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 v
The rapid deployment and increasing sophistication of large language models for personal agents necessitate improved evaluation frameworks beyond existing sandboxed and static methods.
Reliable benchmarking frameworks like STAGE-Claw are critical for accelerating the development and ensuring the safety and efficacy of personal AI agents in real-world applications.
The ability to automatically create and evaluate realistic personal-agent scenarios means faster iteration cycles and more robust development for AI agent systems.
- · AI agent developers
- · cloud computing providers
- · AI research institutions
- · manual testing frameworks
- · legacy software vendors
Automated evaluation reduces development time and cost for personal AI agents.
More robust and reliable personal AI agents proliferate across various industries, enhancing productivity.
The widespread adoption of highly capable agents fundamentally redefines human-computer interaction and white-collar work.
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Read at arXiv cs.AI