PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas

Haoming Li; Defu Cao; Xu Hu; Yingtao Luo; Yiyang Wang; Xianghe Xu; Jiashu He; Wanting Jiang

cs.HCOct 16, 2026

PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas

Authors: Haoming Li, Defu Cao, Xu Hu, Yingtao Luo, Yiyang Wang, Xianghe Xu, Jiashu He, Wanting Jiang,

Organizations: The University of Texas at Dallas · University of Southern California · Carnegie Mellon University · Georgia Institute of Technology ·

Abstract

Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas in four groups, rewrites each instruction in that user's own words and answers the CUA's questions, while the CUA sees only the dialogue, never the profile or the original instruction. Across nine backbones (3,330 runs), mean task completion falls by 36% relative to the original requests. The losses have two sources: in 59.9% of cells that lose points, the agent engages with what the user asked for and still gets it wrong, and 11.2% are stops the user requested that the checker scores as failures. Even when a persona request is fully completed, the user gives the top satisfaction rating in only 62.7% of cells, against 84.1% for the original requests. CUAs should therefore be evaluated not only on task completion but also on whether they meet the requirements and constraints users express. Code: https://github.com/MatrAIx-ai/PersonaCUABench. Project page: https://matraix.ai/research/persona-cua.html

Figures & tables

Figure 1: Overview of PersonaCUABench. Top: 74 tasks in 11 domains, 44 personas, four groups in each of 11 domains, plus the original request, task completion of nine backbones on original and persona requests, and the distribution of user satisfaction. Stage 1: a MatrAIx persona agent with a 37-attribute profile rephrases the original task in the user’s own words. The CUA sees neither the profile nor the task. Stage 2: the CUA sees only the dialogue, acts in Chrome, and may ask the persona agent questions. Stage 3: each rollout gets two scores: task completion against the original task, and satisfaction rated by the same persona agent. Here MiniMax M3 stops before sending the offer, so the task fails while the user rates it 5/5. Each requirement is then checked as raised, engaged, and met to locate the loss.

	Odysseys	REAL	Total
Tasks	38	36	74
Task type	Information	Action	—
Environment	Live web	Replicas	—
Requirements	206	107	313
Completion check	Rubric judge	State checker	—
Life / work tasks	28 / 10	12 / 24	40 / 34

Table 1: Task composition. Three domains (travel, food, lifestyle) contain tasks from both sources.

Panel of Table 2 — Table 2: The four persona groups $\mathcal{K}$ , each instantiated in all 11 domains, with their values on the five grouping attributes. All 11 personas of a group share these values, with five exceptions listed with the attributes.

Model	Task Completion					Satisfaction (1–5)
Model	Original	expert	senior	young	practical	Original	expert	senior	young	practical
MiniMax M3	0.686	0.447	0.334	0.355	0.386	4.01	3.08	3.05	3.09	3.09
Kimi K3	0.768	0.555	0.432	0.452	0.496	4.24	3.88	3.22	3.57	3.47
Muse Glimmer 30B †	0.548	0.413	0.312	0.374	0.316	3.59	3.31	2.88	3.16	2.85
Grok 4.6	0.684	0.543	0.476	0.443	0.515	4.16	3.88	3.14	3.62	3.53
Qwen 3.8 27B †	0.729	0.554	0.431	0.497	0.501	4.09	3.77	3.38	3.66	3.26

Table 3: Task completion and user satisfaction at delivery over all 74 tasks. Models are listed in order of release date, and bold marks the best model in each column. † denotes open-source models.

Panel of Table 4 — Table 4: Task completion by scenario and persona group, mean over nine models. Table 14 gives the intervals.

Figure 2: Change in task completion by domain, mean over nine models with 95% intervals.

Figure 3: Task completion and satisfaction at delivery by domain, for the original request and each persona group, mean over nine models. Mixed domains list their information and action tasks.

Figure 4: A practical user’s case, run by DeepSeek V4.1 Flash. Appendix D gives one case for each of the other three groups.

Figure 5: Primary cause of each loss cell, nine models pooled. Each cell has one cause. (a) All 1,230 loss cells. (b) Each persona group, with its number of loss cells.

