Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitigates this by enforcing compatibility during training, existing approaches often require updating the backbone model. Thi...
Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu et al.· 0 citations
This work analyzes the decoding trajectories of LLaDA 2.0 and identifies a recurring diffusion confidence trap, which improves LLaDA 2.0 over confidence-based decoding, leading to more reliable mathematical reasoning.
Zhenhong Sun, Han-Qing Zhao, Yatao Bian et al.· 0 citations
Leveraging capabilities of large language models (LLMs) in text-to-image (T2I) synthesis is an important research direction. In this work we investigate whether the knowledge of a frozen LLM can be effectively utilized in T2I generation when trained exclusively on standard text-image pairs. We integrate a frozen, reaso...
Achin Jain, Jie An, Siddharth Chaudhary et al.· arXiv.org· 0 citations
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