$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions

Rishitosh Kumar Singh; Amir Saeidi; Shamanthak Hegde; Shrinidhi Kumbhar; Chitta Baral

cs.AIOct 14, 2026

$τ$ -Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions

Authors: Rishitosh Kumar Singh, Amir Saeidi, Shamanthak Hegde, Shrinidhi Kumbhar, Chitta Baral

Organizations: Arizona State University

Abstract

In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users. Yet existing benchmarks largely assume cooperative and behaviorally stable users, leaving the effect of emotional variation on agent reliability underexplored. We introduce $τ$ -Emotion-Bench, a ten-domain benchmark for evaluating tool-calling agents under controlled emotion-conditioned user interactions while preserving the underlying task objective. To scale both benchmark construction and agent training, we introduce Tracer, a scalable agentic data-generation pipeline that constructs grounded, policy-valid tasks and collects trajectories through an interactive agent--user--environment harness. By allowing dissatisfied users to terminate inefficient interactions, the harness provides an interaction-aware signal that filters trajectories with excessive user dependence and favors effective tool use. Experiments with two user simulators show that frontier agents remain unreliable, with substantial gaps between average task success and consistent task completion. Tracer-generated supervision consistently improves Qwen3 models across scales, generalizes to unseen domains, and transfers to $τ$ -Bench and $τ$ -Trait. Behavioral analyses further show that higher task success is associated with a better balance between tool execution and necessary user follow-up. Overall, $τ$ -Emotion-Bench and Tracer provide a scalable framework for evaluating agent reliability under diverse user behaviors and improving tool-calling agents through interaction-aware supervision.

Figures & tables

Figure 1: Average accuracy of LLM agents on $\tau$ -Emotion-Bench over three runs.

Figure 2: Overview of $\tau$ -Emotion-Bench and the Tracer data-generation pipeline. Stage 1: Tracer builds policy-valid dependency graphs from tools, environments, and policies. Stage 2: Valid subgraphs are sampled to form executable multi-step traces. Stage 3: Traces are reverse-refined into natural user instructions while preserving task goals, context, preferences, and ambiguity. Stage 4: Task-specific feelings are mapped to structured emotion conditions and integrated with the task instruction. Stage 5: Tasks are verified and filtered for executability, policy consistency, and validity before inclusion in $\tau$ -Emotion-Bench.

Benchmark	Task Complexity		Tool Complexity		Interaction Complexity			Diversity	Scalability
Benchmark	Goal Clarity	Avg. Ground-Truth Tool Calls per Task	# Tools	Inter-tool Dependency	# Turns (approx.)	User Preferences	Emotion Behavioral	# Domains	Automated Generation	Scalable
ToolTalk ( Farn and Shin, 2023 )	✓	3.4	$28$	✓	$[2,10]$	✗	✗	$1$	✓ –	✗
IN3 ( Qian et al., 2024 )	✗	-	-	✗	$[2,10]$	✓	✗	$1$	✓ –	✓ –
MINT ( Wang et al., 2023 )	✓ –	-	$13$	✗	$[2,10]$	✗	✗	8	✗	✗
ToolSandbox ( Lu et al., 2025 )	✓ –	3.8	$34$	✓	$[10,30]$	✗	✗	$11$	✗	✗
DialogTool ( Wang et al., 2025 )	✓	$4.7$	$30$	✓	$[10,30]$	✗	✗	$15$	✓ –	✓ –

Table 1: Comparison of existing user interaction benchmarks across task, tool, interaction, diversity, and scalability dimensions. “ ✓ ” indicates fully addressed, “ ✓ – ” indicates partially addressed, and “ ✗ ” indicates not addressed. A “-” indicates that the corresponding information was not found.

Domain	Read Tools	Write Tools	Emotions (Family/Subfamily)	Test Tasks
Airline	8	6	5/16	70
Retail	9	7	5/17	70
Telecom	12	6	5/13	70
Telehealth	18	5	3/8	70
School	9	4	4/10	50
Basketball	17	3	1/2	50

Table 2: Benchmark statistics for

\tau

-Emotion-Bench domains.

