Best AI papers explained
A podcast by Enoch H. Kang
515 Episoade
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Zuckerberg's AI Vision Analyzed
Publicat: 26.07.2025 -
Inside Claude: Scaling, Agency, and Interpretability
Publicat: 26.07.2025 -
Personalized language modeling from personalized human feedback
Publicat: 26.07.2025 -
Position: Empowering Time Series Reasoning with Multimodal LLMs
Publicat: 25.07.2025 -
An empirical risk minimization approach for offline inverse RL and Dynamic Discrete Choice models
Publicat: 22.07.2025 -
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Publicat: 22.07.2025 -
The Invisible Leash: Why RLVR May Not Escape Its Origin
Publicat: 20.07.2025 -
Language Model Personalization via Reward Factorization
Publicat: 20.07.2025 -
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Publicat: 18.07.2025 -
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
Publicat: 17.07.2025 -
Soft Best-of-n Sampling for Model Alignment
Publicat: 16.07.2025 -
On Temporal Credit Assignment and Data-Efficient Reinforcement Learning
Publicat: 15.07.2025 -
Bradley–Terry and Multi-Objective Reward Modeling Are Complementary
Publicat: 15.07.2025 -
Probing Foundation Models for World Models
Publicat: 15.07.2025 -
GenAI-Powered Statistical Inference (with Unstructured Data)
Publicat: 14.07.2025 -
Interpretable Reward Modeling with Active Concept Bottlenecks
Publicat: 14.07.2025 -
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
Publicat: 14.07.2025 -
A Collectivist, Economic Perspective on AI
Publicat: 14.07.2025 -
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
Publicat: 12.07.2025 -
The Winner's Curse in Data-Driven Decisions
Publicat: 11.07.2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
