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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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LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization 22/11/2025

Black-Box On-Policy Distillation of Large Language Models 20/11/2025

Solving a million step LLM task with zero errors 20/11/2025

Not All Thoughts Matter: Selective Attention for Efficient Reasoning 19/11/2025

Sample-Efficient Parametric Learning from Natural Language 19/11/2025

Bayesian Optimization in Language space: An Eval-Efficient AI Self-Improvement Framework 18/11/2025

Context Engineering: Sessions, Memory 16/11/2025

The Era of Agentic Organization: Learning to Organize with Language Models 15/11/2025

Understanding neural networks through sparse circuits 14/11/2025

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning 14/11/2025

Multi-Agent Evolve: LLM Self-Improvement Through Co-Evolution 14/11/2025

LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics 14/11/2025

PREFDISCO: Evaluating Proactive Personalization through Interactive Preference Discovery 12/11/2025

Reusing pre-training data at test time is a compute multiplier 10/11/2025

Scaling Agent Learning via Experience Synthesis 09/11/2025

Continuous Autoregressive Language Models 08/11/2025

Toward a Theory of Agents as Tool-Use Decision-Makers 07/11/2025

Nested Learning: The Illusion of Deep Learning Architectures 05/11/2025

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding 05/11/2025

Beyond a million tokens: benchmarking and enhancing long-term memory in llms 04/11/2025

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