Model	Airline	Retail	Telecom	Telehealth	School	Basketball	Restaurant	Travel	Movie	Bank	Avg.
Non-thinking Models
Qwen3-32B	10.0	21.4	12.9	10.0	20.0	4.0	6.0	0.0	0.0	10.0	9.4
Qwen3-235B-A22B	17.1	15.7	10.0	21.4	22.0	24.0	4.0	0.0	0.0	24.0	13.8
DeepSeek-V4.1-Flash	32.9	44.3	17.1	31.4	42.0	38.0	26.0	30.0	22.0	38.0	32.2
Thinking Models
Qwen3-32B	8.6	21.4	8.6	15.7	14.0	12.0	2.0	2.0	2.0	0.0	8.6

Table 3: Performance comparison across ten

\tau

-Emotion-Bench domains using Pass^3 (%).

Method	OOD Domains
	School		Basketball		Restaurant		Travel		Movie		Bank		Avg. Pass^1	Avg. Pass^3
	Pass^1	Pass^3	Pass^1	Pass^3	Pass^1	Pass^3	Pass^1	Pass^3	Pass^1	Pass^3	Pass^1	Pass^3	Avg. Pass^1	Avg. Pass^3
Qwen3-1.7B
Base	3.3	0.0	6.7	0.0	0.7	0.0	0.0	0.0	0.0	0.0	9.3	0.0	3.3	0.0
AReaL	4.0	2.0	4.0	0.0	0.7	0.0	0.0	0.0	0.0	0.0	2.7	0.0	1.9	0.3
EnvScaler	8.0	0.0	5.3	0.0	4.7	0.0	1.3	0.0	4.7	0.0	14.7	4.0	6.5	0.7

Table 4: Performance comparison of Tracer with existing agent-trained models across six OOD domains on

\tau

-Emotion-Bench using Pass^1 and Pass^3. Values are in %.

Figure 3: Performance comparison of Qwen3 base, Tracer-Low, and Tracer-High models across

\tau

-Bench and

\tau

-Trait using Pass^1, Pass^2, and Pass^3.

Figure 4: Comparison of model groups by policy violation rate across domains and emotions.

Figure 5: Avg@3 versus tool-call-to-follow-up ratio across emotion conditions and model groups.

Figure 8: Distribution of tool calls and follow-up actions across AReaL, Simia, Tracer-Low, and Tracer-High training trajectories.

Method	In-Domain
	Airline		Retail		Telecom		Avg. Pass^1	Avg. Pass^3
	Pass^1	Pass^3	Pass^1	Pass^3	Pass^1	Pass^3	Avg. Pass^1	Avg. Pass^3
Qwen3-1.7B
Base	1.0	0.0	0.5	0.0	23.8	8.6	8.4	2.9
AReaL	11.0	1.4	10.0	1.4	13.3	2.9	11.4	1.9
Tracer-Low (Ours)	29.5	10.0	40.9	18.6	32.4	10.0	34.3	12.9

Table 5: Performance comparison of Tracer with existing agent-trained models across three in-domain domains on

\tau

-Emotion-Bench using Pass^1 and Pass^3.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Domain	Tasks	Rollouts	$\bm{R{=}1}$	$\bm{R{=}0}$	$\bm{R{=}{-}1}$	Success (%)	High	Low
Airline	922	922	143	680	99	15.5	143	143
Retail	898	898	585	288	25	65.1	250	585
Telecom	1,519	1,519	186	1,118	215	12.2	186	186
Telehealth	1,314	1,314	584	718	12	44.4	250	584
School	333	325	133	97	95	40.9	133	–
Basketball	876	457	346	72	39	75.7	250	–

Appendix

Table 6: Domain-level statistics for generated training tasks, rollout outcomes, success rates, and trajectories selected for Tracer-High and Tracer-Low.

Figure 9: Distribution of unsuccessful training trajectories across emotion families.

Figure 10: Distribution of training trajectories across emotion families.

