SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
SymbOmni is a novel AI model addressing the 'perpetual novice' problem—the inability of current models to learn cumulatively and evolve autonomously. It employs Symbolic Concept Learning with an optimizable memory module that abstracts low-level operations into reusable symbolic workflow instructions, operating via an induction-transduction cycle. Experiments show it outperforms existing agent systems and closed-source models in image quality and task success, reduces token consumption by over 40%, and achieves state-of-the-art continual learning results.
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[Submitted on 13 Jul 2026]
Title:SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
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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.
Comments: ECCV 2026 (49 pages, 10 figures, project page: this https URL)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.12042 [cs.CV]
(or arXiv:2607.12042v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.12042
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jianru Li [view email] [v1] Mon, 13 Jul 2026 18:00:34 UTC (10,460 KB)
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