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STAGE-Claw addresses the critical gap in agent evaluation by shifting from static, sandboxed tasks to automated, state-based benchmarks that measure final system correctness rather than just textual responses. By enabling scalable testing across 40 realistic scenarios with 11 frontier models, the framework provides actionable insights into tool-call reliability and failure patterns essential for deploying robust personal agents. This approach offers a necessary evolution in benchmarking methodology to support the next generation of autonomous software systems.
STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
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 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.
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 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.
Artificial Intelligence (cs.AI)
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 arXiv-issued DOI via DataCite (pending registration)
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