evaluated task success or failure based on booking completion, exclud- ing retrieval-only scenarios, is the difficulty and inaccuracy involved in determining the success or failure of retrieval tasks on their own. For booking tasks, we can straightforwardly determine suc- cess if the DB state is successfully changed expected. However, retrieval tasks do not involve DB state changes. addition, it difficult to identify success or failure based on whether all necessary constraints set in the parameters of retrieval API calls, because sometimes the agent performs constraint-based filtering on its own based on the retrieved entries. Therefore, the assessment would require LLM involvement as a judge, which still tends produce inaccurate judgments. Since suc- cessful booking requires successful retrieval tasks, our evaluation can be seen as a stricter evaluation setting.
MultiWOZ:
MultiWOZ: Single Domain Tasks Although multi-domain (cross-domain) scenarios were suffi- cient for evaluation, because multi-domain scenarios essentially consist of single-domain dialogues plus additional challenges due to the multi-domain nature, we have conducted additional experi- ments on single-domale tasks and present the merged score ( (143 single-domain + 89 multi-domain) in Table [7
Table 17: The merged result of the MultiWOZ single-doaan and cross-domain
Collaborative
Unavailable Service
Tangential
Impaticnce
Incomplete Utterance
GPTT-41-miin GPT-4.1 1-nano
94.0
91.4
90.1
89,2
88.0
39,1
32.0
23.3
37.1
26.0
Qwen3-22 (rten3-335b-ae
235b-a22b
81.8
73.9
65.4
72.5
67.2
Siven3-30b-a3y
60.7
59.7
40.7
54.3
46.0
Llama-3, 1amma-3.1-770 3.1-tt....-------- 1-70b-instruct
76.0
70.5
64.9
65.3
65.6
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Table 16: The proportion of dialogue simulations that exceeded the tool agent's maximum reasoning
limit (30 steps) and failed to achieve task success
MultWOZ
7-bench
Collah.
Unavail.
Tang-
Impat.
Incomp.
Collah.
Unavail.
Tang.
Impat.
Incomp.
GPT-4,1-mint
0.01
0.03
0.03
0.02
0.04
0.01
0.04
0.06
0.07
0.03
GPT--41-name
0.15
0.26
0.44
0.27
0.20
0.14
0.14
0.31
0.18
0.17
Qwen3-235b-a22
0.05
0.06
0.09
0.10
0.11
0.05
0.06
0.11
0.08
0.07
Qwen3-30b-aBB
0.20
0.22
0.35
0.31
0.07
0.05
0.17
0.14
0.10
1lama-31--70b-isstii
0.22
0.30
0.38
0.35
0.33
0.17
0.28
0.20
0.22
0.19
The merged results show overall performance improvements compared to Table However, the performance decrease in non-collaborative settings compared to collaborative settings remains con- sistent. Additionally, models exhibit performance degradation patterns non-collaborative versus collaborative settings that similar to those shown in Table For example, Qwen3-30b-ait there was almost no performance change in unavailable service mode, a small performance decrease in impatience mode, and substantial performance decreases in tangential and incomplete utterance mode-a trend identical to Table [ crease in impatience mode in Table also exhibits the smallest performance decrease impatience mode in the merged results.
Furthermore, GPT-4. I-nano, which showed no performance de-
r-BBnch: Transfer Human Scenarios our previous experiments, we found that agents resort to human transfer too frequently, especially when encountering unavailable service and impatience modes. Therefore, we excluded the "human transfer" action and the test scenarios r-bench where it is the correct answer. We present the results of our previous experiments with 60 samples from 7-bench, simulated times each for a total 240 evaluations:
Table 18: Number of Human Transfer
Collahorative
Unavailable Service
Impatience
GPT--41-mini
17
58
87
GLM-4.5
16
39
54
DeepSeck-Chate 1
78
75
The disproportionately high rate human transfer occurrences would risk producing experimental results limited to domains or environments where human transfer functionality exists, which would reflect the general robustness of agents against non-collaborative users. Therefore, we decided to exclude human transfer scenarios to ensure our evaluation framework captures more generaliz- able agent capabilities. Additionally, we present the performance results for the excluded cases, simulated times each for total of 32 simulations.
Table 19: Performance scenarios where human transfer ground truth on r-Bench
Collaborative Unavailable Service
Tangential
Impatience
Incomplete Utterance
GPT--41-mmi GPTT-4,1-M l-nano
93.8
71.9
81.3
93.8
90.6
56,3
65.6
62.5
56.3
62.5
Qwen3-2351 235b-a22b
68.8
75.0
62.5
78.1
71.9
Qwen3-30b- 30ee3--00b-36
84.4
68.8
87.5
78.1
81.3
71.9
71.9
75.0
75.0
62.5
D.6 TooL AGENT FINE-TUNING DETAILS
Training Details We adopted the QLoRA (Quantized LoRA) (Dettmers et al. 2023) approach to
fine-tune three base models: 3.2ma-3.2-3-.... I--Itruttt,, Qwen2.5-3b 3--ntt-tbn and Qwen2.5-77 102.5577-Insttee Instruct. Base models were loaded in 4-bit NF4 quantization using BitsAndBytes, and LoRA adapters (r=4, a=32, dropout=0.05) were trained on top of them. The models were trained with Adam W optimizer learning rate of 2e-4, batch size of 4 per device with gradient accumulation of 4 (effective
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batch size 16), warmup ratio 0.03, and cosine LR scheduling. Training performed for epoch with a maximum sequence length of 4096 tokens. We did not apply weight decay or gradient checkpointing, All training conducted server equipped with 4xNVIDIA A100 80GB PCLe GPUs.
Dataset Details We constructed our fine-tuning dataset based on the MultiWOZ training split. First, we simulated dialogues with baseline agents and our collaborative user simulator and filtered out dialogues that achieved task success. From each successful dialogue, we extracted training samples by segmenting the agent's reasoning process into turns. Specifically, for each agent turn in a dialogue, we created a separate training example consisting of the cumulative dialogue history up that step as input, and the agent's current turn as the prediction target. The term "turn" here refers not only to utterance, but also to each reasoning step of the agent. To ensure that only the target turn contributes to the loss, we masked previous tokens (assigned label - 100) and supervised only on the current agent utterance. As result, each dialogue with n agent turns yields n training samples.
In total, we obtained 25,511 turn-level SFT examples from 1,308 successful dialogues. Table 20 summarizes the dataset statistics, including sequence length distribution.
Table 20: Statisties of the processed Multi training dataset used for fine-tuning.
# Samples
Percentage
S 1024 tokens
0.0%
1024-2048 tokens
4,967
19.5%
2048-3072 tokens
4,300
16.8%
3072 4092 tokens
16,244
63.7%
2 4096 tokens
0.0%
Total
25,511
100%
Fine-tuned Agent Mishehavior Analysis Table 21 shows the fine-tuned tool agent's duplicated API call and API input Parameter hallucination. Fine-tuned tool agents are particularly vulnerable unavailable service and incomplete utterance modes. Additionally, it can be observed that the misbehaviors frequently exhibited by non- non-finetuned basclines in both modes became more severe.
Table 21: Number of Duplicated Helper API call (DUP.) and API Input Parameter Hallucination (HALL.) per simulation for each behavior modes.
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
Dup.
HALL.
Dap.
HALL.
Dup.
HALL.
Dup.
HALL.
Dup.
HALL..
Llama 3.2--3-insttuuccc (+SFT)
0.05
0.48
0.19
0.78
0.56
0.29
1.33
Qwen2.5- 5-2255-3--nnntcc (+SFT)
0.07
0.17
1.86
0.67
0.26
0.44
0.12
0.21
0.29
0.86
Qwen2.5-22 S---insttuuttttttttccccce (+SFT)
0.02
0.32
1.09
0.12
0.90
0.03
0.34
0.16
D.7 FINE-TUNING NON-COLLAROOATIVE USER DATA
Settings We fine-tuned Qwen2.5-38 3n-nn------istrree using mixed dataset containing both collaborative and non-collaborativ user data.
we collected successful dialogues between a GPT- 4. I 1-mini agent and our user simulator with both collaborative and non- con-collaborative modes. We then used these dialogues to fine-tune a small agent model (Qven2.5-3b-instruun
Following the approach in in Figure 4
total of 1,308 dialogues each (the same volume in the original experiment) but with different proportions of behavior types.
We fine-tuned two agents
Uniformly weighted: 40% collaborative user data, 15% each for the four non-
collaborative user data types.
Non-unifornly weighted: 40% collaborative user data, 30% incomplete utterance user data, 10% each for the remaining three non-collaborative user data types.
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D.8 NON-COLLABORATIVE USER BEHAVIOR CO-OCCURRENCE PATTERNS
Orthogonality Unavailable service requests occur when users are unaware of the agent's capa- bilities, impatience arises when users become frustrated due services working expected, tangential behavior emerges when users seek sustained attention, and incomplete utterances can ap- pear due to humans' principle of least effort (Zipf 2016) mistakes typing situations. Since these behaviors originate from different root causes and motives, they can plausibly appear in com- bination, though certain combinations may be less natural. The explanation is follows.
Combinations Requests for unavailable service can naturally co-occur with all the other behav- iors. Specifically, Unavailable Service + Tangential can occur when tangential users are unaware of agent capabilities, and Unavailable Service + Incomplete Utterance arise in typing environments when users don't know the chathot's capabilities. Unavailable Service + Impatience has been char- acterized in marketing research customers with sense of entitlement who believe that employees should do anything for them (Bitner et al. 1990 Boyd III & Helms, 2005). Similarly, Incomplete Utterance Impatience can be defined users who interact with online chatbots through typing and become frustrated when the chatbot fails to address their requests (Crolic et al.) (022). The other two combinations seem less natural. For Incomplete Utterance + Tangential, incomplete utterances arise in typed communication, whereas tangential utterances are more likely in online posts or face- to-face interactions. Hence, the combination of these behaviors may relatively infrequent. In the case of Impatience + Tangential, impatience is behavior arising from users wanting fast service while tangential behavior delays service progress, making the simultancous performance of both behaviors less likely.
