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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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Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning 31/12/2025

Activation oracles: training and evaluating llms as general-purpose activation explainers 30/12/2025

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning 29/12/2025

Joint-Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction 29/12/2025

Monitoring Monitorability/ OpenAI 28/12/2025

Detailed Balance in Large Language Model-Driven Agents 28/12/2025

Learning to reason in LLMs by expectation maximization 28/12/2025

Exploratory Causal Inference in SAEnce 25/12/2025

Detailed balance in large language model-driven agents 24/12/2025

The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding 24/12/2025

Adaptation of Agentic AI 23/12/2025

Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning 22/12/2025

Let’s (not) just put things in Context: Test-Time Training for Long-Context LLMs 21/12/2025

TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models 20/12/2025

What’s In My Human Feedback? Learning Interpretable Descriptions of Preference Data 19/12/2025

Bolmo: Byteifying the Next Generation of Language Models 19/12/2025

What happened with sparse autoencoders? 17/12/2025

What Matters Right Now in Mechanistic Interpretability 16/12/2025

CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning 16/12/2025

Self-Improving AI and Human Co-Improvement for Safer Co-Superintelligence 16/12/2025

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