Pith. sign in

REVIEW 9 cited by

The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use

Not yet reviewed by Pith; the record is open.

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

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.10323 v1 pith:UGARQASQ submitted 2024-11-15 cs.AI cs.CLcs.CV

Siyuan Hu , Mingyu Ouyang , Difei Gao , Mike Zheng Shou This is my paper
classification cs.AIcs.CLcs.CV
keywords computerclaudeagentcasebetacasesfuturemodel
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The recently released model, Claude 3.5 Computer Use, stands out as the first frontier AI model to offer computer use in public beta as a graphical user interface (GUI) agent. As an early beta, its capability in the real-world complex environment remains unknown. In this case study to explore Claude 3.5 Computer Use, we curate and organize a collection of carefully designed tasks spanning a variety of domains and software. Observations from these cases demonstrate Claude 3.5 Computer Use's unprecedented ability in end-to-end language to desktop actions. Along with this study, we provide an out-of-the-box agent framework for deploying API-based GUI automation models with easy implementation. Our case studies aim to showcase a groundwork of capabilities and limitations of Claude 3.5 Computer Use with detailed analyses and bring to the fore questions about planning, action, and critic, which must be considered for future improvement. We hope this preliminary exploration will inspire future research into the GUI agent community. All the test cases in the paper can be tried through the project: https://github.com/showlab/computer_use_ootb.

Discussion (0). Sign in to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding

    cs.CV 2025-11 conditional novelty 7.0 of 10

    Supervising an MLLM's intrinsic self-attention with patch-level GUI labels, aggregated via a learnable anchor token and hidden-state-selected query tokens, reaches state-of-the-art 3B-scale GUI grounding accuracy with...

  2. MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios

    cs.AI 2026-02 conditional novelty 6.0 of 10

    MobilityBench is a 100,000-episode benchmark with a replay sandbox for deterministic evaluation of LLM route-planning agents; current models score well on basic tasks but fail preference-constrained routing.

  3. Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight

    cs.HC 2025-09 conditional novelty 6.0 of 10

    GUI agents frequently fall for deceptive interface designs, often without recognizing them, and human supervision of agents improves avoidance only partially while introducing new attention and workload costs.

  4. AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents

    cs.CR 2025-09 conditional novelty 6.0 of 10

    AgentSentinel combines system-level tracing with LLM-based auditing to block 79.6% of attacks in the authors' 60-scenario computer-use agent benchmark.

  5. What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A self-generating graph benchmark produces 36k GUI agent tasks with controllable complexity and ten capability scores, and fine-tuning on its trajectories gives small gains on AndroidControl and OmniAct.

  6. Plover: Steering GUI Agents through Plan-Centric Interaction

    cs.AI 2026-07 conditional novelty 5.0 of 10

    An expert repairing visible plans rescued 23 of 26 failed GUI automation runs, turning 17 into full and 6 into partial successes.

  7. OmniActor: A Generalist GUI and Embodied Agent for 2D&3D Worlds

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A generalist agent with shared shallow layers and task-separated deep experts outperforms single-domain GUI and embodied agents on AndroidControl, GUI-Odyssey, and LIBERO benchmarks.

  8. Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skills

    cs.AI 2025-06 conditional novelty 5.0 of 10

    Mirage-1 combines a hierarchical multimodal skill memory with a skill-augmented Monte Carlo tree search to outperform prior GUI agents on Android and web online benchmarks.

  9. How Small Transformation Expose the Weakness of Semantic Similarity Measures

    cs.CL 2025-09 reject novelty 4.0 of 10

    A diagnostic benchmark of text and code transformations finds embedding similarity metrics often conflate opposition with equivalence; LLM judges discriminate better, and Euclidean distance improves code embeddings.

Pith tools