Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
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A reference-based geometric hashing method recovers cross-model vector correspondences by exploiting local isometric consistency in contrastive embeddings and iteratively bootstrapping from a seed of paired anchors.
LFM models exhibit stability to data reduction and capacity shrinkage that is tied to the flow matching objective, enabling reduced-data training and coarse-to-fine inference with over 2x speedup.
citing papers explorer
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The price of incrementality in k-center clustering
Incremental k-center clustering admits no better than 2-approximation even for non-polynomial algorithms, via a new lower-bound construction.
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Vector Linking via Cross-Model Local Isometric Consistency
A reference-based geometric hashing method recovers cross-model vector correspondences by exploiting local isometric consistency in contrastive embeddings and iteratively bootstrapping from a seed of paired anchors.
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Exploring and Exploiting Stability in Latent Flow Matching
LFM models exhibit stability to data reduction and capacity shrinkage that is tied to the flow matching objective, enabling reduced-data training and coarse-to-fine inference with over 2x speedup.