Best AI papers explained

A podcast by Enoch H. Kang

442 Episodes

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Published: 5/24/2025
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Published: 5/24/2025
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Published: 5/24/2025
Automated Social Science: A Structural Causal Model-Based Approach
Published: 5/24/2025
Causal Interpretation of Transformer Self-Attention
Published: 5/24/2025
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Published: 5/24/2025
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Published: 5/24/2025
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Published: 5/24/2025
Prompts from Reinforcement Learning (PRL)
Published: 5/24/2025
Logits are All We Need to Adapt Closed Models
Published: 5/24/2025
Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Published: 5/23/2025
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Published: 5/23/2025
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Published: 5/23/2025
LLM In-Context Learning as Kernel Regression
Published: 5/23/2025
Personalizing LLMs via Decode-Time Human Preference Optimization
Published: 5/23/2025
Almost Surely Safe LLM Inference-Time Alignment
Published: 5/23/2025
Survey of In-Context Learning Interpretation and Analysis
Published: 5/23/2025
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Published: 5/23/2025
LLM In-Context Learning as Kernel Regression
Published: 5/23/2025
Where does In-context Learning Happen in Large Language Models?
Published: 5/23/2025

11 / 23

Cut through the noise. We curate and break down the most important AI papers so you don’t have to.