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#World Models

RISE: Self-Improving Robot Policy with Compositional World Model

Intermediate

Jiazhi Yang, Kunyang Lin et al.Feb 11arXiv

RISE lets a robot learn safely and cheaply by practicing in its imagination instead of always in the real world.

#Reinforcement Learning#World Models#Compositional World Model

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

Intermediate

Shoubin Yu, Yue Zhang et al.Feb 9arXiv

Visual spatial reasoning often fails when a model only looks at one picture and must imagine new viewpoints.

#Adaptive Test-Time Scaling#World Models#Visual Spatial Reasoning

Agentic Reasoning for Large Language Models

Intermediate

Tianxin Wei, Ting-Wei Li et al.Jan 18arXiv

This paper explains how to turn large language models (LLMs) from quiet students that only answer questions into active agents that can plan, act, and learn over time.

#Agentic Reasoning#LLM Agents#In-Context Learning

Can We Predict Before Executing Machine Learning Agents?

Intermediate

Jingsheng Zheng, Jintian Zhang et al.Jan 9arXiv

Machine learning agents usually improve by writing code, running it for hours, and then using the results to tweak the next try, which is very slow.

#World Models#Predict-then-Verify#Data-centric AI

Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future

Intermediate

Tianshuai Hu, Xiaolu Liu et al.Dec 18arXiv

Traditional self-driving used separate boxes for seeing, thinking, and acting, but tiny mistakes in early boxes could snowball into big problems later.

#Vision-Language-Action#End-to-End Autonomous Driving#Dual-System VLA