• Two separate knobs raise accuracy: compute spent training and compute spent generating. OpenAI's o1 curves showed both work, and the practical move is to turn both, training a stronger model and then scaling its inference on top of that.
• Inference-time scaling is an old idea in new clothes. Classic ML ensembles bought accuracy the same way, by running more computation at prediction time instead of changing the model.
• It travels under several names, test-time scaling, inference-compute scaling, inference scaling, all meaning training-free methods that leave the weights untouched. That is why they apply to any model, including ones you did not train.

• test-time compute — Spending more computation when the model answers — thinking longer, trying multiple attempts — to buy accuracy without training a bigger model.
• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

It gives a clear, practical taxonomy of inference-time scaling methods with code and experimental results, so you can pick the right technique (self-consistency vs. best-of-N vs. search) when squeezing more reasoning out of an LLM at test time.

• articleA Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
• articleBeyond Standard LLMs
• articleFrom GPT-2 to gpt-oss: Analyzing the Architectural Advances
• articleUnderstanding the 4 Main Approaches to LLM Evaluation (From Scratch)

• articleScaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model ParametersCharlie Snell et al.
• articleDiverse reasoning traces teach LLMs to make better decisionswww.amazon.science
• articleWhy We ThinkLilian Weng

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