Beyond Size: The Three Pillars of Test-Time Scaling in Large Language Models

Short summary

A summary of Hariri et al. (2026) formalizing test-time scaling in LLMs into three structural regimes: path-level (chain-of-thought), leaf-level (best-of-N / majority voting), and prefix-level (beam search, MCTS). The paper argues that accuracy is no longer a fixed model property but a function of the inference protocol, and that current evaluation metrics are inadequate for this new era. The shift moves from scaling model size to scaling inference-time compute.

• •Three test-time scaling regimes: path-level (CoT), leaf-level (Best-of-N), prefix-level (MCTS/beam search)
• •Accuracy is now a function of inference protocol, not just model parameters
• •Current evaluation metrics are ill-equipped for test-time scaling era

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