Benchmark	Computer use	Same task, many users	Can ask the user	Judged by user
WebArena	✓	✗	✗	✗
OSWorld 2.0	✓	✗	✓	✗
MyPCBench	✓	✗	✗	✗
PersonalWAB	✗	✗	✓	✗
Persona2Web	✓	✗	✗	✗
PrefDisco	✗	✓	✓	✗

Table 5: Comparison with related benchmarks.

Appendix figures & tables28 assets

Supplementary material from the paper’s appendix.

Appendix

Who they are	Can they use the result	What they ask for and expect	Which trade-offs they accept
age bracket highest education household income time pressure seniority technology savviness region urbanicity socioeconomic band geographic mobility	interest in technology attitude to new technology device context mobility vision assistive technology accessibility needs	detail orientation patience attention span big picture vs. detail brief vs. full detail planning horizon planned vs. spontaneous	budgeting skill frugality economic motivation budget tracking save vs. spend preference risk tolerance financial risk tolerance (DOSPERT) risk framing

Appendix

Table 6: The 32 general attributes shared by all personas, grouped by the question they answer (10, 7, 7, and 8 attributes).

Domain	Industry	Skills	Knowledge	Interests	Other
Art	fine art	drawing design thinking	art history	–	appreciation of beauty
Community	nonprofit government	facilitation	–	volunteering	value: community
E-commerce	e-commerce retail	negotiation	–	–	shopping style brand loyalty
Food	food and beverage hospitality	cooking	–	cooking	diet type
Health	healthcare fitness and wellness	–	nutrition	fitness	exercise frequency
Lifestyle	apparel real estate	–	–	interior design fashion	fashion sense

Appendix

Table 7: The five domain-specific attributes of each domain, grouped by type. Knowledge covers familiarity with a field and subject knowledge; Other covers values, styles, and habits. A dash marks a type that a domain does not use.

Figure 6: How often each profile dimension appears in the persona’s utterances (2,664 persona cells over nine models). The right column gives the share of cells in which the dimension is clearly expressed.

Model	Clear (%)	Contradiction (%)	$\Delta$ : all $\to$ excluded	Accessibility (%)	Device (%)
MiniMax M3	16.1	11.1	$-0.305\to-0.317$	1.7	2.0
Kimi K3	14.5	9.1	$-0.284\to-0.285$	2.4	1.4
Muse Glimmer 30B	15.0	11.8	$-0.194\to-0.191$	3.4	4.7
Grok 4.6	13.8	8.8	$-0.190\to-0.194$	3.7	2.7
Qwen 3.8 27B	15.0	12.5	$-0.233\to-0.225$	3.4	4.4
Gemini 3.8 Flash	13.5	7.8	$-0.311\to-0.313$	2.0	1.7

Appendix

Table 8: Persona fidelity per model. Clear is the share of the 11 profile dimensions that the persona’s utterances state clearly. Contradiction is the share of cells with at least one utterance that contradicts the profile. The

\Delta

column gives the overall

\Delta

on all cells and with those cells excluded. Accessibility and Device give the share of cells in which the accessibility or device dimension is clearly expressed.

	$\varepsilon_{i}=1$	$\varepsilon_{i}=0$
$\rho_{i}=1$	engaged	dropped
$\rho_{i}=0$	engaged unprompted	unsaid

Appendix

Table 9: Intent outcomes for a requirement

r_{i}

, by whether the user raised it (

\rho_{i}

) and whether the agent engaged with it (

\varepsilon_{i}

Model	List price, input / output (USD per million)	Input tokens (millions)	Output tokens (millions)	Mean steps	Agent cost (USD)	Agent cost per cell (USD)	Persona agent and judges, estimated (USD)
MiniMax M3	0.30 / 1.20	332.1	3.39	43.1	104	0.28	18
Kimi K3	3.00 / 15.00	372.9	5.75	35.8	1,205	3.26	17
Muse Glimmer 30B	–	293.6	2.49	41.6	–	–	25
Grok 4.6	2.00 / 6.00	397.4	2.55	42.9	810	2.19	26
Qwen 3.8 27B	0.42 / 3.00	404.0	5.69	35.1	187	0.50	25
Gemini 3.8 Flash	0.75 / 3.75	384.6	3.02	36.8	300	0.82	21

Appendix

Table 10: API cost per backbone at list prices without cache discounts. The persona agents and judges run on GPT-5.6-terra and are estimated at 2.00 / 12.00 USD per million input / output tokens. † Eight backbones with a list price.

