Best AI papers explained
En podkast av Enoch H. Kang
550 Episoder
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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
Publisert: 27.11.2025 -
Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
Publisert: 27.11.2025 -
Ilya Sutskever – We're moving from the age of scaling to the age of research
Publisert: 26.11.2025 -
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Publisert: 26.11.2025 -
Natural emergent misalignment from reward hacking in production RL
Publisert: 25.11.2025 -
Evolution Strategies at the Hyperscale
Publisert: 25.11.2025 -
The Path Not Taken: RLVR Provably Learns Off the Principals
Publisert: 23.11.2025 -
Back to Basics: Let Denoising Generative Models Denoise
Publisert: 23.11.2025 -
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
Publisert: 22.11.2025 -
Black-Box On-Policy Distillation of Large Language Models
Publisert: 20.11.2025 -
Solving a million step LLM task with zero errors
Publisert: 20.11.2025 -
Not All Thoughts Matter: Selective Attention for Efficient Reasoning
Publisert: 19.11.2025 -
Sample-Efficient Parametric Learning from Natural Language
Publisert: 19.11.2025 -
Bayesian Optimization in Language space: An Eval-Efficient AI Self-Improvement Framework
Publisert: 18.11.2025 -
Context Engineering: Sessions, Memory
Publisert: 16.11.2025 -
The Era of Agentic Organization: Learning to Organize with Language Models
Publisert: 15.11.2025 -
Understanding neural networks through sparse circuits
Publisert: 14.11.2025 -
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
Publisert: 14.11.2025 -
Multi-Agent Evolve: LLM Self-Improvement Through Co-Evolution
Publisert: 14.11.2025 -
LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
Publisert: 14.11.2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
