Symbolic Representation for Any-to-Any Generative Tasks

• URL: http://arxiv.org/abs/2504.17261v1
• Date: Thu, 24 Apr 2025 05:35:47 GMT
• Title: Symbolic Representation for Any-to-Any Generative Tasks
• Authors: Jiaqi Chen, Xiaoye Zhu, Yue Wang, Tianyang Liu, Xinhui Chen, Ying Chen, Chak Tou Leong, Yifei Ke, Joseph Liu, Yiwen Yuan, Julian McAuley, Li-jia Li,
• Abstract summary: We propose a symbolic generative task description language and an inference engine capable of representing arbitrary multimodal tasks as structured symbolic flows.<n>Our framework successfully performs over 12 diverse multimodal generative tasks, demonstrating strong performance and flexibility without the need for task-specific tuning.<n>Experiments show that our method not only matches or outperforms existing state-of-the-art unified models in content quality, but also offers greater efficiency, editability, and interruptibility.
• Score: 25.808462395329194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a symbolic generative task description language and a corresponding inference engine capable of representing arbitrary multimodal tasks as structured symbolic flows. Unlike conventional generative models that rely on large-scale training and implicit neural representations to learn cross-modal mappings, often at high computational cost and with limited flexibility, our framework introduces an explicit symbolic representation comprising three core primitives: functions, parameters, and topological logic. Leveraging a pre-trained language model, our inference engine maps natural language instructions directly to symbolic workflows in a training-free manner. Our framework successfully performs over 12 diverse multimodal generative tasks, demonstrating strong performance and flexibility without the need for task-specific tuning. Experiments show that our method not only matches or outperforms existing state-of-the-art unified models in content quality, but also offers greater efficiency, editability, and interruptibility. We believe that symbolic task representations provide a cost-effective and extensible foundation for advancing the capabilities of generative AI.

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