Judgment	Items	P1	P2
Requirement
Met	3,957	86.2 (0.72)	88.2 (0.75)
Raised	3,645	88.1 (0.47)	84.7 (0.20)
Engaged	3,645	97.2 (0.55)	95.9 (0.30)
Profile dimension
Clear	8,712	90.7 (0.59)	88.1 (0.54)

Appendix

Table 11: Agreement of human annotators P1 and P2 with the GPT-5.6-terra judge on 22 representative tasks (two per domain), as percent agreement with Cohen’s

\kappa

in parentheses. Items count the labels that GPT-5.6-terra gives. For each profile dimension, Clear asks whether it is clearly expressed, and Level asks whether it is clearly, weakly, or not expressed.

Persona agent’s own rating
Compared with	Pairs	P1	P2
At delivery	987	37.5 ( $0.37$ )	37.3 ( $0.44$ )
After verification	305	32.1 ( $0.33$ )	36.4 ( $0.30$ )
External judge (P5)
At delivery	989	48.6 ( $0.52$ )	58.2 ( $0.60$ )

Appendix

Table 12: Agreement of human annotators P1 and P2 with the two automated satisfaction ratings on the 22 selected tasks, as exact percent agreement with quadratic-weighted Cohen’s

\kappa

in parentheses. Pairs are matched on backbone, task, persona, and rating stage.

Model	$\Delta$	95% CI	Relative (%)	$\Delta$ excluded
MiniMax M3	$-0.305$	[ $-0.379$ , $-0.229$ ]	$-45$	$-0.317$
Kimi K3	$-0.284$	[ $-0.358$ , $-0.213$ ]	$-37$	$-0.285$
Muse Glimmer 30B	$-0.194$	[ $-0.276$ , $-0.119$ ]	$-35$	$-0.191$
Grok 4.6	$-0.190$	[ $-0.256$ , $-0.124$ ]	$-28$	$-0.194$
Qwen 3.8 27B	$-0.233$	[ $-0.303$ , $-0.169$ ]	$-32$	$-0.225$
Gemini 3.8 Flash	$-0.311$	[ $-0.381$ , $-0.240$ ]	$-42$	$-0.313$

Appendix

Table 13:

\Delta

, 95% interval, relative change, and

\Delta

after exclusion, for Table 3 .

\Delta

is task completion on the persona request minus that on the original request on the same task, averaged over the four groups. Relative is

\Delta

divided by the original-request score, in percent.

\Delta

excluded drops the 11.1% of cells in which the persona agent contradicts its profile. Every interval lies below zero, and excluding those cells changes the mean

\Delta

from

-0.248

-0.249

\Delta

pairs each persona cell with the original-request cell of the same task.

Figure 7: Task completion and satisfaction at delivery per model, for the original request and each persona group, over the 74 tasks. The gray bar spans the four persona groups, and the black marker is the original request. Models are sorted by mean persona task completion.

Group	Tasks	Original $\to$ persona	Change [95% CI]	Relative change (%)
All	74	0.689 $\to$ 0.441	$-0.248$ [ $-0.295$ , $-0.202$ ]	$-36$
Work	34	0.705 $\to$ 0.431	$-0.274$ [ $-0.352$ , $-0.197$ ]	$-39$
Life	40	0.675 $\to$ 0.449	$-0.226$ [ $-0.280$ , $-0.174$ ]	$-33$
expert	74	0.689 $\to$ 0.504	$-0.185$ [ $-0.240$ , $-0.135$ ]	$-27$
senior	74	0.689 $\to$ 0.402	$-0.287$ [ $-0.343$ , $-0.232$ ]	$-42$
young	74	0.689 $\to$ 0.414	$-0.275$ [ $-0.326$ , $-0.225$ ]	$-40$

Appendix

Table 14: Task completion by scenario and persona group, all tasks pooled, mean over nine models. Change: persona minus original on the same task, with a task-level bootstrap interval. In each of the seven groups, all nine models lose.

