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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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Agentic Economic Modeling 03/11/2025

Emergent Introspective Awareness in Large Language Models 03/11/2025

Can Large reasoning models self-train? 01/11/2025

ALITA-G: Self-Evolving Generative Agent for Agent Generation 01/11/2025

Self-improving LLM agents at test-time 30/10/2025

Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization 30/10/2025

Language models are injective and hence invertible 30/10/2025

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory 29/10/2025

RLAD: Training LLMs to Discover Abstractions 29/10/2025

How to Train Your Advisor: Steering Black-Box LLMs with ADVISOR MODELS 29/10/2025

Self-improving LLM agents at Test-Time 27/10/2025

KL-Regularized Reinforcement Learning is designed to Mode Collapse 27/10/2025

How do LLMs use their depth? 27/10/2025

Thought Communication in Multiagent Collaboration 27/10/2025

Reasoning with Sampling: Base Models Outperform RL 26/10/2025

Continual Learning via Sparse Memory Finetuning 26/10/2025

Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences 24/10/2025

The Coverage Principle: How Pre-Training Enables Post-Training 24/10/2025

The Era of Real-World Human Interaction: RL from User Conversations 24/10/2025

Agent Learning via Early Experience 24/10/2025

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