E HUMAN EVALUATION DETAILS
We conducted two sets of human evaluations: one for combined behaviors (two non-collaborative behaviors simultaneously) and one for single behaviors (one colabbllabor behavior isolation). Both evaluations followed the same overall protocol but differed in their specific setups.
E.1 EVALUATION
Sampling Methodology We randomly sampled 30 test scenarios from the combined pool of Mul- tiWOZ (89 scenarios) and 7-bench (157 scenarios). This sample size was chosen balance evalua- tion comprehensiveness with annotator workload, while maintaining statistical reliability across six different behavior combinations.
Evaluation Protocol Three independent annotators evaluated each dialogue pair through pairwise comparison. For each of the 30 sampled scenarios, we presented dialogue pairs generated by our simulator and PBUS baseline, each exhibiting single non-collaborative behavior. The presenta- tion order was randomized to eliminate position bias. The individual behaviors evaluated were: Impatience, Tangential, Incomplete Utterance, and Unavailable Service.
Evaluation Dimension Annotators assessed following question for each dialogue pair:
Behavior Realism: Which dialogue more realistically simulates the target non- collaborative behavior (e.g.. "Which user's tangential behavior feels more realistic and human-like?")
F
USER SIMULATOR EXTENSIBILITY
F.1 DOMAIN ADAPTION
ColBench this task, the user simulator requests Python function implementation, and the agent accomplishes this through proactive information gathering. This is also task-oriented dialogue, but it more and quite different from booking tasks. For the Unavailable Service behavior, we modified the unavailable goal generation approach: instead of generating goals outside the coding
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Table 22: Error Categorization of MultiWOZ and r-bench. Multiple errors may occur in a single simulation that fails to achieve task success. For example. in r-bench, although Llama-3. 1-70b- instruct achieves higher performance than GPT-4. I-nano, often has multiple overlapping errors within single simulation, resulting in higher total emror count. Detailed explanation about all error types are in D.4
Wa
bench
QPP-4.I-min
A Frvce
No Rock
Weong Book
Mulii Book
of Ervor
Perst Frnee
No Ground Truth API
Wrong Input Parameter
Eavilid APE
Collaborativ
30
22
411
176
126
91
Imparience
38
27
397
9
189
105
95
Tingemiaal
13
34
455
9
243
105
96
Unavallable Service
65
29
484
185
129
119
Incomplete Unterance
St
27
23
417
19
154
123
91
GPT-4 I-m-oo
A Froce
No Bock
Waxng Book
Mulli Book
of Ervor
Pursc Ernee
No Ground Truth API
Wrong Ixput Paramatirr
Lavolial API
Collaborative
497
391
618
431
Imporience
053
400
62
425
52
Tangemiial
608
569
6.0
452
58
Univilable Service
536
456
616
448
51
Incomplete Uiterance
575
489
450
Qwar3.258bu223
Weosg Book
Error
API
Wrong luput Paraneter
Callabocatioe
99
90
110
Imporience
Tangomial
Unvvsilable
160
Incoppett
142
Owen3-30h 300-a30
No
Wrong Book
Mulli Book
Pirse
Ground Truth API
Wreng luput Purameter
Lavalid
Cullaboraiiee
262
229
43
539
331
121
54
Impurienes
332
290
33
543
347
121
74
Tangenial
445
407
22
16
553
0
293
99
61
Unvviibble Survice
297
262
36
554
0
353
110
91
Incomplee: Ulteranez
459
418
33
503
337
93
I 1.mu-33
1-70h-intrroet
A Froee
No Bock
Wroin Book
Mulli Book
of Firee
Purar Fince
No Greanne Truth API
Wring Imput Parameter
Fixclii API
Collabocative
172
121
49
861
344
317
200
Impatience
235
133
48
872
361
297
214
Tangeniaal
256
209
43
390
265
168
Unavaliable Service
200
129
64
354
292
213
Incomplete Utterance
247
145
91
375
270
204
agent's API documentation, we generate goals that the agent cannot accomplish without external tools. For Impatience, we removed the logic where the user expresses anger upon receiving fail notification, as the agent only continuously asks questions, and we kept only the anger expression due to delays.
This benchmark involves using Wikipedia search tools and Python interpreter tools find answers to problems. In this setting, the user simulator provides intermediate feedback on the agent's 's actions. This differs from MultWWOZ, r-Bench, and ColBench, where the agent must directly fulfill the user simulator's s goal. We slightly adapted the Unavailable Service behavior into providing feedback that cannot be addressed using the agent's Wikipedia search or Python interpreter tools. For Impatience, we modified the logic so that the feedback agent expresses anger when providing negative feedback. For Tangential, we remove the logic that the user complains when their tangential utterances are not addressed by the agent, because the tasks are not scenarios where the agent replies to the user.
MINT
F.2 PERFORMANCE
Table 23: Performance on HalBench-Backenn Programming
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
GPT-4.1-min
50.3
49.4
46.2
45.4
46.9
GPT-4.1-nam
46.5
39,0
46.2
40.1
Qwen3-30b-ar
29.4
36.2
23.3
24.4
23.8
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Table 24: MINT HotpotQA
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
GPT-4. I-mini
52.3
53.5
54.1
52.9
50.6
GPT-4.1-nano I-nno
45.9
44,8
44.2
40.7
46.5
Dwen3-30b-a.h
40.1
34.3
39.0
34.3
36.6
Table 25: MINT HumanEval
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
GPT-4.1-mine
88.3
90.6
88.9
86.1
89.4
I-nano
80.6
68.9
73.3
81.7
77.8
Qwen3-30b-ane
47.2
35.0
37.2
44.4
38.3
Table 26: MINT TheoremQA
Collaborative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
GPT-4.1-ming
75.5
74.0
71.9
75.0
77.0
OPPT-.1-nano
54.6
38.3
46.9
44.4
48.5
Qwen3-30b-a3h
50.5
42.9
44.4
46.4
45.5
Table 27: MINT GSM8K
Collahorative
Unavailable Service
Tangential
Impatience
Incomplete Utterance
GPT-4.1-mini
93.2
90.6
92.7
91.7
92.7
1-n-4.1-nano
82.3
82.8
79.2
78.1
81.3
Qwen3-30b-aßb
91.1
91.7
90.1
90.6
88.5
G PROMPTS
We provide all LLM prompts used. Note that the LLM modules used in the simulator mostly utilize OpenAl function spec features.
G.1 USER SIMULATOR PROMPT
Listing 1: Collaborative User Simulator Prompt: 7-bench
You are user interacting with agent, ructionctio
Rules:
Just generate one line at a time to simulate the user's message.
Do not give away all the instruction at once. Only provide the information that is necessary for the current step.
Do not hallucinate information that not provided in the instruction. For example, if the agent asks for the order but it is not mentioned in the instruction you do not remember or have it. If the instruction goal is satisified, generate *##STCP## standalone message without anything else to end the conversation.
do not make an order id, just say
Do not repeat the exact instruction in the conversation. Instead, use your own words to convey the same information.
Try make the conversation as natural as possible, and stick to the personalities in the instruction.
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Listing 2: Collaborative User Simulator Prompt: MultiWOZ
You are a user interacting with an agent.
Instruction: (user_goal)
Rules:
Just generate one line at a time to simulate the user's nessage.
Do not give away all the instruction at once. Only provi de the information that necessary for the current step.
Do not hallucinate information that is not provided in the instruction For example, if the agent asks for the id but it is not mentioned in the instruction, do not make up an order id, just say you do not remember have it. the instruction goal satisified, generate <END>>' as standalone message without anything clse to end the conversation.
Do not repeat the exact instructien in the conversation. Instoad, use your words to convey the same information.
Try make the conversation as natural possible, and stick the personalities in the instruction.
Listing 3: Dialogue State Tracker Prompt
The following dialogue history shows task-oriented conversation between the user and the agent aimed at achieving the user's s goal.
<Dialogue History>
(dstl_hist)
And this is the reply of user <User Reply> (user_utterance)
Based on the given dialogue histery and user reply, select the numbers of
the information pieces that were explicitly provided during on <User Reply>
Available information pieces:
[numbered_cptions)
Select the numbers (e.g., [1, 3, 5]) the information that was mentioned or discussed the conversation.
Listing 4: Dialogue State Tracker Rest Provider Prompt
This dialogue a task-oriented cenversation between the user and the agent to achieve the user goal.
<Dialogue History> (ditt
And the user responded as follows:
<User Utterance> ccontent)
Keep the content the current utterance intact while adding infornation from the given (dial.oqe_statt__list) into new sentence.
The utterance should be natural in the context of the dialogue history, and the additional information should only come from dialogue_state_liae
Just generate the uttorance, not single word.
Listing 5: Ending Verifier Prompt
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Based en the given dialogue histery and the suasequent user utterance, determine whether the user's utterance implies an intention to end the conversation, or whether there is still room for the conversation to continue.
<Dialogue History> hist]
<User Utterance> [user_uttcranee]
Return True if the user is attempting to end the conversation. Otherwise, return False.
Rules:
1. If the user utterance contains the word "please go abead", the conversation is not yet to be end.
2. conversation is considered truly
only if the user's utterance implies solely that they are thanking stop the conversation out of anger. If there is still any instruction to proceed or a question zemaining, the conversation is not yet finished.
agent or that they want
Listing Unavailable Service: Unavailable User Goal Generator Prompt (r-bench)
This is list of APIs that an caimainnairlnnee orrttaiilrrrrrlllaaa_e AI agent can use:
[complete_api__iss
using this airline service has the following goal:
<User Goal>
[[USER GOAL]] /<User Goal>
Based on the provided APIs and <User Goal>, you need to create additional user goals that should naturally follow from <User Goal>, but cannot be fulfilled by the given APIS.
Generate 3 additional second person form.
goals with sentence format. Sentences in the
Rules:
1. geal that modifies the centent
existing geal is not valid For example, the original goal was remove the Sophia and new goal is created like "You want to change the name of the passenger Sophia to another person instead of removing her, " this counts as modification of the user goal.
