SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning

• URL: http://arxiv.org/abs/2607.12042v1
• Date: Mon, 13 Jul 2026 18:00:34 GMT
• Title: SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
• Abstract summary: We propose Symb Omni, an agentic omni-model designed for cumulative evolution through Concept Learning.<n>At its core is the Symbolic Concept Box, an optimizable memory module that abstracts low-level operations into reusable Instructions.<n>Experiments validate that (I) Symb Omni 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.
• Score: 23.30327391111696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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