<ul><li>Researchers have proposed a symbolic generative task description language and inference engine for any-to-any multimodal tasks.</li><li>The framework utilizes a symbolic representation comprising functions, parameters, and topological logic.</li><li>A pre-trained language model is used to map natural language instructions to symbolic workflows without the need for training.</li><li>Experiments demonstrate strong performance, efficiency, editability, and interruptibility of the proposed method for multimodal generative tasks.</li></ul>

Symbolic Representation for Any-to-Any Generative Tasks

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