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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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GenAI-Powered Statistical Inference (with Unstructured Data) 14/07/2025

Interpretable Reward Modeling with Active Concept Bottlenecks 14/07/2025

PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications 14/07/2025

A Collectivist, Economic Perspective on AI 14/07/2025

Textual Bayes: Quantifying Uncertainty in LLM-Based Systems 12/07/2025

The Winner's Curse in Data-Driven Decisions 11/07/2025

SPIRAL: Self-Play for Reasoning Through Zero-Sum Games 11/07/2025

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence 11/07/2025

Aligning Learning and Endogenous Decision-Making 11/07/2025

Reliable Statistical Inference with Synthetic Data from Large Language Models 11/07/2025

Multi-Turn Reinforcement Learning from Human Preference Feedback 10/07/2025

Provably Learning from Language Feedback 09/07/2025

Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners 05/07/2025

Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation 05/07/2025

Causal Abstraction with Lossy Representations 04/07/2025

The Winner's Curse in Data-Driven Decisions 04/07/2025

Embodied AI Agents: Modeling the World 04/07/2025

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence 04/07/2025

What Has a Foundation Model Found? Inductive Bias Reveals World Models 04/07/2025

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

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