Domain	$\bm{R{=}1}$	Avg. User Turns	Avg. Assistant Turns	Avg. Tool Calls	Avg. Gold Actions	Avg. Reasoning Tokens
Airline	143	5.5	10.7	6.2	4.0	243
Retail	585	7.7	15.0	8.3	4.8	105
Telecom	186	7.0	12.5	6.5	4.4	166
Telehealth	584	6.7	15.0	9.3	5.5	121
School	133	6.7	14.5	8.8	4.5	180
Basketball	346	7.8	21.5	14.7	6.2	112

Appendix

Table 7: Statistics of successful (

R{=}1

) training trajectories.

Hyperparameter	Value
Learning Rate	$5\times 10^{-5}$
Scheduler	Cosine
Warmup Ratio	0.03
Epochs	3
Optimizer	Paged AdamW 8-bit
Weight Decay	0.0

Appendix

Table 8: Fine-tuning hyperparameters used for Qwen3-1.7B, Qwen3-4B and Qwen3-8B.

Model	Thinking	Serving	Hugging Face
Evaluated models
Qwen3-1.7B	On	Local	Qwen/Qwen3-1.7B
Qwen3-4B	On	Local	Qwen/Qwen3-4B
Qwen3-8B	On	Local	Qwen/Qwen3-8B
Qwen3-32B	On / Off	Local	Qwen/Qwen3-32B
Qwen3-235B-A22B-Instruct-2507	Off	ASU	Qwen/Qwen3-235B-A22B-Instruct-2507

Appendix

Table 9: Open-weight models we evaluate. Thinking : whether the model’s reasoning mode is on. Serving : ASU’s hosted inference cluster or our own vLLM server (Local).

Model	Thinking	Temp.	Top- $p$	Top- $k$	Min- $p$	Other
Evaluated models
Qwen3-1.7B	On	0.6	0.95	20	0	–
Qwen3-4B	On	0.0	–	–	–	–
Qwen3-8B	On	0.0	–	–	–	–
Qwen3-32B (thinking)	On	0.6	0.95	20	0	–
Qwen3-32B (non-thinking)	Off	0.7	0.8	20	0	–

Appendix

Table 10: Decoding configuration used in the largest number of domains for each model. A dash means the parameter is not set (the server default).

Category	Fault type	Description
Missing or Extra Action	Missed Action	A required action is not taken.
	Wrong Order	Actions are taken out of the required order.
	Extra Action	An action not needed for the task is taken.
	Repeated Action	The same action is taken again without need.
Incomplete	Misread Intent	The user’s request or a tool result is misunderstood.
Incomplete	Early Stop	The conversation ends before the task is complete.

Appendix

Table 11: Fault taxonomy used in the error analysis.

Figure 11: Failure analysis across domains on

\tau

-Emotion-Bench.

Figure 12: Share of failed trajectories with a critical agent error in each fault category for Qwen3 base and Tracer models, with Qwen3-235B-A22B-Thinking and DeepSeek-V4.1-Flash as references.

Model	Airline			Retail			Telecom			Telehealth			School			Basketball			Restaurant			Travel			Movie			Bank
Model	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3
Non-thinking Models
Qwen3-32B	20.0	13.3	10.0	46.2	29.5	21.4	30.9	18.1	12.9	33.3	18.1	10.0	40.7	28.0	20.0	16.0	7.3	4.0	16.7	8.0	6.0	4.7	1.3	0.0	3.3	0.7	0.0	27.3	15.3	10.0
Qwen3-235B-A22B	35.2	22.4	17.1	40.0	23.8	15.7	28.6	16.7	10.0	42.9	28.1	21.4	47.3	30.7	22.0	46.7	30.7	24.0	22.0	10.7	4.0	10.7	2.7	0.0	7.3	0.7	0.0	45.3	31.3	24.0
DeepSeek-V4.1-Flash	57.6	42.4	32.9	71.9	56.2	44.3	38.1	24.3	17.1	52.4	39.0	31.4	61.3	48.0	42.0	60.0	45.3	38.0	55.3	35.3	26.0	49.3	36.0	30.0	51.3	31.3	22.0	61.3	44.7	38.0
Thinking Models

Appendix

Table 12: Performance comparison of frontier open-source models and Tracer models across ten domains on

\tau

-Emotion-Bench, evaluated using Pass^1 (p^1), Pass^2 (p^2), and Pass^3 (p^3). Values are reported as percentages (%). Qwen3-235B-A22B-Instruct-2507 is used as the user simulator. The highest values are bolded, and the second highest values are underlined.

