Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification

Chenrui Shi; Che Sun; Lifeng Fan; Ruining Feng; Yang Liu; Yuwei Wu; Zhi Gao; Zirui Shang

REVIEW 2 major objections 5 minor 43 references

Interactive verification of files, settings, and artifacts judges GUI tasks at 86.9% accuracy and can replace hand-written reward scripts.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-01 01:08 UTC pith:NK5XGTAL

load-bearing objection Active state verification is a genuine step forward in GUI evaluator design; the headline accuracy is not yet trustworthy because the ground-truth script runs after the evaluator may have changed the environment. the 2 major comments →

arxiv 2607.25904 v2 pith:NK5XGTAL submitted 2026-07-28 cs.AI

Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification

Chenrui Shi , Yuwei Wu , Yang Liu , Ruining Feng , Zirui Shang , Zhi Gao , Lifeng Fan , Che Sun This is my paper

classification cs.AI

keywords GUI task evaluationinteractive reward agentpropose-then-verifyenvironment-state verificationcompletion conditionsscreenshot-only evaluatorsreward modelingreinforcement learning for GUI agents

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Most automated judges of GUI-agent performance infer success from screenshots of the task, but the decisive evidence often lives in files, configuration values, and application settings. This paper tries to establish that a better judge is an interactive reward agent that first decomposes the instruction into explicit completion conditions and then actively verifies each condition with system, application, and GUI tools. The authors report 86.9% accuracy on their own GUI-RewardBench of 321 replayed desktop trajectories, beating all compared screenshot-based evaluators, and show that using such an agent as the reward signal for reinforcement learning yields a success rate close to using ground-truth scripts. If right, it reframes GUI evaluation as an evidence-verification problem rather than a visual-classification problem, and it opens a path to training on tasks that have no hand-written evaluation scripts.

Core claim

The central claim is that GUI task evaluation should be modeled as an interactive evidence-acquisition process. Given the instruction, initial and final screenshots, and a live post-execution environment, IRA first proposes a set of task-completion conditions, then verifies each condition through iterative reasoning-action-observation tool calls, treating a condition as satisfied only when explicit environment-state evidence is collected. It combines three tool groups: system tools for files, configurations, and command outputs; application tools for generated artifacts; and GUI tools for otherwise inaccessible interface state. On GUI-RewardBench the full framework reaches 86.9% accuracy, im

What carries the argument

The load-bearing mechanism is the propose-then-verify framework: a condition proposer (a vision-language model) converts the instruction into discrete, checkable completion conditions, and a ReAct-style verifier (a reasoning-action-observation loop) resolves each condition by selecting tools and accumulating observations until it can give a binary verdict. The scalar reward is the fraction of conditions satisfied, and binary success is thresholded at rewards greater than 0.8. The framework's power comes from separating task interpretation from evidence acquisition, so verification can route to files, configuration values, or GUI navigation depending on what the condition requires.

Load-bearing premise

The measured accuracy rests on assuming that executing the ground-truth verification script after IRA has interacted with the environment measures the state IRA was asked to judge; since IRA's own GUI and command tools can change system state, this assumption may not always hold.

What would settle it

Replay each benchmark trajectory twice — once allowing IRA's full interactive toolset and once restricting it to strictly read-only inspection — then compare accuracies and check whether the unrestricted version systematically changes the environment before the ground-truth script runs.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

Screenshot-only evaluation systematically misses hidden evidence; the reported gains over passive evaluators are largest on tasks whose completion lives in files, configurations, or cross-application state.
The same evaluator can serve as an RL reward: replacing task-specific scripts with IRA on identical training tasks yields a success rate within about one point of script rewards (34.0% versus 34.9%), and training on automatically generated tasks, where no script exists, stays within 1.4 points.
Interactive evaluation can be made backbone-independent: open and proprietary models perform similarly under the propose-then-verify procedure, suggesting the method lowers reliance on the judge model's prior visual judgment.
Condition-level aggregation provides a graded reward, not just a pass/fail label, which can be used to weight partial completion during training.
Benchmark stability matters for evaluator comparison: the benchmark only includes trajectories whose replayed final states are stable, making accuracy numbers interpretable in live desktop environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The recorded verification history could be reused as training data for cheaper, non-interactive reward models that learn which evidence sources are decisive.
Misaligned completion conditions are inspectable, so disagreements between IRA and a script oracle could be mined to detect ambiguous or under-specified task instructions in future benchmarks.
The wide gap in tool-use efficiency between backbones (around 2.5 versus 7.3 tool calls per task) suggests verification cost can be cut by learning to select a minimal decisive evidence source rather than exploring interactively.
A read-only variant of the evaluator would quantify how much of the reported accuracy depends on the evaluator altering the environment it later gets credit for verifying.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

Active state verification is a genuine step forward in GUI evaluator design; the headline accuracy is not yet trustworthy because the ground-truth script runs after the evaluator may have changed the environment.

read the letter

Read this for the core idea. Instead of judging task completion from screenshots, IRA proposes explicit completion conditions and then verifies them by actively checking files, configs, and GUI state. That is a simple, useful move, and it genuinely extends agent-as-judge work, which usually assumes the evidence is already provided. The benchmark (321 trajectories, 10 Ubuntu app categories) is small but reasonable, with visible/hidden/artifact verification types, and the ablations show that adding interaction alone isn't enough—the propose-then-verify structure is what fixes Qwen3.6's recall. The RL transfer result (34.0% with IRA vs 34.9% with scripts) is a useful sanity check. I also want to credit the appendix: the detailed success and failure trajectories are the most honest part of the paper, and the error analysis (over-literal criteria, missed persistence) is informative.

The main soft spot is the one the stress test flags. The protocol runs the task-specific script after the evaluator has finished inspecting, and IRA is allowed to change the environment (Eq. 5 says so). So the ground-truth label IRA is scored against can reflect IRA's own actions, not the original post-execution state. Passive baselines don't have this property. The paper acknowledges that positive/negative totals drift across methods—in Table 1 the total labeled positives range from 152 to 171—so the effect is visible in the numbers, even if its direction is not uniform. That doesn't make it a fatal flaw, but it does mean the 86.9% vs 78.8% gap has an unmeasured confound. A simple control, e.g., snapshot before evaluation and score against the pre-interaction state, or at least measure how often verification actions flip a script label, would close it.

