#calibration

5 articles

01 ·Paper Deep-Dives·★ MEMBER·PAPER·6 min read Paper Explained: Persona Dosing — Asking an LLM for a Personality Trait by the Score A steering coefficient says nothing about how strongly a trait will show up. PersonaDose trains one description-conditioned controller, then measures a strength-to-score map after training, so you can ask for "sycophancy at 60" instead of guessing a number. 02 ·Inference & Serving·★ MEMBER·PAPER·6 min read Paper Explained: JEV-as-a-Judge — Accept When Confident, Escalate When Unsure A cheap judge that returns only a verdict and label probabilities handles the first pass, and only its low-confidence calls go to a strong LLM. This walkthrough covers the paper's cost measurements, where the two-stage setup works, and where it breaks. 03 ·Training & Alignment·★ MEMBER·PAPER·10 min read PAWBench Explained — Can Video Generators Get the Odds Right, Not Just the Physics? If a video generator is a world model, it owes you more than one plausible rollout — it owes you the right distribution over futures. PAWBench measures that probabilistic alignment across 50 scenarios and 11 systems, and finds that no model gets all the requirements at once. 04 ·Distillation & Compression·★ MEMBER·PAPER·9 min read Evaluating Distilled Models — Is "Close to the Teacher" a Good Metric? Score a distilled student by how often it agrees with its teacher and the students that faithfully reproduce the teacher's mistakes come out on top. What agreement actually guarantees, what breaks outside the training distribution, and how to test for contamination that arrives paraphrased through synthetic data. 05 ·Probability & Statistics·FREE·PAPER·10 min read Bayes' Theorem in AI — Priors, Posteriors, and Uncertainty One line of math — Bayes' theorem — turns into three workhorse tools in real AI systems: probability calibration, active learning, and Bayesian optimization. Starting from a positive medical test, we build up priors, posteriors, and uncertainty with zero background assumed.