STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
Quick Answer
STAGE-Claw introduces an automated framework for evaluating personal agents in realistic scenarios, creating 40 benchmark tasks and assessing 11 models based on final system state correctness.
Quick Take
This approach enhances scalability and reliability in personal-, addressing limitations of traditional benchmarks.
Key Points
- STAGE-Claw automates the creation and validation of benchmark tasks for personal agents.
- Evaluates agents based on the correctness of the final system state, not just textual responses.
- Created a benchmark with 40 challenging real scenario tasks for comprehensive evaluation.
- Assessed 11 frontier models, analyzing task scores, costs, and common failure patterns.
- Offers a scalable, state-based evaluation method for realistic user scenarios.
Paper Resources
Source Excerpt
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-. This paper introduces STAGE-Claw, an automated framework for building and evaluating realistic personal-agent scenarios in state-based personal-computing environments. Given
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