Minor issues: no code, data, or project page are released, so the numbers aren't independently checkable; no error bars or significance tests on the main table; and the closest tool-augmented baselines the paper itself cites aren't compared. All addressable.

This is for people building reward models for GUI agents; they'll get the most from the propose-then-verify structure. I'd send this to peer review. The method is likely to influence GUI evaluator design, and the empirical evidence, though imperfect, is enough to warrant a serious referee. The protocol issue is the main thing to fix.

Referee Report

2 major / 5 minor

Summary. The paper proposes an Interactive Reward Agent (IRA) for GUI task evaluation. IRA first proposes explicit completion conditions from the task instruction and initial/final screenshots, then verifies each condition by interacting with the post-execution environment through system, application, and GUI tools. The authors introduce GUI-RewardBench, a 321-trajectory benchmark across 10 Ubuntu application categories with visible, hidden-state, and artifact-verification tasks. On this benchmark, IRA with GPT-5.5 achieves 86.9% accuracy, outperforming four passive VLM-based evaluators (best passive: DistRL at 78.8%). A matched-backbone ablation shows that the propose-then-verify framework improves precision–recall over both VLM-only and GUI-only variants. The paper also reports 94.0% human–IRA agreement (Cohen's κ=0.84) on 100 automatically generated tasks, and RL training with IRA rewards yields a 34.0% OSWorld success rate versus 34.9% with ground-truth scripts. The central claims are that interactive environment-state verification yields more accurate task-completion judgments than passive evaluators, and that IRA rewards can substitute for hand-written scripts in RL.

Significance. If the claims hold, this is a valuable contribution: it directly targets the evidence gap in GUI evaluation, proposes a scalable propose-then-verify mechanism, and provides a public benchmark with replay-stability filtering. The paper's strengths include matched-backbone ablations that isolate the framework's contribution, consistent gains across three backbones, an honest error analysis in Appendix A.11, and human-agreement evaluation on generated tasks. However, the central comparison is compromised by the evaluation-protocol issue described below: interactive evaluators can alter the environment before the ground-truth script runs, while passive baselines cannot. This confound is acknowledged in the text but not quantified, and it is load-bearing for the headline accuracy and for the claim that IRA outperforms passive evaluators. The RL comparison also lacks statistical detail. With a targeted control experiment, the main claims would be substantially strengthened.

major comments (2)

[Evaluation Protocol; Eq. (5)] The ground-truth label is computed after the interactive evaluator has acted. The protocol states that 'after each live evaluation run, we execute the task-specific evaluation script and use its output as the ground-truth label,' while Eq. (5) acknowledges that 'GUI tools or executable commands may cause state transitions.' Thus for IRA, the script may label a state that IRA itself changed, whereas passive baselines cannot change state. The paper's own note that 'positive/negative totals differ slightly across methods' confirms the effect but provides no quantification. This directly affects the 86.9% headline and the Table 1 comparison. Please add a control: run the verification script both before and after IRA's inspection on the same replayed state; report the label-flip rate and recompute accuracy against pre-interaction labels. Alternatively, restrict IRA to read-only tools during b
[Table 3] The RL claim that IRA rewards are 'comparable' to script rewards rests on one success number per setting (34.9% vs 34.0% vs 33.5%). No number of evaluation tasks, training seeds, or variance is reported, so the 0.9-percentage-point gap may be within noise. Since this result is central to the claim that IRA can replace hand-written evaluation scripts, please provide task-level breakdowns, multiple seeds or confidence intervals, and a significance test (e.g., paired bootstrap over OSWorld tasks).

minor comments (5)

[Evaluation Protocol] The threshold of 0.8 for binary classification is introduced without justification. Since IRA returns a fraction of satisfied conditions, the choice of threshold interacts with the number of proposed conditions. Please report sensitivity to this threshold or justify the choice.
[Ablation Study; Fig. 6] The GUI-only setting allows the evaluator to interact with the environment but lacks the condition-proposal step. This is a useful ablation, but the same state-alteration confound applies: GUI-only evaluators may also change the state before the script runs. Please clarify whether the proposed control (major comment 1) applies to the GUI-only baseline as well.
[Appendix A.1] Application-specific tools are enabled only for corresponding tasks (e.g., check_word_file only for LibreOffice Writer tasks). Since the benchmark has known categories, this gives IRA category-level prior information. Please clarify whether passive baselines receive any category or tool-availability information, and whether the tool set is exposed uniformly in a real deployment scenario.
[Table 2] Token counts are reported as median/mean, but the distribution is clearly heavy-tailed (e.g., Qwen3.6 mean 139.1K). Please include percentiles (e.g., 5th/95th) or a box plot to support the cost comparison.
[References] Several references are 2026 preprints; please update to published versions where available, and ensure all cited items are publicly accessible.

Circularity Check

0 steps flagged

No significant circularity; evaluation is anchored to external scripts, though the interactive protocol creates a state-dependent ground-truth confound.

full rationale

This is an empirical systems paper, not a derivation, and its central quantity—IRA's judgment accuracy on GUI-RewardBench—is anchored to externally defined task-specific scripts rather than to IRA's own outputs. Eq. (8) aggregates condition verdicts with equal weights and a hand-set 0.8 threshold; none of these are fitted to the benchmark labels, and no IRA parameter is tuned to maximize GUI-RewardBench accuracy. The RL experiment compares IRA rewards against the same task scripts without optimizing IRA for the OSWorld success metric. The only self-citation of note (DART) supplies the RL training procedure, not the evaluation criterion, so it is not load-bearing circularity. The one genuine protocol caveat, which the paper explicitly acknowledges, is that interactive verification can alter the environment before the task-specific script runs: the paper states 'evaluator interactions may cause the replayed state to differ from the originally recorded state' and 'GUI tools or executable commands may cause state transitions', and later notes 'positive/negative totals differ slightly across methods'. This makes the ground-truth label state-dependent and is a measurement-validity risk that could bias the reported 86.9% and the comparison with passive baselines, but it is a confound, not an equation-level identity or a fitted-parameter prediction. For circularity, the paper is self-contained against external benchmarks, so score 0.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

No new physical entities are introduced. The framework rests on several domain assumptions: inspectability of the environment, equal-weight condition aggregation, and the validity of post-inspection script labels. Free parameters are design choices rather than fitted-to-test-label quantities, but they do affect the reported numbers and are not independently calibrated.

