Best AI papers explained
Un pódcast de Enoch H. Kang
526 Episodo
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Evaluating Modern Recommender Systems: Challenges and Future Directions
Publicado: 22/4/2025 -
AI in the Enterprise: Seven Lessons from Frontier Companies by OpenAI
Publicado: 22/4/2025 -
Discussion: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Publicado: 21/4/2025 -
AI Agent Protocols and Human Preference
Publicado: 21/4/2025 -
Cross-Environment Cooperation for Zero-Shot Multi-Agent Coordination
Publicado: 20/4/2025 -
Sutton and Silver: The Era of Experience: Learning Beyond Human Data
Publicado: 19/4/2025 -
Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Publicado: 19/4/2025 -
AI Agents: Echoes of Past Technology Pivots?
Publicado: 19/4/2025 -
Minimalist LLM Reasoning: Rejection Sampling to Reinforcement
Publicado: 19/4/2025 -
Securing the Model Context Protocol in Enterprise Environments
Publicado: 19/4/2025 -
Improving Multi-Turn Tool Use with Reinforcement Learning
Publicado: 19/4/2025 -
Cultural Knowledge Conservation and Control in Large Language Models
Publicado: 19/4/2025 -
Data Quality, Repetition, and Scaling of Language Models
Publicado: 18/4/2025 -
Compute-Optimal Scaling Laws for Language Models Revisited
Publicado: 18/4/2025 -
Concise Reasoning via Reinforcement Learning
Publicado: 18/4/2025 -
Throughput Limits for LLM Inference and AI Agent Scheduling
Publicado: 14/4/2025 -
RL Post-training Amplifies Pretraining Behaviors in Language Models
Publicado: 14/4/2025 -
Fast Adaptation of Behavioral Foundation Models
Publicado: 14/4/2025 -
Proprietary Reward Models: Sustaining Advantage in Agentic AI
Publicado: 13/4/2025 -
Why Multi-Agent LLM Systems Fail: A Comprehensive Study
Publicado: 12/4/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
