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Best AI papers explained

Por: Enoch H. Kang

Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

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Latest episodes of the podcast Best AI papers explained

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Dynamic Search for Inference-Time Alignment in Diffusion Models 15/05/2025

Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective 12/05/2025

Leaked Claude Sonnet 3.7 System Instruction tuning 12/05/2025

Converging Predictions with Shared Information 11/05/2025

Test-Time Alignment Via Hypothesis Reweighting 11/05/2025

Rethinking Diverse Human Preference Learning through Principal Component Analysis 11/05/2025

Active Statistical Inference 10/05/2025

Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework 10/05/2025

AI-Powered Bayesian Inference 10/05/2025

Can Unconfident LLM Annotations Be Used for Confident Conclusions? 09/05/2025

Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI 09/05/2025

Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control 09/05/2025

How to Evaluate Reward Models for RLHF 09/05/2025

LLMs as Judges: Survey of Evaluation Methods 09/05/2025

The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs 09/05/2025

Limits to scalable evaluation at the frontier: LLM as Judge won’t beat twice the data 09/05/2025

Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation 09/05/2025

Accelerating Unbiased LLM Evaluation via Synthetic Feedback 09/05/2025

Prediction-Powered Statistical Inference Framework 09/05/2025

Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL 09/05/2025

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