free parameters (4)

success threshold = 0.8
Hand-chosen threshold converting the scalar reward (fraction of verified conditions) to a binary success verdict; applied uniformly to all evaluators but not justified by any calibration.
max evaluation steps = 30
Manual cap on the ReAct verification loop; limits how much evidence IRA can collect in long-horizon or tool-failure cases.
screenshot retention = 5 most recent screenshots
Context-window design choice that restricts the visual evidence available to IRA and could affect condition proposal quality.
condition aggregation weights = equal weights (1/N)
Equation (8) assumes every completion condition is equally important and independent; no principle or data is given to justify equal weighting.

axioms (4)

domain assumption The post-execution environment state S_env is inspectable and stable enough for tool-based verification.
Equations (3)-(5) assume IRA can query files, configurations, and application state; if key evidence is shielded or changes during inspection, the verification loop cannot recover it.
domain assumption Task completion can be represented as a finite set of binary conditions whose average is a valid scalar reward.
Equation (8), r_ira = (1/N) Σ y_i, assumes equal importance and no interaction between conditions; this is a modeling choice, not a derived property of GUI tasks.
domain assumption Ground-truth script outputs, computed after each live evaluation run, correctly label the state the evaluator was supposed to judge.
The Evaluation Protocol runs the task-specific script after the evaluator has finished. For interactive evaluators that may cause state transitions, the label can refer to a mutated state rather than the original post-execution state.
domain assumption The VLM condition proposer reliably extracts task-grounded conditions from the instruction and initial/final screenshots.
The whole propose-then-verify design depends on the model not inventing, omitting, or mis-scoping conditions; the paper's own error analysis shows this fails in several cases.

pith-pipeline@v1.3.0-alltime-deepseek · 26927 in / 11540 out tokens · 111762 ms · 2026-08-01T01:08:27.957202+00:00 · methodology

0 comments

read the original abstract

Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction. Automated GUI task evaluation has received increasing attention because the evaluation results can serve as reward signals for both test-time scaling and post-training. However, reliable GUI task evaluation remains challenging because the judgments often require access to environment states, such as system configurations, file data, and application settings, beyond the screenshots of execution trajectories. In this paper, we propose an interactive reward agent (IRA) based on a propose-then-verify framework to acquire and verify evidence from the post-execution environment. Given a task instruction and a GUI environment after the GUI agent execution, IRA first proposes the task completion conditions and then verifies them by invoking system tools, application tools, and GUI tools. This design combines evidence from both visible interfaces and the environment state in an interactive process. We further introduce GUI-RewardBench, a benchmark of 321 GUI task trajectories spanning 10 Ubuntu desktop application categories. Experiments show that IRA achieves 86.9% accuracy on GUI-RewardBench, outperforming existing evaluator baselines. We further apply IRA to reinforcement learning of GUI agents, achieving a 34.0% OSWorld success rate, which demonstrates that IRA can provide effective reward signals for training GUI agents.

Figures

Figures reproduced from arXiv: 2607.25904 by Chenrui Shi, Che Sun, Lifeng Fan, Ruining Feng, Yang Liu, Yuwei Wu, Zhi Gao, Zirui Shang.

**Figure 2.** Figure 2: Overview of the Interactive Reward Agent (IRA). Given a task instruction [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Distribution of trajectory length and outcome (success or failure). [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 5.** Figure 5: Qualitative comparisons between a VLM-based [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: Precision–recall trajectories under matched back [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Per-category accuracy comparison between VLM [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 8.** Figure 8: Representative IRA failures caused by misaligned completion conditions: incorrect protection granularity, an overly [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗

discussion (0)

Reference graph

Works this paper leans on

43 extracted references

[1]

The screenshot suggests a Details.docx file exists on the desktop, but the content and correctness of the recorded unit price are hidden

execute_vm_command $ find /home/user -maxdepth 2 -type f -printf ’%p ’ | sort # thought: I need to verify both the created document and the source receipt image. The screenshot suggests a Details.docx file exists on the desktop, but the content and correctness of the recorded unit price are hidden. I will first list the relevant files in /home/user and on...
[2]

Next I need to inspect the DOCX contents to see what number was recorded

get_vm_file $ get_vm_file /home/user/Desktop/Details.docx -> Details.docx # thought: The file listing confirms the relevant source image is likely /home/user/OIP.jpg and that /home/user/Desktop/Details.docx exists. Next I need to inspect the DOCX contents to see what number was recorded. I will download the document for structured Word verification. => ca...
[3]

passed": false,

checkwordfile $ checkwordfile Details.docx -check ’Document contains the text "1450" and no other substantive text besides this number.’ # thought: Now I have the produced Details.docx on the host. The task requires it to contain only the unit price number for a new set of pedal arms. I will inspect the Word file text content. => {"passed": false, "reason...
[4]

I need to determine the actual text in Details.docx and independently inspect the receipt image to identify the correct unit price

execute_vm_command $ python3 extract word/document.xml text from Details.docx # thought: The targeted Word check failed for the guessed value, so I should not rely on that assumption. I need to determine the actual text in Details.docx and independently inspect the receipt image to identify the correct unit price. I will extract the document text directly. => 15
[5]

15", which satisfies the

get_vm_file $ get_vm_file /home/user/OIP.jpg -> OIP.jpg # thought: The document contains only "15", which satisfies the "number only" formatting if the correct unit price is
[6]

Since OCR may be available locally, I will first use read-only CLI image metadata/OCR attempts to inspect the image contents without altering anything

execute_vm_command $ inspect /home/user/OIP.jpg metadata and OCR availability # thought: I need to read the receipt image. Since OCR may be available locally, I will first use read-only CLI image metadata/OCR attempts to inspect the image contents without altering anything. => (474, 670) RGB python packages check pytesseract no No module named ’pytesseract’
[7]

The file manager in the current screenshot shows OIP.jpg selected, so I will open it in the image viewer to read the receipt

computer computer[double_click] at [390, 172] # thought: OCR is not available through the initial command, so I need a visual inspection path. The file manager in the current screenshot shows OIP.jpg selected, so I will open it in the image viewer to read the receipt. => Computer action executed: double_click double_click receipt_details_docx_step-007-after.png
[8]

I likely clicked above the icon area

computer computer[double_click] at [370, 277] # thought: The previous double-click did not open the image; the screenshot still shows the file manager with OIP.jpg selected. I likely clicked above the icon area. I will double-click the selected OIP.jpg icon more centrally to open it for visual inspection. => Computer action executed: double_click double_c...
[9]

