#calibration
3 articles
01
·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.
02
·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.
03
·Probability & Statistics·★ MEMBER·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.