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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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E-GEO: A Testbed for Generative Engine Optimization in E-Commerce 04/12/2025

1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities 04/12/2025

Treatment Effect Estimation for Optimal Decision-Making 04/12/2025

Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems 03/12/2025

Debugging misaligned completions with sparse-autoencoder latent attribution 02/12/2025

Building Effective AI Agents \ Anthropic 02/12/2025

How to Correctly Report LLM-as-a-Judge Evaluations 02/12/2025

In-Context Learning with Hypothesis-Class Guidance 02/12/2025

Selecting Belief-State Approximations in Simulators with Latent States 01/12/2025

Latent Collaboration in Multi-Agent Systems 29/11/2025

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning 28/11/2025

DELTA: How Does RL Unlock and Transfer New Algorithms in LLMs? 28/11/2025

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing 27/11/2025

Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs 27/11/2025

Ilya Sutskever – We're moving from the age of scaling to the age of research 26/11/2025

Cognitive Foundations for Reasoning and Their Manifestation in LLMs 26/11/2025

Natural emergent misalignment from reward hacking in production RL 25/11/2025

Evolution Strategies at the Hyperscale 25/11/2025

The Path Not Taken: RLVR Provably Learns Off the Principals 23/11/2025

Back to Basics: Let Denoising Generative Models Denoise 23/11/2025

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