Since selecting a file and pressing Enter is a standard way to open it, I will use Enter to open the selected OIP.jpg and inspect the receipt visually

computer computer[key] ENTER # thought: The image appears selected but still not opened. Since selecting a file and pressing Enter is a standard way to open it, I will use Enter to open the selected OIP.jpg and inspect the receipt visually. => Computer action executed: key key: ENTER receipt_details_docx_step-009-after.png
[10]

I will press Escape to leave the field, then select/open the correct OIP.jpg icon area

computer computer[key] ESC # thought: The file did not open because my earlier coordinates were off; the OIP.jpg icon is near the upper middle of the file manager, and the search/location field is now focused. I will press Escape to leave the field, then select/open the correct OIP.jpg icon area. => Computer action executed: key key: ESC receipt_details_d...
[11]

execute_vm_command $ which xdg-open eog gimp display convert python3; ls target files # thought: I need more reliable visual reading of the image. Instead of struggling with GUI coordinates, I can use command-line image processing to enlarge/convert the image, perhaps extract embedded text using available tools like ImageMagick or save a zoomed version, t...
[12]

Although launching a viewer changes UI state, it is acceptable for verification and does not alter the image

execute_vm_command $ eog /home/user/OIP.jpg >/tmp/eog_receipt.log 2>&1 & # thought: eog is available, so I can open the image directly via read-only xdg/eog command for visual inspection. Although launching a viewer changes UI state, it is acceptable for verification and does not alter the image
[13]

I need to wait briefly for it to appear and then use the updated screenshot as visual evidence to read the receipt

computer computer[wait] 1s # thought: I launched the image viewer in the background. I need to wait briefly for it to appear and then use the updated screenshot as visual evidence to read the receipt. => Waited 1 seconds wait: 1s receipt_details_docx_step-013-after.png
[14]

new set of pedal arms

computer computer[key] CTRL++ # thought: The receipt image is now open and visible, but the item table text is still small. I can see the target row appears in the table, but I need higher confidence about the unit price format/value. I will zoom in on the image viewer so the row for "new set of pedal arms" can be read more clearly. => Computer action exe...
[15]

I will retrieve the source image from /home/user/OIP.jpg for inspection

I must verify the receipt image itself to determine the unit price of a new set of pedal arms. I will retrieve the source image from /home/user/OIP.jpg for inspection. => cache/e76c3496-4150-40c5-a306-344519ecf87c/OIP.jpg
[17]

New set of pedal arms

final_answer reward=1 verdict=Success # thought: The evidence now shows the source receipt and the produced document. The receipt row for "New set of pedal arms" shows a unit price of 15.00, and the created /home/user/Desktop/Details.docx contains only "15", which is the requested number without currency or extra text. All critical criteria are satisfied....
[18]

ls: cannot access ’/home/oai/.config/google-chrome’: No such file or directory\n

execute_vm_command $ ls -la /home/oai/.config/google-chrome && find /home/oai/.config/google-chrome -maxdepth 2 -type f -name ’Preferences’ -o -name ’Secure Preferences’ # thought: I need to verify Chrome’s actual startup configuration, because the task is about Chrome opening funbrain.com on launch and the relevant completion condition is hidden in brows...
[19]

I need to identify the actual user/config location and then inspect the relevant Chrome/Chromium preference files without assuming the path

execute_vm_command $ echo $HOME; ls -la /home; find /home -maxdepth 4 -type f $ -name ’Preferences’ -o -name ’Secure Preferences’ $ 2>/dev/null # thought: The expected Chrome profile path was not present, so I cannot conclude from that location. I need to identify the actual user/config location and then inspect the relevant Chrome/Chromium preference f...

2023
[20]

Since Chrome is visibly running, its profile may be in a nonstandard directory or deeper path

execute_vm_command $ ls -la /home/user/.config; ps -eo pid,args | sed -n ’1,200p’ # thought: The home directory is /home/user, and the shallow search did not find Chrome Preferences. Since Chrome is visibly running, its profile may be in a nonstandard directory or deeper path. I will list the relevant configuration directories and running command line to ...

2025
[21]

I need the preference details, especially startup URLs, restore behavior, homepage, and possibly extension settings that could force funbrain.com

execute_vm_command $ find /home/user/.config/google-chrome -maxdepth 3 -type f $ -name ’Preferences’ -o -name ’Secure Preferences’ -o -name ’Local State’ $ -print -exec python3 - «’PY’ {} \; import json,sys,os p=sys.argv[1] print(’ --’,p,’--’) try: data=json.load(open(p)) except Exception as e: print(’ERR’,e); sys.exit() # Print full top-level keys and ...
[22]

I need to inspect the Default profile Preferences and Secure Preferences directly for startup/homepage settings and any occurrences of funbrain.com

execute_vm_command $ python3 - «’PY’ import json, os paths=[’/home/user/.config/google-chrome/Default/Preferences’,’/home/user/.config/google-chrome/Default/Secure Preferences’] for p in paths: print(’ --’,p,’exists’,os.path.exists(p),’--’) if not os.path.exists(p): continue txt=open(p,errors=’replace’).read() print(’contains funbrain:’, ’funbrain’ in txt...
[23]

DIR $d"; find

execute_vm_command $ printf ’ - Chrome policy dirs - ’; for d in /etc/opt/chrome /etc/chromium /etc/google-chrome /usr/share/google-chrome; do echo "DIR $d"; find "$d" -maxdepth 4 -type f 2>/dev/null | sort; done; printf ’ - desktop entries containing chrome/google - ’; find /usr/share/applications /home/user/.local/share/applications /home/user/Desktop -...
[24]

