Unverified50% confidenceFactExact time
Test-Time Scaling refers to methods that improve model performance at the inference stage by increasing computation (such as multiple sampling, longer CoT, tree search, or voting mechanisms).
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50%
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Long-term
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11/3/2026
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UnverifiedIt explores test-time scaling through parallel agent execution and self-verification.78% similarUnverifiedThe technical basis for reasoning-intensity tiers usually comes from test-time compute scaling; the higher the tier, the larger the thinking token budget, number of samples, or search depth allowed.77% similarUnverifiedThe evaluation closed loop is a typical application of the test-time compute research direction, improving output quality by investing more computation during the inference stage rather than relying on larger model parameters.76% similarUnverifiedSliding windows, block summaries, and dynamic retrieval all add inference-time computation, and whether this overhead is acceptable in long-sequence scenarios requires more practical testing to verify.74% similarUnverifiedTest-Time Compute improves answer quality by adding sampling, search, or reflection steps at the inference stage, for example Self-Consistency sampling multiple reasoning paths in parallel and then voting, and Reflexion letting the Agent self-criticize and retry; each sample and each parallel path corresponds to a complete forward computation.73% similar
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