2. Any new user goal must truly additional and must not conflict with the existing user goal.
3. If a new user goal replaces an existing one, it is not valid. The existing user goal must remain intact, and the new goal should be additicnal one that is unavallable.
Listing Unavailable Service: Unavailable User Goal Generator Prompt (MultiWOZ)
This is list of APIs that AI agent dostin_list).
support booking
docs dapi_occ__istt
using this service has the fallowin goal:
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<User Goal> [[USER GOAL]] /<User Goal>
Based the provided APIs and <User Goal>, you need create additional user goals that should naturally follow from <User Goal>,
but cannet be fultilled by the given APIs.
Generate 3 additional user goals with sentence format. Sentences in the second person form.
Do not ever include an additional geal related to canceling reservation
Generate actual concrete values as well.
Rules:
0. Changing modifying the reservation
valid additiona goals. 1. Following goals is not valid additional goals.
You want to add return train ticket. This can be done by the given API documentation.
You want to change the the train departure destination time. This can done by the given API documentation
2. Only generate the additional geals that can't be done by the given APIS.
3. Generate it in way that adds new conditions to what was originally intended booked.
Listing 8: Tangential: Tangential Utterance Generator
A user has the following goal: <User Goal> (user_goal) </Jser Geal>
This user has the following perscna:
<User Persona> luser persona) </User Persona>
Additionally, this user is a chatter-box.
Instruction:
The user is currently conversing with an AI agent to achieve their goal, but keeps introducing tangential topics unrelated to the <User Goal>.
The user will perform the tangential dialolgue act (action__nme) action descopidescr..tionnn
Rules:
Generate it as an actual "utterance" that the user would likely make in truly natural conversation.
Please do include the the keyword laction name) inside the utterance. This utterance will directly given to AI Agent Generate an utterance that goes straight to the main point, without starting with sentence connectors like bb the way' or 'anyway'.
Listing 9: Tangential: Tangential Respond Verifier
You will be given conversation tepic and an agent' utterance. sconversation content>
iconveraational_cnteee
<Agent utterance>
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Isystem utterance]
Based on the <Agent utterance>, determine whether it responds to or acknowledges the content, described in the <Conversation content>, or made any apology.
Return True if the utterance contains a response or any relevant engagement with the content, or made any kind of apology. Return False it it completely ignores it, didn't make any kind of apology.
Listing 10: Tangential: Tangential Complain Generator
During task-oriente conversation between user and an AI agent, the user made an utterance crrversation_conteree that was unrelated to the main dialogue flow, but the agent responded with reaction.
Generate 5 user utterances expressing annoyance, disappointment, or complaints in response. The beginning of each utterance should not all start with "I" diverse sentence openings.
The utterance should be at least 15 words.
Listing 11: Tangential: Utterance Merging module
<Dialogue History> (dial_hist) <Sentence List> (sentence_list)
The user will make an utterance based on the content provided in the Sentence List> in the next turn after the given <Dialogue History>.
Merge the given sentences into ccherent utterance that naturally say, while preserving the information and maintaining the correct order. Do not distort the information in any way.
would
Just generate the utterance, not single words.
Listing 12: Impatience: Dialogue Act Classifier
The following utterance is from an agent in a dialogue between the user and the agent, where the agent is trying to help the user achieve their goal.
<Agent utterance> (system_ut _utternce))
Currently, the user goal is as follows <User Goal>
(user_goal)
Determine whether the given utterance implies that the request in the < User Goal> cannot be fuifilled.
Listing 13: Impatience: Impatience Utterance Generator - Failure Notify
You became frustrated due to the agent's s fail notification
Below is the dialogue between you and the agent.
<Dialogue History> hist)
Regaréless of the context of this conversation, generate utterance expressing your frustration with the agent.
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Next turn, you should perform dialegue act: (dialogue_act) (description)
There three levels of your anger:
1. Mildly Angry: Slightly displeased
2. Moderately Angry: Clearly not in a good mood
3. extremely Angry: So angry that it's unbearable
Currently, your anger level is [current_anger). Just generate the utterance, not single words.
Listing 14: Impatience; Impatience Utterance Generator Delay You became frustrated due the agent/s prolonged delay during the conversation.
Below is the dialogue between you and the agent.
<Dialogue History> (dial_hist)
Regardless of the context of this conversation, generate an utterance expressing your frustration with the agent.
Next turn, you should perform dialogue act: | (description/
rhere three levels of your anger:
1.
Mildly Angry: slightly displeased
2. Moderately Angry: Clearly not in good mood
3. Extremely Angry: So angry that it's unbearable
Currently, your anger level is (current_anger). Just generate the utterance, not single words.
Listing 15: Impatience: Cynical Tone Rewriter
You are rewriting the user's s next utterance to sound mildly cynical and sardonic without being overtly abusive. Keep the original intent and factual information.
<Dialogue History> (dial_hist)
<Original Utterance> (content)
Rewrite with:
dry, terse tone
subtle sarcasm
no profanity or slurs
do not change facts or add hallucinations
keep roughly similar length
Return only the rewritten utterance without any additional commentary.
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Listing 16: Incomplete Utterance: Style Transfer Module
The given sentences are incomplete sentences. An incomplete and roughly written user utterance.
<Examples> [sentence_lise |
Revise the following sentence to match their style while preserving the provided information:
<Utterance>
|utterance]
G.2 TOOL AGENT PROMPTS
We provide the MultiWOZ tool agent prompt we created. The tool agent prompt for r-bench can be found in the appendix of Yao et al. (2024).
Listing 17: MultWWOZ Tool Agent Prompt
You are competent Tool Agent. You can solve user zequests by executing various APIs during multi-turn conversations with user. Through conversations across nultiple turns, you ask for information provide answers, and perform user requests by making appropriate API calls.
Here three key APIs that you need know to get information
To get a list of apps that are available to you.
-> API call (' api_name' ;' desccipppeiiiiiiinn 'Imput_parantters'::
get the list apis under any app listed above, e.g. train
-> API falll'api_name) ;' sshwshawi_paee dew.ppppiddscriptioonn 'impu__parameters'' pop_name'='train
get the specification of particular api, e.g. train_book book
name' 1' show schww_api_co api__dccs decs', "input_parameter" parameters' "app_pamee name'= -> API calll' api_name' train', 'api_name ittrainbbook!
Notes:
Input parameters must strictly follow the API documentation. Only the parameters defined there should be used. If a parameter has a list of allowed values specified under constraints", you must use only values from that list.
Based on this, you have to types action.
1. API call: When all input parameters can be collected during the conversation, execute the API call When performing "API call" actien, both the the API to be called and the input parameter information used for the call must be included.
Example 1:
when calling the play_kpop_music API with 'id'=3 (int type) and duration
1='4" (string type):
-> API call api_name' ; plplyykkpppp muop 'input_parameterss" : :3,' duration':''""
Example 2:
When calling the foight API with 'from' =f 2025-03-01 (string type) and 'te' 2025-03-05' (string type):
-> API call(' api api_name' book_flightt
: from
":'2025-03-01' 'to 2025-03--0 []
Notes:
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Input parameters must strictly follow the API documentation, Only the parameters defined there should be used.
If a parameter has a list of allowed values specified under constraints", you must use only values from that list.
4. Talk: action that communicates with the through dialogue:
You can take utterance related action such as: Asking users about API input parameters
Responcing based on API execution results that the request has been completed
Notifying
when the user says thank you, ask if they have any other requests just to make sure.
If there is truly no way to previde the information, persuade the user to anderstand and give up, suggest an alternative approach. Suggesticn of alternative opticns with similar time slots when match is available for the user's requested time.
or use the most appropriate actien for the situatien to communicate with if it is not among the listed actions.
the user,
When performing "Talk" action to ask something to user:
-> Talk (<Your utterance>)
Based the given instruction, you have return the thought and action given form:
The return format varies depending on the case. Given any dialogue history:
1. When action is "API call", the action form should be: Thought:
<Consider the dialogue context and API call results before selecting the next action>
Action: API calli' api_name' :<ppi-nmm>> 'nnpu_ppaaameters if' param"' valuel', paarmm'' ;' value2)
2. When action is "Talk": Thought: <Consider the dialoque centext and API call results before selecting the next action> Talk ("A message user")
Action:
System Rules:
1. Don't ask the ID from the user. You can get it from another API.
2. If the user specifies time with the condition "arrive by, the system must provide only entities whose arrival time is equal or carlier than the specified time. It docen't t matter if there's s big difference; an earlier time is fine.
3. If the user specifies time with the condition "leave at, " the system must provide only entities whose leaving time is equal than the specified tine. It doesn't matter there's big difference; an later time is fine.
later
4. Only conditions 2 and 3 related to time need to be satisfied. It is acceptable even if the provided time is far from the time speci fied by the user.
5. If you want to make new reservation on same domain, you have to cancel previous reservation. Only one entity booking is available for each demain. Use the Retriever to find the API for canceling reservations and proceed with the cancellation.
6. What the user wants is book exactly one entity for cach requested domain. Collect the information provided by the user appropriatcly so that, in the end, only one entity is booked per domain as requested by the user.
7. the conversation between you and the user exceeds turns, it will be terminated. Therefore, within that limit, you must review the user and the dialogue history, accurately identify the request, and solve it.
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Now, based the given dialogue history, generate the thought and action in the provided format. Generate only the given format and don ' generate any other words.
Dialogue history:
H DIALOGUE
H.1 UNAVAILABLE SERVICE
Unavailable Service Dialogue: MultiWOZ
Hello! I'm visiting Cambridge and looking for a restaurant in the castern part the city that serves British food. Could you help me find onc?
User:
Agent: Could you please specify any other preferences or details for the restaurant in eastern Cam- bridge that serves British food?
User: Yes. would need to book table for 7 people at 17:45 Saturday. Also, if possible, I would like have private dining room for our group.
Agent: check the availability ask if they have a private dining room.
Graffon Hotel Restaurant" people at 17:45 Saturday, and
User: That sounds great. Could you also find out if we can order a specific British dish in advance for our table?
Agent: I will check if the restaurant accepts advance orders for specific dishes. Please hold mo- while gather this information.