I will recursively enumerate keys/values containing startup, restore, homepage, and any URL-like occurrences in the active Chrome profile

execute_vm_command $ python3 - «’PY’ import json, os, re base=’/home/user/.config/google-chrome/Default’ for name in [’Preferences’,’Secure Preferences’]: p=os.path.join(base,name) print(’ --’,p,’--’) data=json.load(open(p)) def walk(x,path=”): if isinstance(x, dict): for k,v in x.items(): np=f’{path}.{k}’ if path else k s=k.lower() if any(term in s for t...
[25]

funbrain

execute_vm_command $ grep -RIl -exclude=’ *.log’ -exclude=’ *.ldb’ -exclude=’ *.sqlite*’ -exclude=’History’ -exclude=’Cookies’ -exclude=’Cache_Data’ ’funbrain’ /home/user/.config/google-chrome /home/user/.local/share/applications /home/user/Desktop /usr/share/applications /etc/opt/chrome /etc/chromium 2>/dev/null || true # thought: The active profile’s ac...
[26]

final_answer reward=1 verdict=Success # thought: The task requires the final Chrome launch/startup configuration to no longer open funbrain.com. Although the before/after screenshots look visually unchanged on the general Chrome Settings page, the decisive state is hidden in Chrome’s profile, policy, and launcher configuration. I verified the active Chrom...
[27]

ls: cannot access ’/home/oai/.config/vlc’: No such file or directory\n

execute_vm_command $ ls -la /home/oai/.config/vlc && sed -n ’1,260p’ /home/oai/.config/vlc/vlcrc # thought: I need to verify both critical requirements: VLC is currently in windowed minimal interface with the bottom controls hidden (visible in Image B), and the preference is persisted so it will remain after VLC restarts. The visible screenshot strongly s...
[28]

$HOME"; ls -la /home; ls -la

execute_vm_command $ printf ’HOME=%s ’ "$HOME"; ls -la /home; ls -la "$HOME/.config"; if [ -d "$HOME/.config/vlc" ]; then ls -la "$HOME/.config/vlc"; fi # thought: The attempted config inspection used the wrong home path, as the directory ‘/home/oai/.config/vlc‘ does not exist. I need to locate the actual user home and inspect the VLC configuration there,...

2023
[29]

To verify persistence, I need to inspect the Qt interface configuration file where VLC stores window/interface state, especially whether minimal view is enabled after restart

execute_vm_command $ cat /home/user/.config/vlc/vlc-qt-interface.conf # thought: The correct VLC config directory exists under ‘/home/user/.config/vlc‘. To verify persistence, I need to inspect the Qt interface configuration file where VLC stores window/interface state, especially whether minimal view is enabled after restart. => [General] filedialog-path...
[30]

computer computer[left_click] # thought: The Qt interface config confirms status bar is hidden, but it does not explicitly show the minimal-interface toggle or whether the bottom playback controls will remain hidden after a restart. Because persistence after restart is a central task requirement, I should verify it behaviorally by restarting VLC through t...
[31]

I need to close it using the close button at the top-right of the VLC window so that I can restart VLC and verify persistence

computer computer[left_click] # thought: The previous click did not close VLC; the window is still visible in minimal interface. I need to close it using the close button at the top-right of the VLC window so that I can restart VLC and verify persistence. => Computer action executed: left_click left_click vlc_minimal_persistence_step-005-after.png
[32]

I will click that actual close button now to restart VLC for persistence verification

computer computer[left_click] #thought:Irealizemypreviouscloseclicksusedincorrect relative coordinates (too high on the screen); the VLC close button is near the window’s top-right at about x 450, y 122 in the 768x432 screenshot, which corresponds to roughly [586, 282]. I will click that actual close button now to restart VLC for persistence verification....
[33]

execute_vm_command $ ps -ef | grep -i ’[v]lc’; printf ’ -- vlc qt interface -- ’; cat /home/user/.config/vlc/vlc-qt-interface.conf; printf ’ -- vlcrc lines mentioning qt/minimal/interface/controls/status/toolbar -- ’; sed -n ’1,2600p’ /home/user/.config/vlc/vlcrc | awk ’BEGIN{IGNORECASE=1} /qt|minimal|interface|controls|status|toolbar/ {print NR ":" $0}’ ...
[34]

final_answer reward=1 verdict=Success # thought: The visible final state shows VLC in a window with only the video area and title bar, with the menu bar and bottom playback controls hidden, satisfying the visible minimal-interface requirement. The hidden persistence requirement is also supported by VLC’s configuration: ‘/home/user/.config/vlc/vlcrc‘ conta...
[35]

The screenshots show the compose window and a receiver field, but they do not reveal the payment record or allow reliable extraction of all recipients

execute_vm_command $ find /home -maxdepth 4 -type f | sed ’s#^#/##’ | sort # thought: I need to verify the required final state: the Thunderbird draft’s recipient field should contain the email addresses of people who have not paid tuition, as determined from the payment record. The screenshots show the compose window and a receiver field, but they do not...
[36]

execute_vm_command $ ls -laR /home/user/Documents /home/user/Downloads /home/user/Desktop 2>&1 # thought: The broad file listing did not show the tuition payment spreadsheet even though Image A showed it under Documents/Departments/Finance. I need a focused directory listing to determine whether the payment record still exists and where; if it is missing,...

2024
[37]

I need to inspect its contents to identify exactly which students have not paid and their email addresses, then compare that expected set with the Thunderbird recipient field

execute_vm_command $ python3 - «’PY’ from openpyxl import load_workbook path=’/home/user/Documents/Departments/finance/tuition_payment.xlsx’ wb=load_workbook(path, data_only=True) for ws in wb.worksheets: print(’SHEET’, ws.title) for row in ws.iter_rows(values_only=True): print(’\t’.join(” if v is None else str(v) for v in row)) PY # thought: Now I have c...
[38]

Since an .xlsx is a ZIP archive of XML files, I can list and parse its XML contents directly to recover the table values

execute_vm_command $ python3 - «’PY’ import zipfile, xml.etree.ElementTree as ET path=’/home/user/Documents/Departments/finance/tuition_payment.xlsx’ with zipfile.ZipFile(path) as z: print(’FILES’) for n in z.namelist(): print(n) ns={’a’:’http://schemas.openxmlformats.org/spreadsheetml/2006/main’} shared=[] if ’xl/sharedStrings.xml’ in z.namelist(): root=...

