sparse-features

The paper introduces MASS, a hierarchical data selection framework for LLM post-training that uses manifold and sparse feature coverage to select high-value data subsets, outperforming existing baselines in experiments.

Xetrieval is a mechanistic framework that explains dense retrieval by enhancing sentence embeddings with reasoning information and decomposing them into interpretable sparse features, providing feature-level explanations for retrieval decisions without expensive autoregressive generation.