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This paper proposes COM, a method that enforces continuity and ordinality constraints on time series token embeddings to improve the performance of token-based time series large language models.
SAMOSA adapts SAM 2 for visual object tracking by incorporating motion prediction, semantic detection, and geometric constraints to improve robustness and generalization in complex scenarios with distractors, occlusion, and nonlinear motion.
GeoStack introduces a geometric framework to compose independently trained domain experts in Vision-Language Models without catastrophic forgetting, achieving constant-time inference and a 10x reduction in geometric error.