arXiv:2606.16802v1 Announce Type: new Abstract: Current computer-use benchmarks primarily focus on software operation tasks in virtualized systems, whereas scientific instrumentation scenarios require coordinated control over complex interfaces, and feedback-driven parameter adjustment. However, directly evaluating agents on physical high-precision instruments is impractical due to high cost, safety risks, limited accessibility, and difficulty in ensuring reproducible evaluation. This motivates the need for a simulated yet realistic testbed that preserves the operational challenges of scientif
The rapid advancement in AI agents demands sophisticated benchmarking in real-world applications, especially for complex scientific tasks where physical evaluation is impractical.
This development addresses a critical bottleneck in evaluating AI agents for scientific instrument control, enabling faster, safer, and more reproducible development of autonomous research systems.
The ability to reliably benchmark AI agents in a simulated scientific environment will accelerate their deployment from software-only tasks to physical, high-precision industrial and research control.
- · AI Agent Developers
- · Scientific Research Institutions
- · Automation Software Providers
- · Biotech/Pharma Labs
- · Manual Scientific Operators (long-term)
- · Traditional Automation Methods
Improved reliability and autonomous capabilities of AI agents in laboratory and industrial settings.
Accelerated scientific discovery and automation of complex experimental workflows across various fields.
Potential for fully autonomous laboratories requiring minimal human intervention, dramatically increasing research throughput and precision.
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Read at arXiv cs.AI