test-time-compute
Models are being trained to give up News
New work targets the compute burned when a model produces confident, plausible reasoning on problems it cannot solve, with one method training models to refuse instead of reasoning and another cutting total token use by up to 68.5 percent by reallocating compute mid-thought.
Asking a model to check its own work lost every comparison against just sampling more answers News
A controlled experiment counting every token spent found that seven popular self-improvement methods - self-refinement, reflection, debate, best-of-N selection - never reliably beat the simplest baseline of generating several answers and taking the most common one, and ten of them were reliably worse.
Why Asking an AI the Same Question 10,000 Times Barely Helps News
A new analysis shows that sampling many answers from an AI and picking the most common one hits a hard ceiling because the samples are correlated, not independent, so thousands of extra tries can be worth only a couple of genuinely new ones.
Test-Time Compute: Spending More Thinking at the Moment You Ask Lesson
Test-time compute is the idea of making an AI smarter not by retraining it, but by letting it do more work the moment you ask a question: think longer, or generate many answers and pick one.
Meta Muse Image Tool
Meta's agentic image model that uses test-time compute - searching, coding, and iteratively refining its own output - to reach higher quality than a single-pass generator. (The default Instagram-photo training was pulled after backlash; the model remains.)