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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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DINOv3: Vision Models for Self-Supervised Learning 15/08/2025

Agent Lightning: Training Any AI Agents with Reinforcement Learning 14/08/2025

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier 14/08/2025

From Model Weights to Agent Workflows: Charting the New Frontier of Optimization in Large Language Models 12/08/2025

Is Chain-of-Thought Reasoning a Mirage? 12/08/2025

Agentic Web: Weaving the Next Web with AI Agents 11/08/2025

The Assimilation-Accommodation Gap in LLM Intelligence 10/08/2025

The Minimalist AI Kernel: A New Frontier in Reasoning 06/08/2025

Statistical Rigor for Interpretable AI 06/08/2025

Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value 04/08/2025

A foundation model to predict and capture human cognition 04/08/2025

Generative Recommendation with Semantic IDs: A Practitioner’s Handbook 04/08/2025

Hierarchical Reasoning Model 04/08/2025

Test-time Offline Reinforcement Learning on Goal-related Experience 04/08/2025

Interpreting Chain of Thought: A Walkthrough and Discussion 04/08/2025

The wall confronting large language models 04/08/2025

COLLABLLM: LLMs From Passive to Collaborative 31/07/2025

A decade's battle on dataset bias: are we there yet? 29/07/2025

GEPA: Generative Feedback for AI System Optimization 29/07/2025

From AI-Curious to AI-First: Engineering Production AI Systems 28/07/2025

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