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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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Sutton and Silver: The Era of Experience: Learning Beyond Human Data 19/04/2025

Sample, Don't Search: Rethinking Test-Time Alignment for Language Models 19/04/2025

AI Agents: Echoes of Past Technology Pivots? 19/04/2025

Minimalist LLM Reasoning: Rejection Sampling to Reinforcement 19/04/2025

Securing the Model Context Protocol in Enterprise Environments 19/04/2025

Improving Multi-Turn Tool Use with Reinforcement Learning 19/04/2025

Cultural Knowledge Conservation and Control in Large Language Models 19/04/2025

Data Quality, Repetition, and Scaling of Language Models 18/04/2025

Compute-Optimal Scaling Laws for Language Models Revisited 18/04/2025

Concise Reasoning via Reinforcement Learning 18/04/2025

Throughput Limits for LLM Inference and AI Agent Scheduling 14/04/2025

RL Post-training Amplifies Pretraining Behaviors in Language Models 14/04/2025

Fast Adaptation of Behavioral Foundation Models 14/04/2025

Proprietary Reward Models: Sustaining Advantage in Agentic AI 13/04/2025

Why Multi-Agent LLM Systems Fail: A Comprehensive Study 12/04/2025

Play2Prompt: Zero-Shot Tool Instruction Optimization via Tool Play 12/04/2025

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems 12/04/2025

API and GUI Agents: Divergence, Convergence, and Hybrid Approaches 12/04/2025

AI, Chess, and Competitive Advantage: Substitution and Complementation 12/04/2025

Knowledge of the Firm and Replication of Technology 12/04/2025

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