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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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How Bidirectionality Helps Language Models Learn Better via Dynamic Bottleneck Estimation 06/06/2025

A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models 05/06/2025

Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling 05/06/2025

Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models 05/06/2025

IPO: Interpretable Prompt Optimization for Vision-Language Models 05/06/2025

Evolutionary Prompt Optimization discovers emergent multimodal reasoning strategies 05/06/2025

Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? 04/06/2025

Diffusion Guidance Is a Controllable Policy Improvement Operator 02/06/2025

Alita: Generalist Agent With Self-Evolution 02/06/2025

A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning 02/06/2025

Learning Compositional Functions with Transformers from Easy-to-Hard Data 02/06/2025

Preference Learning with Response Time 02/06/2025

Accelerating RL for LLM Reasoning with Optimal Advantage Regression 31/05/2025

Algorithms for reliable decision-making need causal reasoning 31/05/2025

Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality 31/05/2025

Distances for Markov chains from sample streams 31/05/2025

When and Why LLMs Fail to Reason Globally 31/05/2025

IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis 31/05/2025

No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference 31/05/2025

Accelerating RL for LLM Reasoning with Optimal Advantage Regression 31/05/2025

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