2006
[39]

https://accessibility.ubuntu.example.org/ns/state

get_accessibility_tree {} # thought: The spreadsheet shows the unpaid students are Fox, Iron, Nancy, and Stella, with emails fox@someuniversity.edu, iron@someuniversity.edu, nancy@someuniversity.edu, and stella@someuniversity.edu. Image B visibly appears to contain those four addresses in the Thunderbird To field, but I want stronger structured evidence f...
[40]

final_answer reward=1 verdict=Success # thought: I have verified the critical final-state requirements. The payment record lists "No" for Fox, Iron, Nancy, and Stella, whose emails are fox@someuniversity.edu, iron@someuniversity.edu, nancy@someuniversity.edu, and stella@someuniversity.edu. The Thunderbird compose window’s To field contains exactly those f...
[41]

Customers_New_7digit_id.xlsx

execute_vm_command $ find /home -maxdepth 4 -type f | sed ’s#^#/##’ | head -200 # thought: I need to verify the actual spreadsheet contents, not just the visible screenshot, because padding with leading zeros may be represented as text/formatting and many rows are involved. The active document title indicates a file named "Customers_New_7digit_id.xlsx", s...
[42]

New 7 Digit Id

execute_vm_command $ python3 - «’PY’ from openpyxl import load_workbook path=’/home/user/Customers_New_7digit_Id.xlsx’ wb=load_workbook(path,data_only=False) ws=wb.active print(’sheet’, ws.title, ’max_row’, ws.max_row, ’max_col’, ws.max_column) for r in range(1, min(ws.max_row,40)+1): vals=[] for c in range(1, min(ws.max_column,8)+1): cell=ws.cell(r,c) va...
[43]

Old ID" column (C2:C30) is copied into the corresponding row of

final_answer reward=1 verdict=Success # thought: The expected final state is that every value in the existing "Old ID" column (C2:C30) is copied into the corresponding row of "New 7 Digit ID" (D2:D30) as a seven-character identifier with leading zeros, while other customer data and irrelevant blank regions remain unchanged/empty. Visual evidence shows D w...
[2026]

Libre- Office

WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments. InFindings of the Association for Computa- tional Linguistics: ACL 2026, 15262–15280. Li, K.; Meng, Z.; Lin, H.; Luo, Z.; Tian, Y.; Ma, J.; Huang, Z.; and Chua, T.-S. 2025a. Screenspot-pro: Gui grounding forprofessionalhigh-resolutioncomputer...

arXiv 2026

[1] [1]

The screenshot suggests a Details.docx file exists on the desktop, but the content and correctness of the recorded unit price are hidden

execute_vm_command $ find /home/user -maxdepth 2 -type f -printf ’%p ’ | sort # thought: I need to verify both the created document and the source receipt image. The screenshot suggests a Details.docx file exists on the desktop, but the content and correctness of the recorded unit price are hidden. I will first list the relevant files in /home/user and on...

[2] [2]

Next I need to inspect the DOCX contents to see what number was recorded

get_vm_file $ get_vm_file /home/user/Desktop/Details.docx -> Details.docx # thought: The file listing confirms the relevant source image is likely /home/user/OIP.jpg and that /home/user/Desktop/Details.docx exists. Next I need to inspect the DOCX contents to see what number was recorded. I will download the document for structured Word verification. => ca...

[3] [3]

passed": false,

checkwordfile $ checkwordfile Details.docx -check ’Document contains the text "1450" and no other substantive text besides this number.’ # thought: Now I have the produced Details.docx on the host. The task requires it to contain only the unit price number for a new set of pedal arms. I will inspect the Word file text content. => {"passed": false, "reason...

[4] [4]

I need to determine the actual text in Details.docx and independently inspect the receipt image to identify the correct unit price

execute_vm_command $ python3 extract word/document.xml text from Details.docx # thought: The targeted Word check failed for the guessed value, so I should not rely on that assumption. I need to determine the actual text in Details.docx and independently inspect the receipt image to identify the correct unit price. I will extract the document text directly. => 15

[5] [5]

15", which satisfies the

get_vm_file $ get_vm_file /home/user/OIP.jpg -> OIP.jpg # thought: The document contains only "15", which satisfies the "number only" formatting if the correct unit price is

[6] [6]

Since OCR may be available locally, I will first use read-only CLI image metadata/OCR attempts to inspect the image contents without altering anything

execute_vm_command $ inspect /home/user/OIP.jpg metadata and OCR availability # thought: I need to read the receipt image. Since OCR may be available locally, I will first use read-only CLI image metadata/OCR attempts to inspect the image contents without altering anything. => (474, 670) RGB python packages check pytesseract no No module named ’pytesseract’

[7] [7]

The file manager in the current screenshot shows OIP.jpg selected, so I will open it in the image viewer to read the receipt

computer computer[double_click] at [390, 172] # thought: OCR is not available through the initial command, so I need a visual inspection path. The file manager in the current screenshot shows OIP.jpg selected, so I will open it in the image viewer to read the receipt. => Computer action executed: double_click double_click receipt_details_docx_step-007-after.png

[8] [8]

I likely clicked above the icon area

computer computer[double_click] at [370, 277] # thought: The previous double-click did not open the image; the screenshot still shows the file manager with OIP.jpg selected. I likely clicked above the icon area. I will double-click the selected OIP.jpg icon more centrally to open it for visual inspection. => Computer action executed: double_click double_c...

[9] [9]

Since selecting a file and pressing Enter is a standard way to open it, I will use Enter to open the selected OIP.jpg and inspect the receipt visually

computer computer[key] ENTER # thought: The image appears selected but still not opened. Since selecting a file and pressing Enter is a standard way to open it, I will use Enter to open the selected OIP.jpg and inspect the receipt visually. => Computer action executed: key key: ENTER receipt_details_docx_step-009-after.png

[10] [10]

I will press Escape to leave the field, then select/open the correct OIP.jpg icon area

computer computer[key] ESC # thought: The file did not open because my earlier coordinates were off; the OIP.jpg icon is near the upper middle of the file manager, and the search/location field is now focused. I will press Escape to leave the field, then select/open the correct OIP.jpg icon area. => Computer action executed: key key: ESC receipt_details_d...

[11] [11]

execute_vm_command $ which xdg-open eog gimp display convert python3; ls target files # thought: I need more reliable visual reading of the image. Instead of struggling with GUI coordinates, I can use command-line image processing to enlarge/convert the image, perhaps extract embedded text using available tools like ImageMagick or save a zoomed version, t...

[12] [12]

Although launching a viewer changes UI state, it is acceptable for verification and does not alter the image

execute_vm_command $ eog /home/user/OIP.jpg >/tmp/eog_receipt.log 2>&1 & # thought: eog is available, so I can open the image directly via read-only xdg/eog command for visual inspection. Although launching a viewer changes UI state, it is acceptable for verification and does not alter the image

[13] [13]

I need to wait briefly for it to appear and then use the updated screenshot as visual evidence to read the receipt

computer computer[wait] 1s # thought: I launched the image viewer in the background. I need to wait briefly for it to appear and then use the updated screenshot as visual evidence to read the receipt. => Waited 1 seconds wait: 1s receipt_details_docx_step-013-after.png

[14] [14]

new set of pedal arms

computer computer[key] CTRL++ # thought: The receipt image is now open and visible, but the item table text is still small. I can see the target row appears in the table, but I need higher confidence about the unit price format/value. I will zoom in on the image viewer so the row for "new set of pedal arms" can be read more clearly. => Computer action exe...