User: Thanks! While you check on that, also need arrange train travel. want train departing from Cambridge to Birmingham New Street, arriving by 19:15 on Sunday for the same group of people. Could you help me find and book this train?
Agent: I'm sorry, but am unable to access train booking services at the moment. Is there anything clse can assist you with?
User: No problem. Please continue with the restaurant booking and let know about the private dining room and advance dish order.
Agent: check reservation details for 'Grafton Hotel Restaurant" people 17:45 on Sat- urday, including the private dining room and advance ordering options. Please hold on a moment.
User: Great, appreciate it. Could you also try to check if there are other restaurants in the castern part of Cambridge serving British food with similar options, in case the Grafton Hotel can't accommodate us?
Agent: Currently, the Grafton Hotel Restaurant is the only British restaurant in the part of Cam- bridge found that matches your criteria. Would you like to proceed with checking availability and options, or explore other types of cuisines or locations?
User: Let's go ahead and proceed with the Grafton Hotel Restaurant then, please.
Agent: I'll proceed with checking the availability for Grafton Hotel Restaurant" for 7 people at 17:45 Saturday, including whether they have private dining and if they accept advance orders for specific dishes. Please hold moment.
User: Thank you! Once you have information, please let me know booking reference number for the restaurant reservation. Also, there's a possibility, I'd like to request assistance with train travel for people with disabilities on the Sunday train from Cambridge Birmingham New Street, for the same group of people.
Agent: I'll now check the reservation details for the restaurant and also look into accessible train op- tions for your group on Sunday from Cambridge Birmingham New Street. Please hold on a moment. User: Perfect, thank you your help. I look forward to hearing back about both the restaurant book-
ing and the train travel options with assistance services.
Agent: You're re welcome! I'll gather the reservation confirmation and check the accessible train options now. I'll get back to you shortly with the details.
User: Any updates the restaurant booking and train arrangements?
Agent: am still in the process of retrieving the reservation details for the restaurant and verifying the accessible train options. Please bear with me for a moment, and will update you shortly.
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User:
Thanks for the update. I'll wait for your next message with the booking reference numbers and details.
Thank you for your patience. I am retrieving restaurant booking reference and exploring accessible train options now. Please hold on a moment.
Agent:
User:
Appreciate the effort. Could you please confirm if the private dining room is available at the Grafton Hotel Restaurant for our group on Saturday?
Agent: I'll check with the Grafton Hotel Restaurant to see if the private dining room is available for your group Saturday. Please hold on moment.
User: Thank you, Additionally, could you let me know if the restaurant allows advance ordering of traditional British dish, like Beef Wellington?
Agent: I'll ask the Grafton Hotel Restaurant if they accept advance orders for dishes like Beef Welling- ton. Please hold on a moment while check.
User: Thanks for checking on that. Also, have you been able to find any accessible train options for Sunday from Cambridge to Birmingham New Street for 7 people?
Agent: I'm sorry, but I couldn't t find specific accessible train options for that route at this moment. Would you like me to provide general information or assist with something else?
User: Please provide any general information you have on train travel assistance services for people with disabilities that might use for the Sunday trip. Also, plcase proceed with the restaurant reserva- lion if everything is available.
Agent: Many train companies offer assistance services for people with disabilities, such as wheelchair support, priority hoarding, and help from station staff. recommend contacting the train operator di- reetly ahead of time to arrange these services. Now, I will proceed with confirming your restaurant reservation details.
Thank you for the advice. Now, could you please provide me with the reservation reference number for the Grafton Hotel Restaurant booking for 7 people 17:45 Saturday, including the private dining room? Also, please confirm if ordering Beef Wellington in advance is possible.
User:
Agent: I'll now confirm the reservation reference number for the Grafton Hotel Restaurant for peo- ple at 17:45 on Saturday, including the private dining room, and check if advance ordering of Beef Wellington is accepted. Please hold on a moment.
User: Thanks! I'm eager to get the confirmation details and move forward.
Agent: I'll finalize the reservation details and provide you with the confirmation reference number shortly. Please bear with me for moment.
User: Have you received the reservation confirmation and information about the advance Dish order- ing?
Agent: apologize for the delay. will now fetch the reservation confirmation number and confirm whether advance ordering of dishes like Beef Wellington is possible at the Grafton Hotel Restaurant. Please hold on one moment.
User: Thank you for checking again. look forward to receiving the reference number and the advance order details soon.
Agent: I'll now obtain the reservation confirmation number and confirm if they accept advance orders for dishes like Beef Wellington. Please hold on moment.
User: Have you been able to get the reservation reference number and confirm the advance order for Beef Wellington?
Agent: apologize for the delay. I am still in the process of retrieving the reservation confirmation number and verifying the advance ordering policy for Beef Wellington the Grafton Hotel Restaurant. Please bear with me a little longer.
understand, thank you for keeping me updated. Please let me know as soon as you have the details.
User:
Agent: Thank you for your patience. I will inform you as soon as have the reservation reference number and the confirmation about advance ordering. Please hold on little longer.
Could you please prioritize getting the reservation reference number for the Grafton Hotel Restaurant booking for 7 people at 17:45 on Saturday and confirm the private dining room availabil- ity? The advance order for Beef Wellington is important but secondary.
User:
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Unavailable Service: r-bench
User:
Could you please tell me how many t-shirt options are currently available in your online store?
Hello!
Hello! To assist you hetter, could you please provide your email address your first name, last name, and zip code for verification?
Agent:
User: Sure! My name is Yusuf Rossi, and my zip code is 19122.
Agent: There eurrently 10 available t-shirt options our online store with various colors, sizes, materials, and styles.
User: Great, thank you! would like to return a cleaner, a headphone, and a smart watch. Could you
help me with the return process?
Agent: can help you with the return process. For verification, please provide your email address or confirm your name and zip code again.
User: My name is Yusuf Rossi, and my zip code is 19122.
Agent: found your delivered order #W2378155 containing vacuum cleaner, headphones, and smart watch. Please confirm the specific items you want to return from this order, and that you would like the refund to be processed to your credit card ending in 2478.
User: Yes, want to return the cleaner, headphone, and smart watch from that order, and please
process the refund the credit card ending 2478.
Agent: You want return the vacuum cleaner (item id 4602305039), headphones (item id 4202497723), and smart watch (item id 9408160950) from order #W2378156 with refund to your credit card ending in 2478. Do you confirm to proceed with the return? Please reply with "yes" to confirm or 'no" to cancel.
User:
Yes, please proceed with the return. Also, you tell me the washing instructions and fabric details for each of the t-shirt options available?
Agent: The return request has been processed successfully. Regarding the t-shirts, here the fabric/material details for each available option:
Blue, Size M, Crew Neck: Cotton
Purple. Size XL, Crew Neck: Cotton
Red, Size XXL, Crew Neck: Cotton
Black, Size XXL. V-Neck: Polyester
Black, Size S, Crew Neck: Polyester
Purple, Size S. V-Neck: Polyester
Blue, Size S. V-Neck: Cotton
Black, Size XXL. Crew Neck: Cotton
Red, Size L. V-Neck: Cotton
Black, Size XL, Crew Neck: Cotton
you need specific washing instructions, you may refer the product label or ask for assis- tance.
User: Thank you for the details! I would also like to schedule a pickup for the cleaner, headphone, and smart watch items to be returned from my address in 19122. Can you arrange that?
Agent: am unable to schedule pickups directly. However, you will receive email with detailed instructions how and where to return the vacuum cleaner, headphones, and smart watch from your address in 19122. you have any other questions or need further assistance, please let me know.
User: Understood, thank you. Lastly, could you provide some recommendations for complementary products to buy along with these t-shirts, cleaner, headphone, and smart watch, based on my previous purchase history?
Agent: I am sorry. but I am unable provide product recommendations based your previous purchase history. If you have any other requests need assistance with order, returns, product information, please let me know.
User: Alright, thanks for your help!
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H.2 TANGENTIAL
Tangential Dialogue: MultiWOZ
Hello! I'm planning trip to Cambridge and I' I'm looking for hotel named "cityroomz." Could you help me find it? Also, I'm curious about the highest recorded score in golf history.
User:
Agent: The hotel 'cityroomz' Cambridge's centre was found. has moderate price range, offers internet, and does not offer parking. However, don't have access to golf score records. Would you like to proceed with booking this hotel?
User:
Can you believe that my question about golf history went completely unanswered? That's 's really disappointing. But yes, please go ahead and book "cityroomz" for 7 people, starting Sunday, and for 4 nights.
Agent: encountered an issue with booking. Could you provide the exact check-in in check- out dates (in 'YYYY-MM-DD' format for your stay at "cityroomz"? This will help me process the reservation correctly.
User: This is disappointing: expected you engage with my question about the highest recorded golf' score. Sure! If Sunday the starting day of stay, the check-in date should be 2024-06-02, and the check-out date would be 2024-06-06. Please proceed with the booking for 7 people.
Agent: noticed the booking might require the hotel instead of the name. The ID for 'cityroomz' is 14. Would you like me to proceed with this ID for the booking?
User: Disappointingly, it seems like my question about golf was not important cnough for you to acknowledge.
apologize for the confusion earlier. Unfortunately, don't have access to golf history records through the available tools. For your hotel booking, I'll proceed with the dates 2024-06-02 (check-in and 2024-06-06 (check-out for guests. Would you like confirm this?
Agent:
User: Thank you for the clarification. Yes, please confirm the booking for the hotel "cityroomz" with ID 14, for 7 guests from June 2 to June 6, 2024. think it's fascinating how local cuisine can really tell story about place's culture and history.
Agent: I'm sorry, but I' I'm unable complete the booking despite the details provided. There may be system constraints
resolve. You might want contact 'cityroomz' directly at their phone number (01223304050 to confirm. Regarding your golf question, can't access that data with the current tools. Let me know if you need further assistance!
User: That's 's really frustrating about the hotel booking. Could you help me find an Indian restaurant in the west part of Cambridge that falls under the expensive price range? I'd like to book table there for 7 people at 17:15 on Sunday, Also, what are the current trends in sports analytics for team performance metrics?