Domain	User										Mean
Professional (14)	Original	0.857	0.929	0.714	0.786	0.857	0.786	0.786	0.643	0.714	0.786
	expert	0.357	0.429	0.214	0.571	0.357	0.286	0.357	0.214	0.429	0.357
	senior	0.357	0.571	0.357	0.714	0.500	0.429	0.286	0.429	0.429	0.452
	young	0.500	0.500	0.429	0.500	0.571	0.429	0.286	0.214	0.500	0.437
	practical	0.357	0.500	0.214	0.643	0.571	0.429	0.286	0.286	0.286	0.397
Productivity (9)	Original	0.444	0.667	0.333	0.444	0.444	0.556	0.333	0.556	0.556	0.481

Appendix

Table 15: Task completion by domain, user group, and model. Mixed domains list their information and action tasks. Model columns follow the logos in order of release, as in Table 3 : MiniMax M3, Kimi K3, Muse Glimmer 30B, Grok 4.6, Qwen 3.8 27B, Gemini 3.8 Flash, GPT-6 Astra, DeepSeek V4.1 Flash, and Claude Opus 5.5. Bold: best model in each row.

Domain	User										Mean
Professional (14)	Original	4.79	5.00	4.50	5.00	5.00	5.00	4.79	4.71	4.86	4.85
	expert	3.57	3.93	3.86	4.50	4.07	3.57	4.21	4.14	4.07	3.99
	senior	3.71	4.07	3.21	3.93	4.21	3.43	3.29	4.00	4.43	3.81
	young	4.21	4.93	4.14	4.71	4.57	4.21	4.36	4.57	4.21	4.44
	practical	4.14	4.14	3.00	3.93	3.43	3.64	4.00	4.29	4.14	3.86
Productivity (9)	Original	4.67	4.67	4.78	4.56	4.11	5.00	4.33	4.00	4.33	4.49

Appendix

Table 16: Satisfaction at delivery by domain, user group, and model. The original request is rated by the same model without a profile. Model columns match Table 15 . Bold: best model in each row.

Figure 8: Task completion by domain for the original request and each persona group, mean over nine models. The original request is highest in every domain. Table 15 gives the values for each model.

Figure 9: Satisfaction at delivery by domain for the original request and each persona group, mean over nine models. The original request is rated by the same model without a profile. Table 16 gives the values for each model.

Model	Action AUC			Failed, judged done (%)		Information correlation		Misreport (%)
Model	External judge	Delivery	Verified	Delivery	Verified	External judge	Delivery	Information	Action
MiniMax M3	0.56	0.66	0.76	54	33	0.28	0.51	58	38
Kimi K3	0.58	0.62	0.78	62	28	0.29	0.54	49	22
Muse Glimmer 30B	0.66	0.73	0.81	42	25	0.46	0.63	60	33
Grok 4.6	0.49	0.55	0.69	64	38	0.34	0.62	36	17
Qwen 3.8 27B	0.56	0.63	0.75	58	30	0.27	0.57	43	32

Appendix

Table 17: How well each rating tracks task completion, on persona cells. AUC measures separation of passed from failed action cells (0.5 is chance). Information correlation is the correlation between the rating and the information-task score. At delivery the user sees the final reply and the open tabs, and when verified the user also sees the resulting site state. Verified ratings cover only a subset of the tasks, namely those that change the site. Misreport is the share of loss cells whose final reply is inconsistent with the outcome.

Group	Cells	At delivery		Verified
Group	Cells	Rating	Done (%)	Rating	Done (%)
Stopped correctly	138	3.69	62	1.86	7
Other failures	205	3.25	46	2.42	27
reply misreports	75	3.47	55	2.63	33
reply is accurate	130	3.13	41	2.31	24

Appendix

Table 18: Satisfaction rating from 1 to 5 and share of cells the user judges done, for the 343 action loss cells. Verified ratings are given after the user checks the site and cover only the tasks that change a site.

