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When does a scaling result justify a different allocation? A critical review of resource-allocation evidence for AI systems

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

AI scaling studies increasingly evaluate systems that combine a pretrained model with retrieval, search, verification, tools, and interaction. Yet a higher score under a larger budget does not by itself show where additional resources are best spent. This critical integrative review asks when a reported scaling result supports a resource-allocation decision. It compares evidence across pretraining, test-time computation, retrieval, and agent evaluation, distinguishing the performance of a tested procedure from the best performance achievable under a resource limit. The synthesis shows that thr

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