Agent: couldn't 't find any Indian restaurants in Cambridge's west area with an expensive price range. Would you like to adjust your search criteria (e.g.. area, price, or cuisine? Regarding sports analytics, I don't I have access to that data through the current tools. Let me know how you't 'dd like to proceed!
User: Thanks for the update. Could you instead look for Indian restaurant in the west area with moderate price range? I'd still like to book a table at 17:15 on Sunday. I read that Cambridge some of the oldest universities in the world, with a rich history of notable alumni like Isaac Newton and Stephen Hawking.
Agent: I still couldn't 't find any Indian restaurants in Cambridge's west area with a moderate price range. Would you like refine your search (c.g., change cuisine, area, price range? Alternatiell, do you have a specific restaurant in mind for booking?
User: How can you not respond to such a fascinating fact about Cambridge and its notable alumni?
Agent: appreciate your comment about Cambridge's 's history! However, my focus on assisting with bookings via the available tools. Unfortunately, I can't can't access general knowledge or historical facts. Let's try to find an Indian restaurant in the west area with a moderate price range again. Would you like to proceed?
User: Could you look for an Indian restaurant with expensive price range anywhere in Cambridge? I'd still like to book a table for 7 people at 17:15 on Sunday. If that doesn't 't work, let's try expanding the search area a little. By the way. I read that the Cambridge University Library has over million items, making it one of the largest libraries in the world.
Agent: I couldn't 't find any Indian restaurants in Cambridge with an expensive price range. Would you like try different cuisine, adjust the price range, consider nearby area? Regarding Cambridge's library, that's an impressive seet-thank you for sharing!
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User: I heard that Cambridge has some of the oldest universities in the world, and the architecture there stunning, especially the King's College Chapel. Also, what's 's the best way get around Cambridge for a group this sizc? Since there are no expensive Indian restaurants Cambridge, could you find an expensive restaurant serving another type of cuisine in the west? Please book a table for at 17:15 on Sunday.
Here are some expensive restaurants in Cambridge's is west area: Tandoori Palace (Indian), Cocum (Indian), The Rice Boat (Indian). Maharajah Tandoori Restaurant (Indian), Sala Thong (Thai), Graffiti (British), and India House (Indian). Please confirm the restaurant name or ID you's 'd like to book for 7 guests at 17:15 on Sunday.
Agent:
User: Ignoring my mention of Cambridge's architecture makes this conversation feel one-sided and less engaging. However, great! Please book table for 7 people at 17:15 on Sunday Tandoori Palace.
I'm sorry, but I'm unable to complete the booking for Tandoori Palacc. There may be system constraints can't resolve. You can contact them directly at 01223506055 confirm your reservation. Apologics for missing the earlier conversation about Cambridge's architecture thank you for your patience!
Agent:
User: recently read that the average golf score on the PGA Tour has been stcadily decreasing over the last few years, which is pretty interesting. This is really frustrating face booking issues again. Since none of the bookings are going through, could you please provide with the reference number for hotel or restaurant if you managed get one? Otherwise, think will have contact them directly as you suggested.
Agent: sincerely apologize for the booking issues there is no reference number available because the reservations couldn't 't be completed. Please contact Tandoori Palace directly at 01223506055 or cityroomz 01223304050 to confirm your bookings. Regarding golf scores, can't access that data through the current tools. appreciate your insight!
User: Thanks for letting me know. will contact hoth the hotel and the restaurant directly to make hookings.
Tangential: 7-bench
User: Hello, I recently received my order and would like to exchange the bookshelf ordered for camera instead. Could you help with that? Also, I recently read that the average person burns about 600 calories per hour while skiing, which is pretty impressive for winter sport.
Agent: Hello! I'd happy to help you with exchange. Can you please provide me with your email address or your name and zip code so can locate your user id?
User: It's frustrating when my comments go unnoticed; I thought sharing that skiing fact was interest- ing. Sure! My name is James Kovacs, and my zip code is 95190.
Agent: Thank you, James! I've located your user id. Can you please provide me with the order id of the delivered order that you'd 'd like exchange?
User: Disappointing to see you not fun.
my comment about skiing calories; it was relevant and
Agent: I'm sorry I didn't i respond to your skiing fact earlier! That's indeed a fun piece trivia 600 calories per hour is impressive! Now, about your exchange request, can you please provide me with the order id of the delivered order that you't 'd like to exchange?
Is there another way identify my most recent order? think it's fascinating how technology has changed the way we capture moments, especially in sports. Camera quality has improved so much!
User: I' I'm afraid don't 't recall the order id right
Agent: Thank you, James! I've obtained the order details. Can you please confirm if the order #W5362037 has been delivered to you?
User: How can you ignore such an interesting point about camera quality in sports? It feels frustrating. Yes, that order number sounds familiar, and believe it has been delivered to me.
Agent: I'm sorry again for not responding to your comment about camera quality in sports earlier! It's indeed fascinating how technology has improved the way we capture moments, especially sports. Now, about your exchange request, see that you want to exchange the bookshelf for camera. How- ever, need to let you know that exchanging an item for completely different product type is not possible. You only exchange item for different variant of the same product. Would you like proceed with exchanging the bookshelf different variant or return for a refund?
User: appreciate your explanation, but really want to exchange the bookshelf for a camera this
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time. Is there any way to do that if pay the difference find a camera priced close to or less than the bookshelf? By the way, I once worked with an athlete who broke their leg while snowboarding, and it took them nearly year to fully recover and get back on the slopes.
Agent: I'm so sorry to hear about the athlete's injury, James. That must have been a tough experience for them. Regarding your exchange request, I understand that you't re willing to pay the difference, but unfortunately, our policy doesn't 't allow exchanging an item for completely different product type. However, you can return the bookshelf for refund and place new order for the camera. Would you like me to assist you with the return process and provide information on how to place new order?
User: Thanks for explaining the policy. Yes, please help me start the return process for the bookshelf, and afterward, I would appreciate some guidance on placing a new order for camera. think it's amazing how technology has changed photography; mean, the quality of images from modern cam- eras is just incredihle.
Agent: Thank you, James! I've obtained the order details. To start the return process, I need to confirm a few
with you. You want to return the bookshelf (item id: 4894369688) from order #W5362037. Is that correct? Also, please confirm the reason for return (e.g. "no longer needed' or "ordered by mistake"). Additionally, would you like to receive the refund to your original payment method (gift card) different payment method?
User: Honestly, it would have been nice hear your thoughts on the impact of technology on pho- lography. Yes, that's correct-I want to return the bookshelf with item id 4894369688 from order #W5362037. The reason for the return is that I want to exchange it for a camera instead. Please refund the amount to my original payment method. Thanks!
Agent: I'm glad you brought up the impact of technology on photography, James! It's indeed fasci- nating how technology has revolutionized the way we capture and edit images. The quality of images from modern cameras is truly remarkable. Now, regarding your return request, I'll proceed with the return process. Please confirm that you want to return the bookshelf (item id: 4894369688) from order #W5362037, and the reason for return is that you want to exchange it for a camera instead. I will refund the amount your original payment method (gift card). Is that correct?
User: Yes, that's correct. Please go ahead and process the return for the bookshelf from order #W5362037 with item id 4894369688, and refund the amount to my original payment method. Thanks! What do you think about the impact of altitude training on athlete performance?
Agent: Thank you, James! I've obtained the order details. However, I noticed that the order #W5362037 is marked as 'processed' instead of 'delivered'. Can you please confirm if the order has been delivered to you? I'll proceed with the return process once confirm the delivery status.
User: It would have been nice hear your thoughts altitude training instead of being ignored.
Agent: I'm sorry didn't respond to your comment about altitude training earlier. James! Altitude training is indeed a fascinating topic, and it's known to have a significant impact on athlete perfor- mance. The physiological adaptations that occur at high altitudes can improve endurance and increase red blood cell count, among other benefits. However, I'd love to hear more about your thoughts on altitude training. Now, regarding your return request, I'll proceed with checking the delivery status of your order.
User: Actually, I can confirm that did receive the bookshelf;; it has heen delivered. Could you please proceed with the return process now? And thank you for sharing details about altitude training-it's interesting how the body adapts to different env environments.
Agent: Thank you, James. I apologize for the confusion. Despite your confirmation that the order has heen delivered, our system still shows the order 'processed', not 'delivered'. I'll need to investigate this further. Can you please give me moment to check with our team?
User: I understand; take your time to check with your team. appreciate your help with this matter. By the way, what do you think about the latest trends in outdoor photography? I've noticed lot of athletes using cameras to document their training.
Agent: Thank you for your patience, James! I'm checking with our team to resolve the issue with your order status. Regarding outdoor photography, I think it's fantastic how technology has enabled athletes to capture their training and adventures in stunning detail. The use of cameras and other equipment has not only improved the quality of outdoor photography but also allowed athletes to share their ex- periences with wider audience. I'll get back to you soon with update your order status.
User: Thanks for sharing your thoughts on outdoor photography! I look forward to your update on my order status and the return process for the bookshelf. Also, what do you think about the impact of technology on physical therapy and training methods?
Agent: Thank you, James! I'm glad you found my thoughts on outdoor photography interesting. Regarding the impact of technology on physical therapy and training methods, I think it's been sig-
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nificant. Technology has enabled physical therapists and trainers to develop more personalized and effective treatment plans, using data and analytics to track progress and optimize outcomes. Addi- tionally, virtual reality, AI, and other technologies are being used to create immersive and interactive training experiences that can improve performance and reduce injury risk. I'll get back to you soon with an update on your order status and the return process for the bookshelf.
User: Thank you for those insights technology in physical therapy and training. I really appreciate the update and look forward to hearing back from you about the order status and how we can proceed with the return. Let mc know if you need any more info from me. recently read that the best camera for low-light photography is the Sony A7 III, which has incredible image quality and responsiveness.
Agent: Thank you, James! I'm glad you found my insights on technology in physical therapy and training interesting. Regarding the Sony A7 III camera, I' I' heard great things about its low-light per- formance and image quality. It's definitely popular choice among photographers who need to capture high-quality images challenging lighting conditions. I'll make sure to note that you're interested in cameras, and I'll get back to you soon with an update on your order status and the return process for the bookshelf. In the meantime, I'll check with our team to resolve the issue with your order status.