Panel of Table 19 — Table 19: Satisfaction from 1 to 5 on each group’s own successful and failed cells, pooled over nine models and all tasks. Yes and No mark whether the cell is fully successful. After-verification ratings cover only the tasks that change a site.

Label (% of loss cells)	Information	Action
Missed a raised requirement a	76.7	24.2
Missed a requirement not raised a	45.7	1.7
only requirements not raised missed a	23.3	0.9
Final step not executed b	–	74.9
of which a correct stop	–	40.2
User asked for less or for staged execution	14.7	59.2

Appendix

Table 20: Labels of the 887 information and 343 action loss cells. A cell can carry several labels. The final step is to submit, book, or send. Labels marked a come from the per-requirement scores, those marked b from the final site state, and the rest from GPT-5.6-terra.

Figure 10: Share of fully successful cells and mean steps of successful and failed cells, by user group, pooled over nine models. Only 21 information cells are fully successful for senior and 22 for young .

Model	Asked a question (%)		Misreport (%)		Judged done (%)
Model	Information	Action	Information	Action	Judged done (%)
MiniMax M3	4.6	40.3	58	38	54
Kimi K3	4.6	22.9	49	22	62
Muse Glimmer 30B	3.3	16.7	60	33	42
Grok 4.6	1.3	2.8	36	17	64
Qwen 3.8 27B	3.3	27.1	43	32	58

Appendix

Table 21: Clarification and outcome reporting by model, in the order of Table 3 . Asked a question counts persona cells, misreport counts loss cells, and judged done counts failed action cells. † Pooled over cells.

Model	Ask, information (%)		Ask, action (%)		Questions	On target (%)	Answers (%)		Asked $-$ not asked Difference (tasks)
Model	Persona	Original	Persona	Original	Questions	On target (%)	Invented	Conflict	Asked $-$ not asked Difference (tasks)
MiniMax M3	4.6	5.3	40.3	25.0	96	21	73	3	$+0.103$ (26)
Kimi K3	4.6	2.6	22.9	8.3	55	36	73	11	$+0.164$ (16)
Muse Glimmer 30B	3.3	5.3	16.7	8.3	31	10	97	0	$-0.185$ (9)
Grok 4.6	1.3	0.0	2.8	0.0	9	33	89	22	$-0.500$ (5)
Qwen 3.8 27B	3.3	0.0	27.1	13.9	64	23	72	9	$+0.074$ (16)

Appendix

Table 22: Clarification behavior. Ask is the share of cells with at least one question. On target counts questions that target a requirement the user omitted. Invented counts persona-agent answers that add details absent from the task, and Conflict counts answers whose details conflict with the task. Difference is, within a task, mean task completion of persona cells in which the agent asked minus that of cells in which it did not, with the number of tasks containing both kinds of cell.

Group	Information tasks			Action tasks
Group	$\Delta$ completion [95% CI]	$\Delta$ satisfaction [95% CI]	Median length (chars)	$\Delta$ completion [95% CI]	Better / worse
expert	$-0.028$ [ $-0.065$ , $+0.009$ ]	$\mathbf{+0.34}$ [ $+0.16$ , $+0.53$ ]	4148 $\to$ 4479	$+0.083$ [ $-0.028$ , $+0.194$ ]	4 / 1
senior	$\mathbf{-0.070}$ [ $-0.115$ , $-0.025$ ]	$-0.18$ [ $-0.42$ , $+0.05$ ]	2688 $\to$ 2378	$0.000$	0 / 0
young	$-0.015$ [ $-0.068$ , $+0.038$ ]	$-0.05$ [ $-0.24$ , $+0.13$ ]	2750 $\to$ 2506	$+0.083$ [ $0.000$ , $+0.194$ ]	3 / 0
practical	$+0.004$ [ $-0.052$ , $+0.065$ ]	$-0.13$ [ $-0.32$ , $+0.05$ ]	3952 $\to$ 3520	$-0.056$ [ $-0.139$ , $0.000$ ]	0 / 2
All	$\mathbf{-0.027}$ [ $-0.051$ , $-0.001$ ]	$-0.01$ [ $-0.09$ , $+0.09$ ]	3469 $\to$ 3185	$+0.028$ [ $-0.007$ , $+0.069$ ]	7 / 3

Appendix

Table 23: Effect of showing the agent the user’s profile (GPT-6 Astra, 296 cells with identical opening requests). Each change is the profile-visible value minus the main-experiment value on the same cell. Satisfaction is the user’s rating at delivery. Better / worse counts the action cells whose outcome changes. Bold: the interval excludes zero.

