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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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Causal Attribution Analysis for Continuous Outcomes 12/06/2025

Training a Generally Curious Agent 12/06/2025

Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q’s 12/06/2025

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning 12/06/2025

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs 11/06/2025

Agentic Supernet for Multi-agent Architecture Search 11/06/2025

Sample Complexity and Representation Ability of Test-time Scaling Paradigms 11/06/2025

Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators 10/06/2025

LLMs Get Lost In Multi-Turn Conversation 09/06/2025

PromptPex: Automatic Test Generation for Prompts 08/06/2025

General Agents Need World Models 08/06/2025

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models 07/06/2025

Decisions With Algorithms 07/06/2025

Adapting, fast and slow: Causal Approach to Few-Shot Sequence Learning 06/06/2025

Conformal Arbitrage for LLM Objective Balancing 06/06/2025

Simulation-Based Inference for Adaptive Experiments 06/06/2025

Agents as Tool-Use Decision-Makers 06/06/2025

Quantitative Judges for Large Language Models 06/06/2025

Self-Challenging Language Model Agents 06/06/2025

Learning to Explore: An In-Context Learning Approach for Pure Exploration 06/06/2025

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