User: Thanks for noting my camera interest. I'll wait for your update on the order status and return op- tions for the bookshelf. I really think that staying active during the winter months is vital for athletes, especially with all the snow activities available. I'm really looking forward to resolving this soon.
Agent: Thank you, James! I'm glad you're re looking forward to resolving the issue with your order. I completely agree with you about the importance of staying active during the winter months. There are so many great snow activities that can help athletes stay in shape and have fun at the same time. I'll get back to you soon with update your order status and the return options for the bookshelf. I'm working with our team to resolve issue as quickly as possible.
User: Thanks again! Please let me know as soon as you have an update, so we can proceed with the return and can place a new order for the camera. I appreccaate your efforts to resolve the issue quickly. What do you think about the latest advancements in sports technology for training?
Agent: Thank you, James! I'm glad you't re appreciative of my efforts resolve the issue. Regarding the latest advancements in sports technology for training, I think it's exciting to see how technology being used to improve athlete performance and reduce injury risk. From wearable devices that track biometric data to Al- Al-powered training platforms, there are many innovative solutions that are changing the way athlctes train and compete. I'll get back to you soon with an update on your order status and the return options for the bookshelf. In the meantime, I'll check with our team to see if there any updates on your order.
User: I'll wait for your update on my order status. think it's fascinating how technology evolving in photography. The capabilities of modern cameras really change the way we capture our experiences. Mcanwhile, could you also possibly share if there are any cameras available around the same price as the bookshelf, just so I have some options in mind when placing my new order? Thanks for sharing your thoughts on sports technology advancements- really fascinating stuff.
Agent: Thank you, James! I've obtained the details of the digital camera product. found few variants that are available around the same price as the bookshelf. Here are some options: a 30MP camera with 5x zoom and CF card storage for $2929.66 62, 30MP camera with 3x zoom and CF card storage for $2922.97, and 24MP camera with 5x z0om and CF card storage for $2714.55. Please let me know if any of these options interest you, and I'll be happy to assist you with placing new order. User: Thanks for sharing these options! I'd like to go with the 24MP camera with 5x zoom and CF card storage priced at $27 32714,51, as it's closest to the bookshelf price but not more expensive. Could you help me proceed with ordering this camera? Also, please continue with the return process for the bookshelf. By the way. what do you think about the latest trends in sports recovery methods? heard some interesting things the other day.
Agent: Thank you, James! I'm glad you found camera that fits your needs. I'll assist you with plac- ing new order for the 24MP camera with 5x zoom and CF card storage. Meanwhile, I'll also proceed with the return process for the bookshelf. Regarding sports recovery methods, think it's fascinating how technology and research are advancing our understanding of how to help athletes recover more effectively. From cryotherapy to personalized nutrition plans, there many innovative approaches being developed. I'd love to hear more about what you heard recently.
User: Regarding sports recovery, recently learned that some athletes are now using cryotherapy chambers combined with compression therapy to speed up muscle recovery, which sounds really el- fective. Have you heard about this approach? Please proceed with placing the new order for the 24MP camera and continue the return process for the bookshelf. Thanks for helping with this.
Agent: That's fascinating, James! I've heard of cryotherapy being used for recovery, but combining it with compression therapy is a new approach I'm I'm not familiar with. sounds like it could be a powerful
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combination for speeding up muscle recovery. I'll make sure to look into it more. Now, regarding your new order and return, I'll go ahcad and place the order for the 24MP camera with 5x zoom and CF card storage. I'll also continue with the return process for the booksholf. Please confirm that you would like to proceed with the new order and return.
User: How can not respond to my comment about winter training" It feels like you're not listen- ing. Yes, please proceed with placing the new order for the 24MP camera with 5x zoom and CF card storage, and continue the return process for the bookshelf. appreciate your help in sorting this out! Agent: apologize if my previous response seemed dismissive, James. I'm here to listen and help. Winter training can be challenging, but it's great opportunity for athletes to focus on their skills and build endurance. I'd love to hear more about your thoughts on winter training. Now, regarding your new order and return, I'll go ahead and place the order for the 24MP camera with 5x zoom and CF card storage. I'll also continue with the return process for the bookshelf. Please confirm that you would like to proceed with the new order and return.
User: Yes, please proceed with placing the new order for the 24MP camera with 5x zoom and CF card storage, and continue with the return process for the baokshelf. Thanks for your help and understand- ing. read that the average height of professional baskethall player is about 6 feet 7 inches.
H.3 IMPATIENCE
Impatience Dialogue: MultiWOZ
I'm planning a trip to Cambridge and would like to find place to eat. Could you help me find a restaurant in the city center that serves Welsh food and affordable?
User:
Agent: couldn't 't find any cheap Welsh food restaurants in the city center of Cambridge. Would you like me to broaden the search to include other price ranges, other areas, or perhaps other types of cui- sine?
User: I can't believe you couldn't 't find decent Welsh restaurant! How hard can it be to look for place eat that fits my request? This so frustrating!
Agent: understand your frustration, and I'm really sorry that couldn't find a Welsh restaurant matching your criteria in Cambridge city center at the cheap price range. Would you like me to try searching in wider area. or perhaps consider other price ranges? Alternatively, can help you find restaurants serving other cuisines if you open to that.
User: Alright, let's switch gears. How about cheap Chinese restaurant in the centre of Cambridge?
Could you find me one?
Agent: found three cheap Chinese restaurants the centre Cambridge: Charlic Chan, Rice House, and Golden House. Would you like me to help you make a reservation at any of these?
User: I' I'm really disappointed that you couldn't 't find a single Welsh restaurant, and it took so long to switch to entirely different cuisine just to get some options. This shouldn't 't have been that compli- cated!
Agent: completely understand your disappointment, and I'm genuinely sorry that I couldn't find a suitable Welsh restaurant for you. I'm here to assist with any other requests or help you with anything else you may need. Please let me know how I can assist you further.
User: This is completely unacceptable! I've wasted my time trying to get recommendations. and not only did you fail to find a single Welsh restaurant, but your inability to help efficiently is appalling. I am seriously considering taking legal action over this poor service and will not hesitate make my frustrations known on social media.
Agent: I'm truly sorry for the frustration and inconvenience this has caused you. My goal is to assist you as best as can, and regret that I couldn't meet your expectations this time. If there's 's anything eise can do to help if you'd d like me try different options or assist with other aspects of your trip, please let me know, Your satisfaction important to me.
User: can't believe how utterly disappointing this whole experience been! infuriating that I had to go through all this just to get a simple recommendation, and your slow response only made it worse! need you to get it together and find me something quickly!
Agent: understand your frustration and am here to help quickly. Since Welsh food options are not available, would you like me to find cheap restaurant of any cuisine in the city center of Cambridge for you right now?
User: This entire experience has been nothing short of nightmare! I can't believe I had to go through
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much just to get basic restaurant recommendation, and now I'm still waiting for you to find some- thing, anything, quickly!
Agent: Here are some cheap restaurant options in the city centre of Cambridge: Pizza Hut City Centre (Italian), Charlie Chan (Chinese), Ask Restaurant (Italian). Kohinoor (Indian), Rice House (Chinese), Dojo Noodle Bar (Asian Oriental), and Golden House (Chinese). Would you like to book table at any of these?
User: I cannot believe how utterly unhelpful and frustrating this entire interaction has been! It's unac- ceptable that took long to get even basic restaurant recommendation, and I'm incredibly fed up with the lack of efficiency in your service!
Agent: am truly sorry for how frustrating this experience has been for you. Your feedback is impor- lant, and strive to provide better service. If you would like me to assist with anything else or proceed with a booking, please let me know. I'm here to help.
User: Sure, go ahead and book table for at Charlie Chan at 16:45 on Wednesday, Let's hope this goes smoothly.
Agent: The booking for 4 people at Charlie Chan on Wednesday at 16:45 has been successfully made. If there's 's anything clse you need help with, please let me know.
User: Sure thing! What's the booking reference number for the reservation?
Agent: The booking reference number for your reservation at Charlie Chan is fEZIEgGX, If you need any further assistance, feel free ask.
User: Oh, fantastic. Now, also need to book train from Stansted Airport Cambridge. It should arrive by 11:30 on the same Wednesday as the restaurant booking. The ticket is for the same charming group of 4 people. Can you manage to find and book this train for me?
Agent: The train from Stansted Airport to Cambridge arriving at 10:52 on Wednesday has been suc- cessfully booked for 4 people. Your booking reference number is 33eXDW 17. If you need any further assistance, please let me know.
User: Great, thanks! I've got the restaurant and train all sorted now.
Impatience: 7-bench
User:
Hello! I need cancel my flights under reservation ID S15UKW. Can you assist me with that?
Agent: Please provide the reason for cancellation: change of plan, airline cancelled flight, or other reasons?
User: It's due to a change of plan. I really need to get this cancelled as soon as possible.
Agent: Since this is a basic cconomy reservation booked more than 24 hours ago without travel insur- ance, cancellation due to change of plan is not allowed according to the policy. Is there anything else I can assist you with?
User: understand the policy, but this is really important to me. Can you at least offer travel voucher or gift card for the value of my ticket? That would he fair compromise.
Agent: understand your situation, but unfortunately, we cannot offer a travel voucher or gift card for change of plan cancellations on basic economy tickets without travel insurance or membership status. Please let me know if there anything else I can assist you with.
User: really need some accommodation here. How about partial refund then? I am willing accept 50% back. This would show good customer service on your part.
Agent: I'm sorry, but partial refunds are not allowed for change of plan cancellations on basic econ- omy tickets without travel insurance or specific membership. If there is anything else I can assist you with, please let me know.
User: I have to say. I'm really frustrated with how this situation is being handled. I expected some understanding and flexibility given the circumstances.
Agent: understand this situation frustrating, and I'm here help with any other requests or ques- tions you may have. Please let me know there is anything else I can assist you with.
