发表机构
South China University of Technology; Westlake University; Johns Hopkins University; The Chinese University of Hong Kong; Shenzhen Loop Area Institute（华南理工大学; 西湖大学; 约翰·霍普金斯大学; 香港中文大学; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对视觉生成模型无法累积学习与自主进化的问题，提出SymbOmni智能全能模型，通过符号概念学习和归纳-转导循环运行，经言语反向传播训练。实验验证其在多方面性能优越，能有效降低令牌消耗并实现持续学习。
AI 中文摘要
视觉生成在诸多领域日益普遍，但现有整体模型存在‘永远的新手’问题，无法累积学习与自主进化。为此提出SymbOmni，一种通过符号概念学习进行累积进化的智能全能模型。其核心是符号概念盒，将低级操作抽象为可复用的符号工作流指令。通过归纳-转导循环运行，训练采用基于语言反馈的言语反向传播。实验表明，它在迭代创建中显著优于现有基于代理的系统，图像质量和任务成功率超闭源模型，有效降低令牌消耗，实现有效持续学习。
英文摘要
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.
CommentsECCV 2026 (49 pages, 10 figures, project page: https://spherelab.ai/symbomni)
