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

Conformal Language Modeling via Posterior Sampling 20/08/2026

BoNVoyage: Learning Better Rewards without Ranking 20/08/2026

Demystifying Agent Skills: Why They Work—Until They Don’t 18/08/2026

Jagged Judges: Epistemic Stability Under Silence, Pressure, and Persistence 15/08/2026

Predicting Neural Scaling Laws without Training: A Data Manifold Oracle 15/08/2026

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing 11/08/2026

Overcoming the Incentive Collapse Paradox 11/08/2026

Position: Modular Memory is the Key to Continual Learning Agents 10/08/2026

Harness RL is Meta-Learning: Training to Self-Improve at Test Time 08/08/2026

Escaping the Nash Trap: Structural Estimation and Alignment of Strategic Reasoning in Large Language Models 07/08/2026

When Does LeJEPA Learn a World Model? 07/08/2026

Do Modules Stay in Their Lane? Role Drift in Compound LLM Systems 03/08/2026

Do you really need to pretrain Q-functions for online RL fine-tuning? 01/08/2026

The Evolution of Digital Search: From Blue Links to Delegated Decision-Making 29/07/2026

Ask, Don’t Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement 28/07/2026

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning 26/07/2026

Understanding Reasoning from Pretraining to Post-Training 24/07/2026

A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior 23/07/2026

Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference 19/07/2026

Rethinking the Evaluation of Harness Evolution for Agents 19/07/2026

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