Best AI papers explained
Un pódcast de Enoch H. Kang
550 Episodo
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Provably Learning from Language Feedback
Publicado: 9/7/2025 -
Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners
Publicado: 5/7/2025 -
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation
Publicado: 5/7/2025 -
Causal Abstraction with Lossy Representations
Publicado: 4/7/2025 -
The Winner's Curse in Data-Driven Decisions
Publicado: 4/7/2025 -
Embodied AI Agents: Modeling the World
Publicado: 4/7/2025 -
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Publicado: 4/7/2025 -
What Has a Foundation Model Found? Inductive Bias Reveals World Models
Publicado: 4/7/2025 -
Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond
Publicado: 3/7/2025 -
Learning to Explore: An In-Context Learning Approach for Pure Exploration
Publicado: 3/7/2025 -
Human-AI Matching: The Limits of Algorithmic Search
Publicado: 25/6/2025 -
Uncertainty Quantification Needs Reassessment for Large-language Model Agents
Publicado: 25/6/2025 -
Bayesian Meta-Reasoning for Robust LLM Generalization
Publicado: 25/6/2025 -
General Intelligence Requires Reward-based Pretraining
Publicado: 25/6/2025 -
Deep Learning is Not So Mysterious or Different
Publicado: 25/6/2025 -
AI Agents Need Authenticated Delegation
Publicado: 25/6/2025 -
Probabilistic Modelling is Sufficient for Causal Inference
Publicado: 25/6/2025 -
Not All Explanations for Deep Learning Phenomena Are Equally Valuable
Publicado: 25/6/2025 -
e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
Publicado: 17/6/2025 -
Extrapolation by Association: Length Generalization Transfer in Transformers
Publicado: 17/6/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
