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FLAT: Resampling Image and Text into 1D Flexible-Length Aligned Transmodal Tokens for Retrieval and Generation

FLAT is a pre-training framework that jointly optimizes a shared multimodal encoder with text-to-image and image-to-text decoders, enabling flexible-length aligned transmodal representations for retrieval and generation. It maps visual and textual inputs into a unified 1D sequence space and demonstrates strong performance on various tasks.

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PublishedSeptember 15, 2026Guangyu Sun, Shlok Kumar Mishra, Wentao Bao
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FLAT enables flexible-length cross-modal generation and retrieval by aligning visual and textual representations in a unified 1D sequence space, supporting tasks like linear interpolation and zero-shot composed retrieval.

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