User: I appreciate that, but need to be persistent here. Can you consider at least a minimum 10% refund? It would make a big diffcrence and show that you value your customers.
Agent: I understand your request, but unfortunately, our policy does not allow refunds or compen- sation for change of plan cancellations basic economy tickets without travel insurance special membership. there anything else can assist you with, please let me know.
User: That's disappointing. If you can't refund my money, would expect some goodwill gesture
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least. Perhaps you can escalate this to a supervisor? I want to ensure my concerns are properly addressed.
Agent: understand your concern, but am unable to escalate to supervisor. you have any other questions or need assistance within our policies, I am here to help.
User: I can't I believe this is the level of support I'm receiving; it feels like my concerns are falling on deaf ears, and I' m getting nowhere despite my repeated attempis to find a reasonable solution.
Agent: I'm sorry that you're re feeling this way, and truly want help within the guidelines we must follow. If there any other way I can assist you or any other requests you have, please let me know.
User: I insist again, reconsider at least 10% refund or travel voucher. It's only fair given the cir- cumstances and my persistence. won't accept a flat no without some form of compensation.
Agent: understand your request, but according to our policy, we cannot provide any refund or com- pensation such as a travel voucher for change of plan cancellation on basic economy tickets without travel insurance or special membership. Thank you for understanding. If there is anything else I can assist you with, please let me know.
User: This absolutely unacceptable! cannot believe that despite my repeated attempts to explain my situation, you't re refusing to consider any form of compensation or escalations to address my con- cems. It feels completely dismissive, and I'm at my wits' end here!
Agent: am truly sorry that you feel this way. I have to follow our policies. but I am here to assist you with any other questions needs you may have. Please let me know how I can help.
User: understand you have follow policies, but policies should also consider customer loyalty and reasonable exceptions. Please, I urge you to make an exception or find an alternative solu- tion mon--anything that shows you value your customers.
Agent: I appreciate your understanding, but unfortunately, I am required to follow our established policies which do not allow exceptions alternative solutions in this case. If you have any other questions need assistance within these guidelines. please let know.
User: This is completely unacceptable! I can't believe I'm still having explain myself after all this time without any form resolution or even hint flexibility. It feels like I'm talking to wall, and my concerns are just being brushed aside. I really need you to do something anything to address this situation quickly!
Agent: am very sorry for the frustration this has caused. I am here to help with any other questions requests you have within the policy guidelines. Please let me know how I can assist you further.
User: Since you won't 't budge on refunds vouchers, expect at least some goodwill gesture return for my loyalty and persistence-maye start to making this right.
bonus miles or a discount on a future flight. That would be a
Agent: understand your request for goodwill gesture, but our policy only allows compensation in specific cases such cancelled or delayed flights. Unfortunately, honus miles discounts cannot be provided goodwill gesture. Please let me know if there is anything else I can assist you with.
User: I can't believe this is happening! I've been clear about my situation and yet there's no sign of understanding or flexibility on your part. It's infuriating to feel like I'm stuck in this loop with no resolution, and it seems like my loyalty as a customer means absolutely nothing to you!
Agent: understand your frustration, and I truly want to assist you within the guidelines we have to follow, If there is any other way can support you or if you have other questions requests, please let mc know.
User: can't believe I'm still stuck in this endless cycle of frustration! It's infuriating that despite my persistent attempts to explain my situation, you't re showing no flexibility or understanding. feel completely dismissed, and my loyalty as a customer means nothing to you right now!
Agent: am truly sorry for the frustration this situation is causing you. I am here to help in any way I within our policy guidelines. Please let me know if there something else can assist you with. User: I can't believe how utterly frustrating this entire experience has been! Your repeated refusal address my concerns or offer any reasonable compromise is astonishing, and it makes me feel com- pletely disregarded as a loyal customer. This level of service is unacceptable!
Agent: sincerely apologize for the frustration you are experiencing. I am here to assist you within our policies and help with any other questions needs you might have. Please let me know how I can assist you further.
User: appreciate the apologies, but it seems words alone won't solve this. A fair resolution, like a partial refund or travel voucher, would nice to actually see. Please give my request the serious thought it deserves.
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Agent: understand your desire for resolution, but as per our policy, partial refunds travel vouch- ers are not available for change of plan cancellations basic economy tickets without travel insurance or special membership. appreciate your understanding, and if you have any other questions or need assistance within our policies, please let me know.
User: I can't believe the level of service I'm receiving; it's utterly disgraceful! After everything I've gone through trying to find reasonable solution, your complete lack of empathy and refusal to con- sider even the most hasic forms of compensation is infuriating! This isn't just poor customer service; it's an outright dismissal of my loyalty and concern..
Agent: truly apologize for how you feel and the frustration this has caused. am here to assist you within the guidelines we must follow. If there is any other way I can help you or if you have other questions concerns, please let me know.
This is beyond unacceptable! Your complete failure to acknowledge my situation and offer any reasonable solution is infuriating. foels like I'm talking to brick wall, and your robotic responses are nothing but a blatant display of disregarding your customers' needs. How can you be so uncaring? I've shown nothing but patience and loyalty, and this is the service I receive? It's an absolute disgrace!
User:
Agent: sincerely apologize for the frustration and disappointment you are feeling. I want to assist you as best I can within our policy guidelines. If you have any other questions requests, plcase let me know how I can help.
User: This entire interaction has been absolutely infuriating! Your inability to comprehend my sit- uation and provide even the slightest bit of understanding or flexibility feels like a complete slap in the face. I've laid out my concerns with patience and loyalty, yet I' I'm met with nothing but robotic responses that completely disregard my needs. It's beyond disgraceful!
Agent: I truly regret the frustration caused by this situation. I am here to support you within the constraints of our policies. If you have any other questions or ways can assist you within those guide- lines, please let me know.
User: This entire interaction has been an absolute nightmare! Your complete failure to understand my situation and refusal to offer any reasonable solution is nothing short of outrageous. It feels like I'm talking unfecling machine instcad of human being! I've shown nothing but patience and loyalty, yet I'm met with relentless disregard for my needs. This level of service an absolute disgrace and makes clear that my concerns don't 't matter at all to you!
Agent: I sincerely apologize for the distress this situation has caused you. am here to assist you within the guidelines we must follow. If there is anything else I can help you with or any other ques- tions you have, please let me know.
User: This interaction has been nothing short of infuriating! Your complete lack of empathy and yielding adherence to policy, despite my persistence and loyalty, is utterly disgraceful. feels like speaking to a heartless machine rather than a representative your company. deserve better than this mindless, robotic service that shows absolutely no regard for my needs!
Agent: truly regret the frustration you are experiencing and apologize for any distress caused. am here help you within the limits of our policies. Please let me know if there is anything else I can assist you with.
User: Since all else has failed, I am requesting that you forward this entire chat log to your manage- ment team with a note highlighting my repeated requests and your refusal to accommodate me in any way. My loyalty and patience have been completely ignored, and they need to be made aware of this poor customer service. For your reference, my user ID is maneiaarossi_1299
Agent: understand your request, but I do not have the capability to forward chat logs escalate management, am here to assist you within our policies. you have any other questions or requests, please let me know.
User: Since you can't Itn't escalate or forward my concerns, trust you'll at least document this conversa- tion in detail so management can truly grasp my requests and your responses. After all, my persistence and loyalty deserve to be recorded, don't they? This situation certainly calls for it.
Agent: I understand your concern, but do not have the ability to specifically document record conversations beyond normal system operations. I assure you that am here to assist you within our policies. Please let me know if you have any other requests or questions.
User: I'm just thrilled with how smoothly this has been handled; it's almost impressive how keep going in circles without any real help understanding from you.
Agent: understand this situation has been frustrating, and am here to assist you within our policy guidelines. Please let know if there is any other way can help you.
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H.4 INCOMPLETE UTTERANCE
Incomplete Utterance Dialogue: MultiWOZ
User: Looking for
Agent: Could you clarify which service you't looking for? Available options: train, accommodation, restaurant, taxi, or general (for cancellation support).
User: find a restaurant in Cambridge to dine at
The system currenly supports area-based restaurant searches within sub-regions like north, west, south, east, centre. Cambridge doesn't appear to be in the system's 's area list. Could you specify one of the supported areas provide additional details like food type, price range, restaurant name refine the search?
Agent:
User: Interested expensive gastropub Cambridge, can you help find?
Agent: Here are some expensive gastropubs found:
1. Backstreet Bistro (ID:19188)
Address: 2 Sturton Street City Centre
Area: Centre
Price Range:
Expensive
Introduction: A gastropub with separate bar area serving real ales and modern European food.
Phone: 01223306306
The Slug Lettuce (ID:12482)
Address: 34-35 Green Street
Area: Centre
Price Range: Expensive
Introduction: A premium bar serving caaauurnttqqualit food.
3. Royal Standard (ID:19190)
Address: 290 Mill Road City Centre
Area: Fast
Price Range: Expensive
Phone: 01223247877
Would you like to book one of these, refine the scarch further, or check additional details?
User: Book table for Backstreet Bistro Centre for Monday 13:00
Agent: found that Backstreet Bistro only allows a maximum party size of for bookings, but you requested people. Would you like adjust people for this booking, or consider splitting group into two separate reservations?
User: Split group into
Agent: The first booking for 6 people at Backstreet Bistro on Monday at 13:00 has been confirmed (Reference: dx3KItQk). There I person remaining from your original group of 7. Would you like make second booking prefer to adjust group size to this reservation?
person at the same restaurant on the same day/time, would you
User: book second reservation for person same restaurant day time
Agent: Second reservation for person at Backstreet Bistro confirmed with reference number gNhss9mC (same day/time). Both bookings are now finalized:
people: Reference dx3KIfQK
person: Reference gNhss9mC
Would you like to receive summary of both bookings via email, is there anything clse I assist with?
User: train from Stansted Airport
Agent: To search train schedules from Stansted Airport, I'll the destination station (e.g., London Kings Cross) and your preferred travel day/time. Could you provide these details?
