Qing Wang

Memory and generalization in neural networks can be disentangled and quantified, revealing that optimal generalization is achievable with minimal memorization through targeted interventions.

Public skill version histories can dramatically enhance task-specific skill evolution, leading to significant performance gains in AI agents.

TIDE-MC achieves unprecedented efficiency in matrix completion, handling billion-scale datasets without running out of memory while delivering up to 11,647x speedup.