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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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Context Engineering: Beyond Simple Prompting to LLM Architecture 28/07/2025

Agentic Misalignment: LLMs as Insider Threats 28/07/2025

Small Language Models: Future of Agentic AI 28/07/2025

Learning without training: The implicit dynamics of in-context learning 28/07/2025

Inverse Scaling in Test-Time Compute 28/07/2025

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra 28/07/2025

Microsoft's Blueprint: AI, Quantum, and the Agentic Future 26/07/2025

Zuckerberg's AI Vision Analyzed 26/07/2025

Inside Claude: Scaling, Agency, and Interpretability 26/07/2025

Personalized language modeling from personalized human feedback 26/07/2025

Position: Empowering Time Series Reasoning with Multimodal LLMs 25/07/2025

An empirical risk minimization approach for offline inverse RL and Dynamic Discrete Choice models 22/07/2025

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities 22/07/2025

The Invisible Leash: Why RLVR May Not Escape Its Origin 20/07/2025

Language Model Personalization via Reward Factorization 20/07/2025

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions 18/07/2025

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective 17/07/2025

Soft Best-of-n Sampling for Model Alignment 16/07/2025

On Temporal Credit Assignment and Data-Efficient Reinforcement Learning 15/07/2025

Bradley–Terry and Multi-Objective Reward Modeling Are Complementary 15/07/2025

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