Best AI papers explained

437 Episodo

1. General Intelligence Requires Reward-based Pretraining

   Publicado: 25/6/2025
2. Deep Learning is Not So Mysterious or Different

   Publicado: 25/6/2025
3. AI Agents Need Authenticated Delegation

   Publicado: 25/6/2025
4. Probabilistic Modelling is Sufficient for Causal Inference

   Publicado: 25/6/2025
5. Not All Explanations for Deep Learning Phenomena Are Equally Valuable

   Publicado: 25/6/2025
6. e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

   Publicado: 17/6/2025
7. Extrapolation by Association: Length Generalization Transfer in Transformers

   Publicado: 17/6/2025
8. Uncovering Causal Hierarchies in Language Model Capabilities

   Publicado: 17/6/2025
9. Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers

   Publicado: 17/6/2025
10. Improving Treatment Effect Estimation with LLM-Based Data Augmentation

    Publicado: 17/6/2025
11. LLM Numerical Prediction Without Auto-Regression

    Publicado: 17/6/2025
12. Self-Adapting Language Models

    Publicado: 17/6/2025
13. Why in-context learning models are good few-shot learners?

    Publicado: 17/6/2025
14. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina∗

    Publicado: 14/6/2025
15. The Logic of Machines: The AI Reasoning Debate

    Publicado: 12/6/2025
16. Layer by Layer: Uncovering Hidden Representations in Language Models

    Publicado: 12/6/2025
17. Causal Attribution Analysis for Continuous Outcomes

    Publicado: 12/6/2025
18. Training a Generally Curious Agent

    Publicado: 12/6/2025
19. Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q’s

    Publicado: 12/6/2025
20. Strategy Coopetition Explains the Emergence and Transience of In-Context Learning

    Publicado: 12/6/2025