2025/04/24 by Jiaqi Chen, Chen, Jiaqi, Zhu, Xiaoye +19
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Flexibility (engineering) #Generative grammar #Generative model #Inference #Language and cultural evolution #Machine Learning (cs.LG) #Natural language #Reinforcement Learning in Robotics #Representation (politics) #Task (project management) #The Symbolic
paper · pdf · doi:10.48550/arxiv.2504.17261
openalex publication_date 2025/04/24 · openalex created_date 2025/10/18 · openalex updated_date 2026/08/05
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.