3 papers
cs.LG2025
Enforcing Orderedness to Improve Feature Consistency
Sophie L. Wang, Alex Quach, Nithin Parsan +1
Sparse autoencoders (SAEs) have been widely used for interpretability of neural networks, but their learned features often vary across seeds and hyperparameter settings. We introdu…
cs.LG2025
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under…
cs.RO2025
Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features
Makram Chahine, Alex Quach, Alaa Maalouf +2
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models often struggle to ge…