Quantitative Indicator System for the Assessment of Intelligent Body Capabilities
The pass@K evaluation metric designed by MCPMark redefines the dimensions of measuring the performance of AI intelligences. The metric effectively distinguishes between a model's single burst and continuous stability by calculating the task success rate in K independent attempts. When specifically implemented, the system records the model's multi-dimensional performance in terms of the accuracy of code submission, the completeness of process steps, and the reasonableness of exception handling, and ultimately generates a three-dimensional evaluation report containing pass@1 (first-time success rate), pass@5 (success rate within five attempts), and avg@K (average performance score).
Compared to the binary judgment of traditional benchmarking, this multi-round verification mechanism can more accurately reflect the reliability of the intelligences in real business scenarios. For example, in the GitHub task group test, a high-quality model may exhibit a pass@5 pass rate of 90%+, but only a pass@1 performance of 70%. This data discrepancy reveals the potential of the model to improve task completion through self-correction, which provides an important reference for the design of fault-tolerant mechanisms for intelligentsia.
This answer comes from the articleMCPMark: Benchmarking the Ability of Large Model-Integrated MCPs to Perform Intelligent Body TasksThe
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