1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander
Nikolai Smolyanskiy
We study how to predict the downstream closed-loop performance of a learned latent world model from validation-time diagnostics alone. Choosing the right checkpoint from a world-mo…
cs.RO2026★ 1 cited
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Alexander Popov, Alperen Degirmenci, David Wehr +9
We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an…
cs.RO2026
Planning-aligned Token Compression for Long-Context Autonomous Driving
Zhixuan Liang, Yuxiao Chen, Yurong You +12
Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computationa…