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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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Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond 03/07/2025

Learning to Explore: An In-Context Learning Approach for Pure Exploration 03/07/2025

Human-AI Matching: The Limits of Algorithmic Search 25/06/2025

Uncertainty Quantification Needs Reassessment for Large-language Model Agents 25/06/2025

Bayesian Meta-Reasoning for Robust LLM Generalization 25/06/2025

General Intelligence Requires Reward-based Pretraining 25/06/2025

Deep Learning is Not So Mysterious or Different 25/06/2025

AI Agents Need Authenticated Delegation 25/06/2025

Probabilistic Modelling is Sufficient for Causal Inference 25/06/2025

Not All Explanations for Deep Learning Phenomena Are Equally Valuable 25/06/2025

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs 17/06/2025

Extrapolation by Association: Length Generalization Transfer in Transformers 17/06/2025

Uncovering Causal Hierarchies in Language Model Capabilities 17/06/2025

Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers 17/06/2025

Improving Treatment Effect Estimation with LLM-Based Data Augmentation 17/06/2025

LLM Numerical Prediction Without Auto-Regression 17/06/2025

Why in-context learning models are good few-shot learners? 17/06/2025

Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina∗ 14/06/2025

The Logic of Machines: The AI Reasoning Debate 12/06/2025

Layer by Layer: Uncovering Hidden Representations in Language Models 12/06/2025

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