Search papers, labs, and topics across Lattice.
3
0
6
0
A learned shortlist can make private dense retrieval both accurate and practical, achieving near-full-corpus quality with a fraction of the computational cost.
The form of answer labels, not just their quantity, fundamentally shapes what LLMs learn during fine-tuning, revealing a surprising causal relationship that could redefine training strategies.
Lagrange achieves robust open-world driving by transforming decision-making into a Lagrangian action minimization problem, ensuring both interpretability and compliance with vehicle kinematics.
