VQV Signal
WhatWorkedBench is introduced to evaluate AI agents' ability to predict outcomes of experimental changes by inspecting code and forecasting performance across configurations. It uses exhaustive CPU execution to provide accurate response surfaces for component settings.
WhatWorkedBench is introduced to evaluate AI agents' ability to predict outcomes of experimental changes by inspecting code and forecasting performance across configurations. It uses exhaustive CPU execution to provide accurate response surfaces for component settings.
AI-assisted summary based on the listed source.
AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding, the accuracy of predictions about component changes after budgeted experimentation. Agents inspect code, select measurements, and submit a response surface, a...
Reliable experimental understanding enables AI agents to optimize and adapt systems more effectively by anticipating the impact of changes. This benchmark provides a standardized way to measure and improve such predictive capabilities.
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VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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