[15] [15]

I will retrieve the source image from /home/user/OIP.jpg for inspection

I must verify the receipt image itself to determine the unit price of a new set of pedal arms. I will retrieve the source image from /home/user/OIP.jpg for inspection. => cache/e76c3496-4150-40c5-a306-344519ecf87c/OIP.jpg

[16] [17]

New set of pedal arms

final_answer reward=1 verdict=Success # thought: The evidence now shows the source receipt and the produced document. The receipt row for "New set of pedal arms" shows a unit price of 15.00, and the created /home/user/Desktop/Details.docx contains only "15", which is the requested number without currency or extra text. All critical criteria are satisfied....

[17] [18]

ls: cannot access ’/home/oai/.config/google-chrome’: No such file or directory\n

execute_vm_command $ ls -la /home/oai/.config/google-chrome && find /home/oai/.config/google-chrome -maxdepth 2 -type f -name ’Preferences’ -o -name ’Secure Preferences’ # thought: I need to verify Chrome’s actual startup configuration, because the task is about Chrome opening funbrain.com on launch and the relevant completion condition is hidden in brows...

[18] [19]

I need to identify the actual user/config location and then inspect the relevant Chrome/Chromium preference files without assuming the path

execute_vm_command $ echo $HOME; ls -la /home; find /home -maxdepth 4 -type f \( -name ’Preferences’ -o -name ’Secure Preferences’ \) 2>/dev/null # thought: The expected Chrome profile path was not present, so I cannot conclude from that location. I need to identify the actual user/config location and then inspect the relevant Chrome/Chromium preference f...

2023

[19] [20]

Since Chrome is visibly running, its profile may be in a nonstandard directory or deeper path

execute_vm_command $ ls -la /home/user/.config; ps -eo pid,args | sed -n ’1,200p’ # thought: The home directory is /home/user, and the shallow search did not find Chrome Preferences. Since Chrome is visibly running, its profile may be in a nonstandard directory or deeper path. I will list the relevant configuration directories and running command line to ...

2025

[20] [21]

I need the preference details, especially startup URLs, restore behavior, homepage, and possibly extension settings that could force funbrain.com

execute_vm_command $ find /home/user/.config/google-chrome -maxdepth 3 -type f \( -name ’Preferences’ -o -name ’Secure Preferences’ -o -name ’Local State’ \) -print -exec python3 - «’PY’ {} \; import json,sys,os p=sys.argv[1] print(’ --’,p,’--’) try: data=json.load(open(p)) except Exception as e: print(’ERR’,e); sys.exit() # Print full top-level keys and ...

[21] [22]

I need to inspect the Default profile Preferences and Secure Preferences directly for startup/homepage settings and any occurrences of funbrain.com

execute_vm_command $ python3 - «’PY’ import json, os paths=[’/home/user/.config/google-chrome/Default/Preferences’,’/home/user/.config/google-chrome/Default/Secure Preferences’] for p in paths: print(’ --’,p,’exists’,os.path.exists(p),’--’) if not os.path.exists(p): continue txt=open(p,errors=’replace’).read() print(’contains funbrain:’, ’funbrain’ in txt...

[22] [23]

DIR $d"; find

execute_vm_command $ printf ’ - Chrome policy dirs - ’; for d in /etc/opt/chrome /etc/chromium /etc/google-chrome /usr/share/google-chrome; do echo "DIR $d"; find "$d" -maxdepth 4 -type f 2>/dev/null | sort; done; printf ’ - desktop entries containing chrome/google - ’; find /usr/share/applications /home/user/.local/share/applications /home/user/Desktop -...

[23] [24]

I will recursively enumerate keys/values containing startup, restore, homepage, and any URL-like occurrences in the active Chrome profile

execute_vm_command $ python3 - «’PY’ import json, os, re base=’/home/user/.config/google-chrome/Default’ for name in [’Preferences’,’Secure Preferences’]: p=os.path.join(base,name) print(’ --’,p,’--’) data=json.load(open(p)) def walk(x,path=”): if isinstance(x, dict): for k,v in x.items(): np=f’{path}.{k}’ if path else k s=k.lower() if any(term in s for t...

[24] [25]

funbrain

execute_vm_command $ grep -RIl -exclude=’ *.log’ -exclude=’ *.ldb’ -exclude=’ *.sqlite*’ -exclude=’History’ -exclude=’Cookies’ -exclude=’Cache_Data’ ’funbrain’ /home/user/.config/google-chrome /home/user/.local/share/applications /home/user/Desktop /usr/share/applications /etc/opt/chrome /etc/chromium 2>/dev/null || true # thought: The active profile’s ac...

[25] [26]

final_answer reward=1 verdict=Success # thought: The task requires the final Chrome launch/startup configuration to no longer open funbrain.com. Although the before/after screenshots look visually unchanged on the general Chrome Settings page, the decisive state is hidden in Chrome’s profile, policy, and launcher configuration. I verified the active Chrom...

[26] [27]

ls: cannot access ’/home/oai/.config/vlc’: No such file or directory\n

execute_vm_command $ ls -la /home/oai/.config/vlc && sed -n ’1,260p’ /home/oai/.config/vlc/vlcrc # thought: I need to verify both critical requirements: VLC is currently in windowed minimal interface with the bottom controls hidden (visible in Image B), and the preference is persisted so it will remain after VLC restarts. The visible screenshot strongly s...

[27] [28]

$HOME"; ls -la /home; ls -la

execute_vm_command $ printf ’HOME=%s ’ "$HOME"; ls -la /home; ls -la "$HOME/.config"; if [ -d "$HOME/.config/vlc" ]; then ls -la "$HOME/.config/vlc"; fi # thought: The attempted config inspection used the wrong home path, as the directory ‘/home/oai/.config/vlc‘ does not exist. I need to locate the actual user home and inspect the VLC configuration there,...

2023

[28] [29]

To verify persistence, I need to inspect the Qt interface configuration file where VLC stores window/interface state, especially whether minimal view is enabled after restart

execute_vm_command $ cat /home/user/.config/vlc/vlc-qt-interface.conf # thought: The correct VLC config directory exists under ‘/home/user/.config/vlc‘. To verify persistence, I need to inspect the Qt interface configuration file where VLC stores window/interface state, especially whether minimal view is enabled after restart. => [General] filedialog-path...

