4 papers
When Does Continual Learning Require Learning
Anne Harrington, Nayan Saxena, Michael Murphy +7
As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largely frames this as a problem o…
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…
Don't Think of the White Bear: Ironic Negation in Transformer Models Under Cognitive Load
Logan Mann, Nayan Saxena, Sarah Tandon +3
Negation instructions such as 'do not mention ' can paradoxically increase the accessibility of in human thought, a phenomenon known as ironic rebound. Large language models…
Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals
Sophie Li, Nicholas Huang, Nayan Saxena +4
Large language models (LLMs) improve reasoning accuracy when generating multiple candidate solutions at test time, but standard methods like Best-of-N (BoN) incur high computationa…