Best AI papers explained
A podcast by Enoch H. Kang
431 Episodes
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Revisiting Superficial Alignment Hypothesis
Published: 3/14/2025 -
Diagnostic uncertainty: teaching language Models to describe open-ended uncertainty
Published: 3/14/2025 -
Language Model Personalization via Reward Factorization
Published: 3/14/2025 -
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration
Published: 3/14/2025 -
How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
Published: 3/14/2025 -
Can Large Language Models Extract Customer Needs as well as Professional Analysts?
Published: 3/13/2025 -
Spurlens: finding spurious correlations in Multimodal llms
Published: 3/13/2025 -
Improving test-time search with backtrack- Ing Improving test-time search with backtrack- Ing against in-context value verifiersagainst in-context value verifiers
Published: 3/13/2025 -
Adaptive elicitation of latent information Using natural language
Published: 3/13/2025 -
Document Valuation in LLM Summaries: A Cluster Shapley Approach
Published: 3/13/2025 -
s1: simple test time scaling
Published: 3/13/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.