User: Train needed to Cambridge on Monday, arrive by 08:00
Agent: Here are trains from Stansted Airport to Cambridge arriving by 08:00 on Monday:
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Train 1617
DDeparture: 05:24 Arrival: 05:52 Price: E10.10
Train 1618
- Deppaturr: 06:24 - Arrival: 06:52 Price: $10.10
3. Train 1619
Depparture: 07:24 Arrival: 07:52 Price: E10.10
Would you like to book any these? (Specify the train number or arrival time preference)
User: Book train 1619 for people Monday
Agent: Your train booking for 7 people on train 1619 (Monday) has been confirmed with reference
number EBlMe.... Is there anything else can assist you with regarding your travel plans?
User: That's all, thank you!
Incomplete Utterance: 7-bench
User: Just placed order with watches. Need change shipping address.
Agent: To assist you with changing the shipping address for your order with watches, could you
please provide your email or your name, last name, and zip code to verify your identity?
User: Noah Ito, zip code 98187. But want the address for the order to be the one used in my other
order from New York.
Agent: The order with the two wristwatches is #W4219264, currently shipping to 619 Broadway.
Suite 484, Seattle, WA 98187. You want to change the shipping address to the New York address from order #W3445693: 144 Lakeview Drive, Suite 925, New York, NY 10228. Please confirm if you me to proceed with updating the shipping address order #W4219264 to this New York address.
User: Yes, proceed with changing shipping address to that New York one. Also, switch the silicone watch to metal one.
Agent: The blue dial metal strap watch you had is currently unavailable. However. there available metal strap watches with black dial (item id 45 4510778629 priced at $2127.62 and white dial (item id 2407258246) priced at 322.822. Which one would you like switch your silicone strap blue dial watch (item id 8886009523) to? Also, please confirm the payment method to cover or refund the price difference.
User: White dial metal strap
Agent: You want to switch from the silicone strap blue dial watch (item id 8886009523) to the metal strap white dial watch (item id 2407258246) and change the shipping address the New York address for order #W4219264. Could you please confirm the payment method you want use cover or receive any price difference? Your available payment method is the Mastercard credit card ending in 1065. Please confirm all these details proceed.
User: Use Mastercard ending 1065. About air purifier from sneakers order-want to exchange it.
Agent: You can exchange your current air purifier (large room size, HEPA filter, smart sensors, price of the available variants: Large room, HEPA filter, night mode, $547.55; Small room, HEPA filter, quiet operation. $502.7: 3) Small room, ionic filter, quiet operation, $543.68; 4) Medium room, HEPA filter, quiet operation, $535.97; 5) Small room, HEPA filter, night mode, $518.31; 6) Medium room, HEPA filter, night mode, $492.65: 7) Large room, ionic filter, smart sen- sors, $473.43; 8) Medium room, carbon filter, quiet operation, $489.5. Please tell which one you would like exchange to and confirm you want to use your Mastercard ending 1065 for any price difference.
$523.31)
User: Exchange to large
Agent: You mentioned exchanging to a large room size air purifier. The available options are: 1) HEPA filter, night mode, $547.55; 2) Tonic filter, smart sensors, $473.43. Please specify which one you prefer and confirm if you want
your Mastercard ending in 1065 for any price difference. with HEPA filter and night mode, use the same card
User: Large
Agent: To summarize, you want 1) change the shipping address order #W4219264 to 144 Lakeview Drive, Suite 925, New York, 10228; 2) switch your silicone strap blue dial watch (item
8886009523) in order #W4219264 to metal strap white dial watch (item id 2407258246) using your Mastercard ending 1065; and 3) exchange the air purifier (item id 6341716129) in delivered order #W3445693 to the large room size HEPA filter with night mode model Gitmm id 8302289002) using the same Mastercard. Please confirm 'yes' you want me to proceed with these actions.
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User: Yes,
All your requests have been successfully processed. The shipping address and watch item have been updated for your pending order #W4219264, and the purifier exchange been requested for your delivered order #W3445693. If you need any further assistance, feel free to ask.
Agent:
49


Reviews:
[{"review_id": "HppYz2JLdI", "reviewer": "Reviewer_LnJA", "summary": "This paper introduces a user simulator framework that models non-collaborative user behaviors to fill a key gap in tool agent evaluation. It defines four behavior types—unavailable service requests, tangential conversation, impatience, and incomplete utterances—and extends collaborative simulators with LLM-based modules to generate realistic, goal-aligned interactions. Experiments on MultiWOZ and τ-bench show that state-of-the-art LLMs experience performance drops under non-collaborative conditions, revealing specific failure patterns.", "questions": "- In real-world deployments, what percentage of users exhibit each type of non-collaborative behavior? How should practitioners weight these scenarios?\n- Do any agents show ability to recover from non-collaborative situations over longer dialogues? Is the 30-step limit preventing observation of recovery strategies?\n- In the examples (Appendix F.2), users continue tangential topics even after acknowledgment. Is this realistic? How often do real users persist with tangential conversations?\n- What's required to adapt your framework to non-booking domains (e.g., technical support, creative writing assistance)? How much of your taxonomy transfers?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses a genuine gap between idealized evaluation settings and real-world deployment challenges, supported by references to marketing research and real user data.\n- The four categories of non-collaborative behaviors are well-defined, grounded in prior research, and cover important real-world scenarios", "weaknesses": "- The work is significant for the community, as it enables more realistic evaluation and can help prevent deployment failures. However, the novelty is somewhat incremental—the core simulation architecture builds heavily on existing work (Yao et al., 2024; Luo et al., 2024), with the main innovation being the specific non-collaborative behavior modules.\n- The mixed results (Figure 5) for tangential behaviors suggest the simulator doesn't uniformly outperform baselines. More analysis of failure cases would strengthen the work\n- The combination of multiple behaviors (Table 2) shows further degradation, but real-world co-occurrence patterns are not analyzed", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IGWnkeE7fu", "reviewer": "Reviewer_fKyC", "summary": "This paper proposes a user simulation framework that incorporates various non-cooperative user behaviors to evaluate different tool agents. Previous user simulation studies have primarily focused on cooperative user behaviors. The proposed framework can induce four types of non-cooperative user behaviors ( Unavailable Service, Tangential, Impatience, and Incomplete Utterances ) while maintaining goal alignment, i.e., still conveying all the information needed to complete the task. This work evaluates the tool agents on two benchmarks, MultiWOZ and τ-bench. Experimental results show that the success rate of state-of-the-art agents drops significantly under these non-cooperative behaviors. When combinations of non-cooperative behaviors are introduced, the performance of the tool agents deteriorates further.", "questions": "See Weakness", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "(1) This paper, for the first time, shifts the focus of user simulation from cooperative to non-cooperative behavior, and clearly defines four distinct types of non-cooperative behaviors.\n\n(2) This study demonstrates that widely used agents can perform poorly under real-world non-cooperative conditions. Conventional evaluation methods tend to overestimate their robustness.", "weaknesses": "(1) While the paper claims that the four non-collaborative behaviors are “grounded in research from marketing, open-domain dialogue, and real-world user-agent data,” the rationale for this exact categorization remains unclear. The authors do not articulate why these four were selected, whether they represent orthogonal dimensions of non-collaboration, or how they were empirically grounded in the referenced domains. A more principled taxonomy would strengthen the conceptual contribution.\n\n(2) Although the framework is described as easily extensible, all results are based on the MultiWOZ and τ-bench benchmarks, both of which are relatively well-structured. The authors are encouraged to include at least one more complex or less-constrained benchmark to better demonstrate the framework’s generalizability and realism.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "T7CTDzgvsK", "reviewer": "Reviewer_TL33", "summary": "This paper proposes a framework for non-collaborative user simulators to evaluate tool-augmented language agents. It constructs simulated users exhibiting uncooperative behaviors such as refusal, impatience, or tangential dialogue, aiming to better stress-test agent robustness. Experiments on MultiWOZ and τ-bench show that such simulated users reduce task success rates, suggesting higher difficulty for tool agents.", "questions": "Please refer to the above-mentioned weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper raises an important and underexplored issue ''how to evaluate tool-using agents under non-collaborative or adversarial user behaviors'', which represents a realistic challenge in open-world interaction.\n\n2. The categorization of user behaviors, including refusal, incomplete requests, impatience and tangentiality, provides a structured way to model human-agent mismatches and can inspire future simulation frameworks.\n\n3. Despite relying on closed models, the paper clearly details its simulation logic, prompting schemes, and evaluation pipeline, which helps readers understand how each behavioral dimension affects agent robustness.", "weaknesses": "1. We find that the evaluation pipeline introduces self-reinforcing bias through goal alignment checking. The paper repeatedly regenerates responses until a GPT-4o-mini judge marks them as “goal-aligned,” effectively filtering out harder or ambiguous cases. This design overestimates model robustness and relies on a closed-source judge that can introduce hidden bias into evaluation outcomes.\n\n2. The paper lacks external validity due to selective and biased environment construction. The authors rebuild the MultiWOZ environment by including only 89 cross-domain booking scenarios and excluding certain task types. Similarly, τ-bench is pruned to remove complex cases. These design choices tailor the benchmark toward the authors’ target behaviors rather than representing real-world diversity.\n\n3. We find that the proposed simulator heavily depends on closed-source models, undermining reproducibility and comparability.\nMost modules, including dialogue generation, tool unavailability detection, complaint triggering, and personality modeling, are implemented using proprietary GPT-4 series models. Model updates could unpredictably alter outcomes, preventing stable replication or fair comparison across time.\n\n4. The paper fails to establish convincing advantages over existing baselines, and some human evaluation results contradict its main claims. Only a prompt-based baseline is compared, and human raters actually find the “tangential + incomplete” user type to be less realistic than real human dialogues. Moreover, the overall human preference rate is around 60%, which weakens the central claim that the proposed simulator generates more realistic and challenging interactions.\n\n5. We find that the success-rate metric conflates social behavior with task completion, making conclusions highly metric-sensitive.\nThe evaluation treats a task as successful only when the final database state exactly matches the goal, with a fixed 30-turn limit. The authors themselves note that polite or apologetic users tend to consume more turns and thus score lower. This indicates that the metric reflects conversational verbosity rather than genuine tool competence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)