Best AI papers explained

474 Avsnitt

1. Probabilistic Modelling is Sufficient for Causal Inference

   Publicerades: 2025-06-25
2. Not All Explanations for Deep Learning Phenomena Are Equally Valuable

   Publicerades: 2025-06-25
3. e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

   Publicerades: 2025-06-17
4. Extrapolation by Association: Length Generalization Transfer in Transformers

   Publicerades: 2025-06-17
5. Uncovering Causal Hierarchies in Language Model Capabilities

   Publicerades: 2025-06-17
6. Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers

   Publicerades: 2025-06-17
7. Improving Treatment Effect Estimation with LLM-Based Data Augmentation

   Publicerades: 2025-06-17
8. LLM Numerical Prediction Without Auto-Regression

   Publicerades: 2025-06-17
9. Self-Adapting Language Models

   Publicerades: 2025-06-17
10. Why in-context learning models are good few-shot learners?

    Publicerades: 2025-06-17
11. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina∗

    Publicerades: 2025-06-14
12. The Logic of Machines: The AI Reasoning Debate

    Publicerades: 2025-06-12
13. Layer by Layer: Uncovering Hidden Representations in Language Models

    Publicerades: 2025-06-12
14. Causal Attribution Analysis for Continuous Outcomes

    Publicerades: 2025-06-12
15. Training a Generally Curious Agent

    Publicerades: 2025-06-12
16. Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q’s

    Publicerades: 2025-06-12
17. Strategy Coopetition Explains the Emergence and Transience of In-Context Learning

    Publicerades: 2025-06-12
18. Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

    Publicerades: 2025-06-11
19. Agentic Supernet for Multi-agent Architecture Search

    Publicerades: 2025-06-11
20. Sample Complexity and Representation Ability of Test-time Scaling Paradigms

    Publicerades: 2025-06-11