AI Principles
The science behind AI — transformer architectures, training dynamics, and evaluation methodology. MONA explains how AI actually works, with precision over hype.
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From Vanilla RNN to LSTM and GRU: How Gating Mechanisms Solved the Long-Term Memory Problem
Trace how LSTM forget, input, and output gates fix the vanishing gradient problem that crippled vanilla RNNs, and how …
From ReLU to SwiGLU: How Activation and Loss Functions Shape LLM Training
Trace the path from ReLU to SwiGLU and understand how activation functions, cross-entropy loss, and gradient dynamics …
Backpropagation and Gradient Descent: How Neural Networks Learn From Errors
Learn how backpropagation and gradient descent train neural networks by propagating error signals backward through …
What Is an Evaluation Harness and How Standardized Frameworks Benchmark LLMs
Evaluation harnesses standardize LLM benchmarking by fixing prompts, scoring, and conditions. Learn how the pipeline …
Benchmark Contamination, Score Divergence, and the Technical Limits of LLM Evaluation Harnesses
Same model, same benchmark, different scores. Understand why evaluation harnesses diverge and how benchmark …
What Is Benchmark Contamination and How Training Data Overlap Inflates LLM Evaluation Scores
Benchmark contamination inflates LLM scores when training data overlaps with test sets. Learn how data leaks in and why …
Benchmark Contamination: N-Gram Overlap and Hard Limits
Benchmark contamination and overfitting look identical in scores. Understand what n-gram overlap, deduplication, and …
From Baselines to Factorial Design: Prerequisites and Core Components of Ablation Experiment Design
Ablation studies reveal which components matter, but only with the right baselines, controls, and statistical methods. …
Class Imbalance, Normalization Traps, and the Hard Limits of Confusion Matrix Analysis
Confusion matrices hide failures under class imbalance. Learn how normalization direction changes what you see and why …
What Is the MMLU Benchmark and How 57 Academic Subjects Test LLM Knowledge
MMLU tests large language models across 57 academic subjects with 15,908 questions. Learn how it works, where it breaks, …
What Is an Ablation Study and How Removing Components Reveals What Makes AI Models Work
Ablation studies reveal what each model component does by removing it. Learn the experimental design and failure modes …
What Is a Confusion Matrix and How It Reveals Where Your Classifier Fails
A confusion matrix reveals exactly where classifiers fail. Understand true positives, false negatives, and why accuracy …
MMLU's 6.5% Label Error Rate and Benchmark Score Saturation
MMLU's 6.5% label error rate means frontier models cluster above 88%, saturating scores. Score saturation explains why …
From Perplexity to Few-Shot Prompting: Prerequisites for Understanding Evaluation Harness Internals
Evaluation harness scores depend on perplexity, few-shot prompting, and tokenization most teams skip. Learn the …
From Binary to Multi-Class: Deriving Precision, Recall, and F1 from a Confusion Matrix
Precision, recall, and F1 all come from the same confusion matrix. Learn to extract each metric for binary and …
Combinatorial Explosion, Interaction Effects, and the Hard Limits of Ablation Studies at Scale
Ablation studies hit a wall at scale: combinatorial explosion and non-additive interactions make exhaustive testing of …
Why F1 Score Fails on Imbalanced Datasets: MCC, PR-AUC, and the Limits of Harmonic Averaging
F1 score hides classifier failures on imbalanced datasets by ignoring true negatives. Learn why MCC and PR-AUC reveal …
Precision, Recall, F1 Score: What the Confusion Matrix Reveals
What accuracy won't show: precision, recall, and F1 score expose true classifier performance. The confusion matrix …
From True Positives to Macro Averaging: The Building Blocks Behind Precision, Recall, and F1
Precision, recall, and F1 score measure what accuracy hides. Learn how true positives, confusion matrices, and macro …
What Is Model Evaluation and How Benchmarks, Metrics, and Human Judgment Measure LLM Quality
Model evaluation combines benchmarks, automated metrics, and human judgment to measure LLM quality. Learn why high …
Perplexity, BLEU, ROUGE, and ELO: The Core Metrics Behind LLM Evaluation Explained
Perplexity, BLEU, ROUGE, and Elo measure fundamentally different properties of language models. Learn when each metric …
Benchmark Contamination, Metric Gaming, and the Hard Limits of LLM Evaluation
Benchmark contamination inflates LLM scores while real-world performance lags. Learn why metric gaming and saturated …
What Are Bias and Fairness Metrics and How They Detect Discrimination in ML Predictions
Fairness metrics test whether ML models discriminate by group. Learn how disparate impact, equalized odds, and the …
The Impossibility Theorem and Why No Model Can Satisfy Every Fairness Metric at Once
When group base rates differ, no algorithm satisfies calibration, equal error rates, and demographic parity at once. …
What Is Toxicity and Safety Evaluation and How Guard Models Score Harmful AI Outputs
Toxicity and safety evaluation scores AI outputs for harm using classifiers and red teaming. Learn how guard models …
HarmBench, ToxiGen, and MLCommons Taxonomy: The Datasets and Standards Behind AI Safety Testing
HarmBench, ToxiGen, and MLCommons AILuminate define how AI safety is measured. Learn the datasets, classifiers, and …
Demographic Parity vs. Equalized Odds vs. Calibration: Core Fairness Metrics Compared
Demographic parity, equalized odds, and calibration define fairness differently and cannot all be satisfied at once. …
False Positives in Toxicity Detection: Dialect Bias, Bypasses
Toxicity classifiers over-flag minority dialects and miss adversarial attacks. Explore the statistical bias—from dialect …
Red Teaming for AI: Adversarial Testing Exposes Failures
Red teaming uses adversarial testing to reveal AI vulnerabilities. Discover what it catches, mechanics, and why it …
OWASP LLM Top 10, MITRE ATLAS, and the Frameworks That Structure AI Red Teaming
OWASP LLM Top 10 and MITRE ATLAS give red teams structured attack categories. Learn how these frameworks turn AI …