Best AI papers explained
A podcast by Enoch H. Kang
550 Episodes
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PREFDISCO: Evaluating Proactive Personalization through Interactive Preference Discovery
Published: 11/12/2025 -
Reusing pre-training data at test time is a compute multiplier
Published: 11/10/2025 -
Scaling Agent Learning via Experience Synthesis
Published: 11/9/2025 -
Continuous Autoregressive Language Models
Published: 11/8/2025 -
Toward a Theory of Agents as Tool-Use Decision-Makers
Published: 11/7/2025 -
Nested Learning: The Illusion of Deep Learning Architectures
Published: 11/5/2025 -
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Published: 11/5/2025 -
Beyond a million tokens: benchmarking and enhancing long-term memory in llms
Published: 11/4/2025 -
Agentic Economic Modeling
Published: 11/3/2025 -
Emergent Introspective Awareness in Large Language Models
Published: 11/3/2025 -
Can Large reasoning models self-train?
Published: 11/1/2025 -
ALITA-G: Self-Evolving Generative Agent for Agent Generation
Published: 11/1/2025 -
Self-improving LLM agents at test-time
Published: 10/30/2025 -
Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
Published: 10/30/2025 -
Language models are injective and hence invertible
Published: 10/30/2025 -
ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
Published: 10/29/2025 -
RLAD: Training LLMs to Discover Abstractions
Published: 10/29/2025 -
How to Train Your Advisor: Steering Black-Box LLMs with ADVISOR MODELS
Published: 10/29/2025 -
Self-improving LLM agents at Test-Time
Published: 10/27/2025 -
KL-Regularized Reinforcement Learning is designed to Mode Collapse
Published: 10/27/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
