Best AI papers explained
En podcast av Enoch H. Kang
437 Avsnitt
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LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra
Publicerades: 2025-07-28 -
Microsoft's Blueprint: AI, Quantum, and the Agentic Future
Publicerades: 2025-07-26 -
Zuckerberg's AI Vision Analyzed
Publicerades: 2025-07-26 -
Inside Claude: Scaling, Agency, and Interpretability
Publicerades: 2025-07-26 -
Personalized language modeling from personalized human feedback
Publicerades: 2025-07-26 -
Position: Empowering Time Series Reasoning with Multimodal LLMs
Publicerades: 2025-07-25 -
An empirical risk minimization approach for offline inverse RL and Dynamic Discrete Choice models
Publicerades: 2025-07-22 -
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Publicerades: 2025-07-22 -
The Invisible Leash: Why RLVR May Not Escape Its Origin
Publicerades: 2025-07-20 -
Language Model Personalization via Reward Factorization
Publicerades: 2025-07-20 -
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Publicerades: 2025-07-18 -
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Publicerades: 2025-07-17 -
Soft Best-of-n Sampling for Model Alignment
Publicerades: 2025-07-16 -
On Temporal Credit Assignment and Data-Efficient Reinforcement Learning
Publicerades: 2025-07-15 -
Bradley–Terry and Multi-Objective Reward Modeling Are Complementary
Publicerades: 2025-07-15 -
Probing Foundation Models for World Models
Publicerades: 2025-07-15 -
GenAI-Powered Statistical Inference (with Unstructured Data)
Publicerades: 2025-07-14 -
Interpretable Reward Modeling with Active Concept Bottlenecks
Publicerades: 2025-07-14 -
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
Publicerades: 2025-07-14 -
A Collectivist, Economic Perspective on AI
Publicerades: 2025-07-14
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.