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Best AI papers explained

Por: Enoch H. Kang

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

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Latest episodes of the podcast Best AI papers explained

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Causality-Aware Alignment for Large Language Model Debiasing 29/04/2025

Reward Models Evaluate Consistency, Not Causality 28/04/2025

Causal Rewards for Large Language Model Alignment 28/04/2025

Sycophancy to subterfuge: Investigating reward-tampering in large language models 28/04/2025

Bidirectional AI Alignment 28/04/2025

Why Do Multi-Agent LLM Systems Fail? 27/04/2025

LLMs as Greedy Agents: RL Fine-tuning for Decision-Making 27/04/2025

LLM Feedback Loops and the Lock-in Hypothesis 27/04/2025

Representational Alignment Drives Effective Teaching and Learning 27/04/2025

Adaptive Parallel Reasoning with Language Models 27/04/2025

AI: Rewiring the Flow of Ideas and Human Knowledge 27/04/2025

Learning and Equilibrium with Ranking Feedback 27/04/2025

Designing Human-AI Collaboration: A Sufficient-Statistic Approach 27/04/2025

GOAT: Generative Adversarial Training for Human-AI Coordination 27/04/2025

π0.5: Generalization in Robotic Manipulation via Diverse Data 27/04/2025

NoWag: Unified Compression for Large Language Models 26/04/2025

Optimal Tool Calls in Language Model Reasoning 26/04/2025

Data Selection for Empirical Risk Minimization 26/04/2025

LoRe: Low-Rank Reward Modeling for Personalized LLMs 26/04/2025

ParaPO: Reducing Language Model Verbatim Reproduction 26/04/2025

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