arXiv:2602. 07739v2 Announce Type: replace-cross Abstract: Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space.
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HyCoSeq is a new framework for learning genomic representations in hyperbolic space. It combines weighted Lorentzian residual aggregation with multi‑curvature Lorentz encoding, enabling full Lorentz representations to contribute directly to local aggregation. A bidirectional LSTM further captures contextual relationships across the sequence, extending local hyperbolic convolutions to sequence‑level representations. Experiments show HyCoSeq surpasses existing hyperbolic baselines and competes with much larger pretrained DNA language models without large‑scale pretraining.
arXiv:2606. 29464v1 Announce Type: cross Abstract: Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs that can efficiently train contrastive vision-language models under strict data and compute budgets.
arXiv:2609.10305v1 Announce Type: new Abstract: Language models under one million parameters matter for edge deployment, domain adaptation, and reproducible research, yet a two-layer LSTM or Transfor...
arXiv:2608.29313v1 Announce Type: cross Abstract: CLIP-like vision-language models (VLMs) trained with contrastive objectives learn strong global image-text representations, but their Euclidean embed...
The paper introduces Hyperbolic Multimodal Continual Learning (HMCL), a method that preserves the Lorentz geometry of hyperbolic multimodal models during sequential updates. By restricting all modalities to a shared hyperbolic isometry, HMCL formulates a joint closest‑admissible (CA) correction—along with a minimal‑rotation (MR) variant—to adjust AdamW updates while maintaining task performance. Experiments on a 16‑task classification‑retrieval stream with three hyperbolic backbones show that HMCL-CA achieves the highest overall score, reduces geometric drift by up to 95.5 %, and improves semantic hierarchy preservation on ImageNet‑WordNet. whyItMatters":"The study demonstrates that explicitly maintaining hyperbolic geometry during continual learning yields superior performance and reduced representation drift compared to existing baselines."
arXiv:2609.24564v1 Announce Type: new Abstract: CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing CLIP's text encode...