Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.

Anthropic study finds that longer reasoning during inference can harm LLM accuracy and amplify unsafe tendencies.

New research reveals that longer reasoning processes in large language models can degrade performance, raising concerns for AI safety and enterprise use.

New research shows that longer reasoning processes in large AI models do not always lead to better performance. Instead, ...

Jean-Pierre Florens, Joël L. Horowitz, Ingrid Van Keilegom, Bias-Corrected Confidence Intervals in a Class of Linear Inverse Problems, Annals of Economics and Statistics, No. 128 (December 2017), pp.

Sliced inverse regression (SIR) and principal Hessian directions aim to reduce the dimensionality of regression problems. An important step in the method is the determination of a suitable dimension.

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

Goldilocks vs. Robin Hood: Using Shape-Constrained Regression to Evaluate U-Shaped (or Inverse U-Shaped) Theories in Data. ... The final model (“just right”) permits exactly one inflection point.