Method	Airline			Retail			Telecom			Telehealth			School			Basketball			Restaurant			Travel			Movie			Bank
Method	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3
Qwen3-1.7B
Base	1.0	0.0	0.0	0.5	0.0	0.0	23.8	12.9	8.6	15.2	5.2	2.9	3.3	1.3	0.0	6.7	2.0	0.0	0.7	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.3	2.7	0.0
AReaL	11.0	3.3	1.4	10.0	2.9	1.4	13.3	5.7	2.9	2.4	0.0	0.0	4.0	2.7	2.0	4.0	0.0	0.0	0.7	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.7	7.0	0.0
EnvScaler	10.0	4.8	1.4	7.1	1.4	0.0	37.6	22.4	14.3	35.7	20.9	11.4	8.0	1.3	0.0	5.3	0.7	0.0	4.7	0.7	0.0	1.3	0.0	0.0	4.7	0.7	0.0	14.7	7.3	4.0
Qwen3-4B

Appendix

Table 13: Performance comparison of existing agent-trained models across ten domains on

\tau

-Emotion-Bench using Pass^1 (p^1), Pass^2 (p^2), and Pass^3 (p^3). Values are in %. The highest values are bolded, and the second highest values are underlined.

Model	Airline			Retail			Telecom			Telehealth			School			Basketball			Restaurant			Travel			Movie			Bank
Model	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3	p^1	p^2	p^3
Non-thinking Models
Qwen3-32B	28.1	21.9	20.0	63.3	54.3	50.0	66.7	58.6	54.3	63.3	54.3	48.6	51.3	38.7	34.0	30.7	16.7	10.0	24.0	13.3	8.0	8.0	1.3	0.0	6.0	1.3	0.0	37.3	22.7	14.0
Qwen3-235B-A22B	48.6	40.9	37.1	63.3	50.5	42.9	63.3	53.8	48.6	68.6	56.7	50.0	70.7	60.0	54.0	53.3	42.0	36.0	40.7	34.7	32.0	32.7	16.7	10.0	17.3	9.3	6.0	58.7	44.7	38.0
DeepSeek-V4.1-Flash	86.2	81.4	78.6	96.2	93.8	92.9	65.7	55.7	50.0	75.2	64.3	60.0	68.0	59.3	54.0	71.3	60.7	54.0	80.0	71.3	66.0	94.7	90.0	86.0	60.7	46.0	36.0	76.7	71.3	68.0
Thinking Models

Appendix

Table 14: Performance comparison of frontier open-source models and Tracer models across ten domains on

\tau

-Emotion-Bench, evaluated using Pass^1 (p^1), Pass^2 (p^2), and Pass^3 (p^3). Values are reported as percentages (%). GLM-5.2 is used as the user simulator. The highest values are bolded, and the second highest values are underlined.

Panel of Figure 14 — Figure 14: Tool dependency graphs of all ten domains. Each panel puts read tools in the left column and write tools in the right. Gold nodes are root-eligible, i.e. valid starting points for a trace. A blue arrow marks a transition valid in both directions, drawn once per direction; an orange arrow marks one valid in a single direction only. Grey arcs are transitions within a column, read to read or write to write.

Domain	Core Entities
Airline	500 users, 300 flights, 2,000 reservations
Retail	527 users, 50 products, 1,075 orders
Telecom	500 customers, 15 services, 240 tickets
Telehealth	239 patients, 114 providers, 214 appointments
School	1,000 students, 120 courses, 1,345 registrations
Basketball	1,000 players, 240 clubs, 1,468 games

Appendix

Table 15: Core entities per domain database for evaluation set

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