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README.md

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 # LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
 
-This model implements **LexSemBridge**, a unified framework that enhances dense query representations through fine-grained, input-aware vector modulation. LexSemBridge constructs latent enhancement vectors from input tokens using statistical, learned, and contextual paradigms, integrating them with dense embeddings via element-wise interaction. It operates as a plug-in without modifying the backbone encoder and naturally extends to both text and vision modalities, aiming to improve performance on fine-grained retrieval tasks where precise keyword alignment and span-level localization are crucial.
+This model implements **LexSemBridge**, a unified framework that enhances dense query representations through fine-grained, input-aware vector modulation. LexSemBridge constructs latent enhancement vectors from input tokens using statistical, learned, and contextual paradigms, integrating them with dense embeddings via element-wise interaction. It naturally extends to both text and vision modalities with an appropriate tokenization, aiming to improve performance on fine-grained retrieval tasks where precise keyword alignment and span-level localization are crucial.
 
 The model is based on the paper [LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation](https://huggingface.co/papers/2508.17858).