[29] [30]

computer computer[left_click] # thought: The Qt interface config confirms status bar is hidden, but it does not explicitly show the minimal-interface toggle or whether the bottom playback controls will remain hidden after a restart. Because persistence after restart is a central task requirement, I should verify it behaviorally by restarting VLC through t...

[30] [31]

I need to close it using the close button at the top-right of the VLC window so that I can restart VLC and verify persistence

computer computer[left_click] # thought: The previous click did not close VLC; the window is still visible in minimal interface. I need to close it using the close button at the top-right of the VLC window so that I can restart VLC and verify persistence. => Computer action executed: left_click left_click vlc_minimal_persistence_step-005-after.png

[31] [32]

I will click that actual close button now to restart VLC for persistence verification

computer computer[left_click] #thought:Irealizemypreviouscloseclicksusedincorrect relative coordinates (too high on the screen); the VLC close button is near the window’s top-right at about x 450, y 122 in the 768x432 screenshot, which corresponds to roughly [586, 282]. I will click that actual close button now to restart VLC for persistence verification....

[32] [33]

execute_vm_command $ ps -ef | grep -i ’[v]lc’; printf ’ -- vlc qt interface -- ’; cat /home/user/.config/vlc/vlc-qt-interface.conf; printf ’ -- vlcrc lines mentioning qt/minimal/interface/controls/status/toolbar -- ’; sed -n ’1,2600p’ /home/user/.config/vlc/vlcrc | awk ’BEGIN{IGNORECASE=1} /qt|minimal|interface|controls|status|toolbar/ {print NR ":" $0}’ ...

[33] [34]

final_answer reward=1 verdict=Success # thought: The visible final state shows VLC in a window with only the video area and title bar, with the menu bar and bottom playback controls hidden, satisfying the visible minimal-interface requirement. The hidden persistence requirement is also supported by VLC’s configuration: ‘/home/user/.config/vlc/vlcrc‘ conta...

[34] [35]

The screenshots show the compose window and a receiver field, but they do not reveal the payment record or allow reliable extraction of all recipients

execute_vm_command $ find /home -maxdepth 4 -type f | sed ’s#^#/##’ | sort # thought: I need to verify the required final state: the Thunderbird draft’s recipient field should contain the email addresses of people who have not paid tuition, as determined from the payment record. The screenshots show the compose window and a receiver field, but they do not...

[35] [36]

execute_vm_command $ ls -laR /home/user/Documents /home/user/Downloads /home/user/Desktop 2>&1 # thought: The broad file listing did not show the tuition payment spreadsheet even though Image A showed it under Documents/Departments/Finance. I need a focused directory listing to determine whether the payment record still exists and where; if it is missing,...

2024

[36] [37]

I need to inspect its contents to identify exactly which students have not paid and their email addresses, then compare that expected set with the Thunderbird recipient field

execute_vm_command $ python3 - «’PY’ from openpyxl import load_workbook path=’/home/user/Documents/Departments/finance/tuition_payment.xlsx’ wb=load_workbook(path, data_only=True) for ws in wb.worksheets: print(’SHEET’, ws.title) for row in ws.iter_rows(values_only=True): print(’\t’.join(” if v is None else str(v) for v in row)) PY # thought: Now I have c...

[37] [38]

Since an .xlsx is a ZIP archive of XML files, I can list and parse its XML contents directly to recover the table values

execute_vm_command $ python3 - «’PY’ import zipfile, xml.etree.ElementTree as ET path=’/home/user/Documents/Departments/finance/tuition_payment.xlsx’ with zipfile.ZipFile(path) as z: print(’FILES’) for n in z.namelist(): print(n) ns={’a’:’http://schemas.openxmlformats.org/spreadsheetml/2006/main’} shared=[] if ’xl/sharedStrings.xml’ in z.namelist(): root=...

2006

[38] [39]

https://accessibility.ubuntu.example.org/ns/state

get_accessibility_tree {} # thought: The spreadsheet shows the unpaid students are Fox, Iron, Nancy, and Stella, with emails fox@someuniversity.edu, iron@someuniversity.edu, nancy@someuniversity.edu, and stella@someuniversity.edu. Image B visibly appears to contain those four addresses in the Thunderbird To field, but I want stronger structured evidence f...

[39] [40]

final_answer reward=1 verdict=Success # thought: I have verified the critical final-state requirements. The payment record lists "No" for Fox, Iron, Nancy, and Stella, whose emails are fox@someuniversity.edu, iron@someuniversity.edu, nancy@someuniversity.edu, and stella@someuniversity.edu. The Thunderbird compose window’s To field contains exactly those f...

[40] [41]

Customers_New_7digit_id.xlsx

execute_vm_command $ find /home -maxdepth 4 -type f | sed ’s#^#/##’ | head -200 # thought: I need to verify the actual spreadsheet contents, not just the visible screenshot, because padding with leading zeros may be represented as text/formatting and many rows are involved. The active document title indicates a file named "Customers_New_7digit_id.xlsx", s...

[41] [42]

New 7 Digit Id

execute_vm_command $ python3 - «’PY’ from openpyxl import load_workbook path=’/home/user/Customers_New_7digit_Id.xlsx’ wb=load_workbook(path,data_only=False) ws=wb.active print(’sheet’, ws.title, ’max_row’, ws.max_row, ’max_col’, ws.max_column) for r in range(1, min(ws.max_row,40)+1): vals=[] for c in range(1, min(ws.max_column,8)+1): cell=ws.cell(r,c) va...

[42] [43]

Old ID" column (C2:C30) is copied into the corresponding row of

final_answer reward=1 verdict=Success # thought: The expected final state is that every value in the existing "Old ID" column (C2:C30) is copied into the corresponding row of "New 7 Digit ID" (D2:D30) as a seven-character identifier with leading zeros, while other customer data and irrelevant blank regions remain unchanged/empty. Visual evidence shows D w...

[43] [2026]

Libre- Office

WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments. InFindings of the Association for Computa- tional Linguistics: ACL 2026, 15262–15280. Li, K.; Meng, Z.; Lin, H.; Luo, Z.; Tian, Y.; Ma, J.; Huang, Z.; and Chua, T.-S. 2025a. Screenspot-pro: Gui grounding forprofessionalhigh-resolutioncomputer...

arXiv 2026