Best AI papers explained

436 Episodes

1. Microsoft's Blueprint: AI, Quantum, and the Agentic Future

   Published: 7/26/2025
2. Zuckerberg's AI Vision Analyzed

   Published: 7/26/2025
3. Inside Claude: Scaling, Agency, and Interpretability

   Published: 7/26/2025
4. Personalized language modeling from personalized human feedback

   Published: 7/26/2025
5. Position: Empowering Time Series Reasoning with Multimodal LLMs

   Published: 7/25/2025
6. An empirical risk minimization approach for offline inverse RL and Dynamic Discrete Choice models

   Published: 7/22/2025
7. Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

   Published: 7/22/2025
8. The Invisible Leash: Why RLVR May Not Escape Its Origin

   Published: 7/20/2025
9. Language Model Personalization via Reward Factorization

   Published: 7/20/2025
10. Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions

    Published: 7/18/2025
11. Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective

    Published: 7/17/2025
12. Soft Best-of-n Sampling for Model Alignment

    Published: 7/16/2025
13. On Temporal Credit Assignment and Data-Efficient Reinforcement Learning

    Published: 7/15/2025
14. Bradley–Terry and Multi-Objective Reward Modeling Are Complementary

    Published: 7/15/2025
15. Probing Foundation Models for World Models

    Published: 7/15/2025
16. GenAI-Powered Statistical Inference (with Unstructured Data)

    Published: 7/14/2025
17. Interpretable Reward Modeling with Active Concept Bottlenecks

    Published: 7/14/2025
18. PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications

    Published: 7/14/2025
19. A Collectivist, Economic Perspective on AI

    Published: 7/14/2025
20. Textual Bayes: Quantifying Uncertainty in LLM-Based Systems

    Published: 7/12/2025