SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning

Researchers propose SymbOmni, an agentic omni-model that uses Symbolic Concept Learning to overcome the 'perpetual novice' problem in visual generation. The model outperforms existing agent-based and closed-source systems in image quality and task success while reducing token consumption by over 40%. SymbOmni achieves cumulative gains in continual learning benchmarks, setting a new state of the art.

arXiv:2607.12042v1 Announce Type: new Abstract: Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing monolithic models remain fundamentally constrained by their inability to learn cumulatively and evolve autonomously, which is a limitation we term the "perpetual novice" problem. They lack mechanisms for structuring experience into reusable knowledge and therefore rely on brittle, "from-scratch" reasoning for each task, resulting in poor compositional generalization and inefficient knowledge retention. Motivated by these limitations, we propose SymbOmni, an agentic omni-model designed for cumulative evolution through Symbolic Concept Learning. At its core is the Symbolic Concept Box, an optimizable memory module that abstracts low-level operations into reusable Symbolic Workflow Instructions. SymbOmni operates through an induction-transduction cycle: experiences are abstracted into symbolic concepts induction , which are then adaptively composed to solve novel tasks transduction . The training is done by verbalized backpropagation with language-based feedback to enable continuous self-improvement without gradient-based model fine-tuning. Comprehensive experiments validate that I SymbOmni significantly outperforms existing agent-based systems for iterative creation and also surpasses closed-source models e.g., Nano Banana, GPT-Image-1 in both image quality and task success rates; II SymbOmni effectively reduces token consumption by over 40% while maintaining competitive generation quality; and III SymbOmni enables effective continual learning by achieving cumulative gains across multiple online-learning benchmarks and setting a new state of the art.