SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning

Visual generation is increasingly ubiquitous in diverse do-mains, from text-to-image/video synthesis to multimodal interactive cre-ation. Yet prevailing monolithic models remain fundamentally constrainedby their inability to learn cumulatively and evolve autonomously, whichis a limitation we term the “perpetual novice” problem. They lack mech-anisms for structuring experience into reusable knowledge and there-fore rely on brittle, “from-scratch” reasoning for each task, resultingin poor compositional generalization and inefficient knowledge reten-tion. Motivated by these limitations, we propose SymbOmni, an agenticomni-model designed for cumulative evolution through Symbolic Con-cept Learning. At its core is the Symbolic Concept Box, an optimizablememory module that abstracts low-level operations into reusable Sym-bolic 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 withlanguage-based feedback to enable continuous self-improvement withoutgradient-based model fine-tuning. Comprehensive experiments validatethat (I) SymbOmni significantly outperforms existing agent-based sys-tems for iterative creation and also surpasses closed-source models (e.g.,Nano Banana, GPT-Image-1) in both image quality and task successrates; (II) SymbOmni effectively reduces token consumption by over 40%while maintaining competitive generation quality; and (III) SymbOmnienables effective continual learning by achieving cumulative gains acrossmultiple online-learning benchmarks and setting a new state of the art.