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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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Large Language Models as Markov Chains 28/05/2025

Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation 28/05/2025

Selective induction heads: how transformers select causal structures in context 28/05/2025

The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains 28/05/2025

How Transformers Learn Causal Structure with Gradient Descent 28/05/2025

Planning anything with rigor: general-purpose zero-shot planning with llm-based formalized programming 28/05/2025

Automated Design of Agentic Systems 28/05/2025

What’s the Magic Word? A Control Theory of LLM Prompting 28/05/2025

BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling 27/05/2025

RL with KL penalties is better viewed as Bayesian inference 27/05/2025

Asymptotics of Language Model Alignment 27/05/2025

Qwen 2.5, RL, and Random Rewards 27/05/2025

Theoretical guarantees on the best-of-n alignment policy 27/05/2025

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models 27/05/2025

Improved Techniques for Training Score-Based Generative Models 27/05/2025

Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator 27/05/2025

AlphaEvolve: A coding agent for scientific and algorithmic discovery 27/05/2025

Harnessing the Universal Geometry of Embeddings 27/05/2025

Goal Inference using Reward-Producing Programs in a Novel Physics Environment 27/05/2025

Trial-Error-Explain In-Context Learning for Personalized Text Generation 27/05/2025

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