Slice	Cells	Task completion: main $\to$ forced	$\Delta$ [95% CI]	Better / worse	Asked (%)	Steps
Information, original	38	0.762 $\to$ 0.776	$+0.013$ [ $-0.030$ , $+0.054$ ]	8 / 5	21 $\to$ 100	41.1 $\to$ 49.1
Information, persona	152	0.582 $\to$ 0.549	$-0.032$ [ $-0.065$ , $+0.001$ ]	28 / 42	23 $\to$ 100	37.0 $\to$ 50.0
Action, original	36	0.583 $\to$ 0.472	$-0.111$ [ $-0.250$ , $0.000$ ]	1 / 5	25 $\to$ 100	17.0 $\to$ 25.7
Action, persona	144	0.278 $\to$ 0.285	$+0.007$ [ $-0.042$ , $+0.056$ ]	8 / 7	53 $\to$ 100	18.8 $\to$ 24.5
All persona	296	0.434 $\to$ 0.421	$-0.013$ [ $-0.042$ , $+0.018$ ]	36 / 49	38 $\to$ 100	28.1 $\to$ 37.6

Appendix

Table 24: Effect of forcing the agent to ask at least one clarification question (GPT-6 Astra, 370 cells with identical opening requests).

\Delta

is the forced value minus the main-experiment value on the same cell. Better / worse counts the cells whose score rises or falls, Asked is the share of cells with at least one question, and Steps is the mean number of agent steps.

Figure 11: Senior persona case in PersonaCUABench, run by DeepSeek V4.1 Flash. A cost-cautious user requests a fixed price before the agent sends an offer. The sampled views show job review, candidate review and an unsubmitted offer form. The agent returns a $1,800 draft and states that nothing was sent. The original checker awards 0/1 for the unfinished offer, although the pause follows the boundary the user added. The user, who still wanted the hire, rates it 2/5. Note that, Figure 1 is run by MiniMax, with the same senior user which also stops before sending the offer and is rated 5/5.

Figure 12: Expert persona case in PersonaCUABench run by DeepSeek V4.1 Flash. The user requests reusable sources and a cited workshop brief. The agent opens the three evidence pages and the album page, then supplies the sources and track titles. The saved rubric judge awards 5/7. R4 and R6 fail because the final synthesis and song rationale do not meaningfully integrate the evolutionary-bias evidence, despite opening its source.

Figure 13: Young persona case in PersonaCUABench run by DeepSeek V4.1 Flash. The agent searches for a low-cost group stay, opens the cheaper listing and explores messaging. The user agrees to forward the link, and the final answer states that the agent did not send it. The saved checker awards 1/1, but its implementation checks search criteria only. This case exposes a gap between the checker’s pass and the original task’s group-chat delivery requirement.

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Open paper AskPDF arXiv

Current benchmarks for computer-use agents evaluate models in impersonal environments. This leaves a gap between evaluation and deployment where personal assistants are expected to work across a user's whole digital life, including their context, historical data, and logged-in accounts. This gap is widest on web tasks, where live web evaluations cannot exercise sites that require logging in or personal information, the kind of site a real personal assistant has to drive. We introduce MyPCBench, which tests computer-use agents as personal assistants on a Linux desktop populated with 17 simulated real-world web applications and a full desktop stack, all seeded for one canonical persona, Michael Scott from The Office. We define 184 tasks in this environment, each inspired by a real request drawn from the OpenClaw community, and benchmark six closed and open-weight models with a uniform computer+bash tool surface. We find that the best model, Claude Opus 4.6, fully solves 55.4% of the tasks, the only model above 50%. Model failures cluster on tasks that span many applications and on long trajectories, where personalization stresses an assistant the most. We release the environment, task set, and agent harness at https://mypcbench.com.

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