AI evaluation

GLOSARIO

Temperature: Definition, Mechanics, and Limits of the Sampling Parameter in Language Models

Temperature is an inference parameter that modifies the probability distribution of logits to control output randomness. This entry explains its mathematical mechanism, its interaction with top-k and top-p, and its limits for reproducibility and evaluation.

22 Oct 2026 ↗

GLOSARIO

Reasoning in Models: How It Works, Examples, and Limits

A rigorous definition of reasoning capabilities in language models, a step-by-step explanation of their operation, three applied examples, evaluation methods, and an analysis of documented limitations from primary literature.

21 Oct 2026 ↗

GLOSARIO

Overfitting: Definition, Detection Mechanisms, and Limits of Generalization

Overfitting occurs when a machine learning model memorizes noise and specific patterns in training data, achieving high accuracy on that data but failing to generalize to new inputs. This technical guide explains the phenomenon through the bias-variance tradeoff, details detection methods using loss curves and cross-validation, and contrasts overfitting with underfitting and data leakage. It includes applied examples in fraud detection, medical diagnosis, and logistics, along with a mitigation protocol based on regularization, early stopping, and data augmentation.

20 Oct 2026 ↗

GLOSARIO

Adversarial Testing: Definition, Methodology, and Limits in AI Systems

Adversarial testing in AI is an intentional evaluation discipline that seeks to break a system's guarantees through hostile inputs or designed scenarios. This entry defines the concept, distinguishes manual practice from automation, details the technical lifecycle, and analyzes its inherent limits regarding coverage and reproducibility.

17 Oct 2026 ↗