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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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Tina: Tiny LoRA Reasoning Models 25/04/2025

Evaluating large language models in theory of mind tasks 25/04/2025

QUEST: Quality Sampling for Machine Translation 24/04/2025

Offline Preference Learning via Simulated Trajectory Feedback 24/04/2025

Reasoning Elicitation in Language Models via Counterfactual Feedback 24/04/2025

Eliciting Human Preferences with Language Models 24/04/2025

Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning 24/04/2025

γ-Bench: Evaluating LLMs in Multi-Agent Games 24/04/2025

DRAFT: Self-Driven LLM Tool Mastery via Documentation Refinement 24/04/2025

Optimal Prediction Sets for Enhanced Human-AI Accuracy 24/04/2025

Self-Correction via Reinforcement Learning for Language Models 24/04/2025

Tractable Multi-Agent Reinforcement Learning through Behavioral Economics 24/04/2025

Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement 24/04/2025

Iterative Nash Policy Optimization for Language Model Alignment 24/04/2025

SycEval: Benchmarking LLM Sycophancy in Mathematics and Medicine 23/04/2025

Stack AI: Democratizing Enterprise AI Development 22/04/2025

Evaluating Modern Recommender Systems: Challenges and Future Directions 22/04/2025

AI in the Enterprise: Seven Lessons from Frontier Companies by OpenAI 22/04/2025

Discussion: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? 21/04/2025

AI Agent Protocols and Human Preference 21/04/2025

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