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STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios

arXiv AI Archived Jun 10, 2026 ✓ Full text saved

arXiv:2606.10394v1 Announce Type: new Abstract: Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existing benchmarks still rely on sandboxed artifacts, static task design, and coarse scoring, which hinder scalability and limit progress toward reliable personal-agent evaluation. This paper introduces STAGE-Claw, an automated framework for building and evaluating realistic personal-agent scenarios in st

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    Computer Science > Artificial Intelligence [Submitted on 9 Jun 2026] STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios Sirui Liang, Bohan Yu, Peiyu Wang, Shiguang Guo, Wenxing Hu, Pengfei Cao, Jian Zhao, Cao Liu, Ke Zeng, Xunliang Cai, Kang Liu Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existing benchmarks still rely on sandboxed artifacts, static task design, and coarse scoring, which hinder scalability and limit progress toward reliable personal-agent evaluation. This paper introduces STAGE-Claw, an automated framework for building and evaluating realistic personal-agent scenarios in state-based personal-computing environments. Given a task hint, STAGE-Claw automatically creates and validates a realistic benchmark task with its environment, task prompts, ground truth, and related verification programs. Agents are then evaluated in realistic operating environments, where performance is measured by the correctness of the final system state rather than only the textual response. Using STAGE-Claw, this paper creates a benchmark with 40 challenging real scenario agent tasks, evaluates 11 frontier models, and analyzes their task scores, costs, tool-call reliability, and common failure patterns. Overall, STAGE-Claw offers a scalable, state-based way to evaluate agents in realistic user scenarios. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.10394 [cs.AI]   (or arXiv:2606.10394v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.10394 Focus to learn more Submission history From: Sirui Liang [view email] [v1] Tue, 9 Jun 2026 04:16:35 UTC (2,414 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Jun 10, 2026
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    Jun 10, 2026
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