Best AI papers explained
Un pódcast de Enoch H. Kang
550 Episodo
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PLAN-AND-ACT: LLM Agent Planning with Synthetic Data
Publicado: 8/4/2025 -
SEARCH-R1: LLMs Learn to Reason and Search via Reinforcement Learning
Publicado: 8/4/2025 -
The Theory of the Firm: Information, Incentives, and Organization
Publicado: 8/4/2025 -
Four Formalizable Theories of the Firm
Publicado: 8/4/2025 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Publicado: 6/4/2025 -
CodeTool: Process Supervision for Enhanced LLM Tool Invocation
Publicado: 6/4/2025 -
Evaluating LLM Agents in Multi-Turn Conversations: A Survey
Publicado: 6/4/2025 -
Epistemic Alignment in User-LLM Knowledge Delivery
Publicado: 6/4/2025 -
MCP is (not) all you need
Publicado: 6/4/2025 -
AI, Human Skills, and Competitive Advantage in Chess
Publicado: 5/4/2025 -
Inference-Time Scaling for Generalist Reward Modeling
Publicado: 4/4/2025 -
Optimal Pure Exploration in Linear Bandits via Sampling
Publicado: 4/4/2025 -
Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products
Publicado: 4/4/2025 -
Emergent Symbolic Mechanisms for Reasoning in Large Language Models
Publicado: 3/4/2025 -
Inference-Time Alignment: Coverage, Scaling, and Optimality
Publicado: 3/4/2025 -
Sharpe Ratio-Guided Active Learning for Preference Optimization
Publicado: 3/4/2025 -
Active Learning for Adaptive In-Context Prompt Design
Publicado: 3/4/2025 -
Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
Publicado: 3/4/2025 -
On the Biology of a Large Language Model
Publicado: 1/4/2025 -
Async-TB: Asynchronous Trajectory Balance for Scalable LLM RL
Publicado: 1/4/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
