Best AI papers explained
En podcast af Enoch H. Kang
512 Episoder
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Learning-to-measure: in-context active feature acquisition
Udgivet: 19.10.2025 -
Andrej Karpathy's insights: AGI, Intelligence, and Evolution
Udgivet: 19.10.2025 -
Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data
Udgivet: 18.10.2025 -
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Udgivet: 18.10.2025 -
The attacker moves second: stronger adaptive attacks bypass defenses against LLM jail- Breaks and prompt injections
Udgivet: 18.10.2025 -
When can in-context learning generalize out of task distribution?
Udgivet: 16.10.2025 -
The Art of Scaling Reinforcement Learning Compute for LLMs
Udgivet: 16.10.2025 -
A small number of samples can poison LLMs of any size
Udgivet: 16.10.2025 -
Dual Goal Representations
Udgivet: 14.10.2025 -
Welcome to the Era of Experience
Udgivet: 14.10.2025 -
Value Flows: Flow-Based Distributional Reinforcement Learning
Udgivet: 14.10.2025 -
Self-Adapting Language Models
Udgivet: 12.10.2025 -
The Markovian Thinker
Udgivet: 12.10.2025 -
Moloch’s Bargain: emergent misalignment when LLMs compete for audiences
Udgivet: 12.10.2025 -
Transformer Predictor Dynamics and Task Diversity
Udgivet: 11.10.2025 -
Base models know how to reason, thinking models learn when
Udgivet: 11.10.2025 -
Spectrum tuning: Post-training for distributional coverage and in-context steerability
Udgivet: 11.10.2025 -
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
Udgivet: 11.10.2025 -
MLPs Learn In-Context on Regression and Classification tasks
Udgivet: 11.10.2025 -
Is Pre-Training Truly Better than Meta-Learning?
Udgivet: 11.10.2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
