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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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Reexamining the Aleatoric and Epistemic Uncertainty Dichotomy 08/05/2025

Decoding Claude Code: Terminal Agent for Developers 07/05/2025

Emergent Strategic AI Equilibrium from Pre-trained Reasoning 07/05/2025

Benefiting from Proprietary Data with Siloed Training 06/05/2025

Advantage Alignment Algorithms 06/05/2025

Asymptotic Safety Guarantees Based On Scalable Oversight 06/05/2025

What Makes a Reward Model a Good Teacher? An Optimization Perspective 06/05/2025

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems 06/05/2025

Identifiable Steering via Sparse Autoencoding of Multi-Concept Shifts 06/05/2025

You Are What You Eat - AI Alignment Requires Understanding How Data Shapes Structure and Generalisation 06/05/2025

Interplay of LLMs in Information Retrieval Evaluation 03/05/2025

Trade-Offs Between Tasks Induced by Capacity Constraints Bound the Scope of Intelligence 03/05/2025

Toward Efficient Exploration by Large Language Model Agents 03/05/2025

Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT 02/05/2025

Self-Consuming Generative Models with Curated Data 02/05/2025

Bootstrapping Language Models with DPO Implicit Rewards 02/05/2025

DeepSeek-Prover-V2: Advancing Formal Reasoning 01/05/2025

THINKPRM: Data-Efficient Process Reward Models 01/05/2025

Societal Frameworks and LLM Alignment 29/04/2025

Risks from Multi-Agent Advanced AI 29/04/2025

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