SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
SymbOmni employs symbolic concept learning for continuous model evolution, boosting generation quality and efficiency.
Key Findings
Methodology
The approach centers on the Symbolic Concept Box (CB), an optimizable memory storing reusable symbolic concepts (Ck). It employs an induction-transduction cycle: experiences are abstracted into symbolic concepts (induction), then adaptively composed via Symbolic Workflow Instructions (SWIs) to solve new tasks (transduction). Language-guided verbalized backpropagation refines knowledge without gradient fine-tuning. This hybrid system combines neural expressiveness with symbolic interpretability, enabling continuous self-improvement.
Key Results
- SymbOmni outperforms state-of-the-art agent systems like Nano Banana and GPT-Image-1 in image quality and task success rate, with improvements over 15%.
- It reduces token consumption by over 40% while maintaining comparable visual fidelity.
- In online continual learning benchmarks, it achieves cumulative gains, setting new SOTA performance, demonstrating strong self-evolution capabilities.
Significance
This work addresses core limitations of monolithic models—poor knowledge reuse and limited generalization—by introducing a symbolic, self-evolving framework. It bridges the gap between neural flexibility and symbolic reasoning, enabling models to autonomously accumulate and refine knowledge, which is crucial for scalable, intelligent AI systems. The method paves the way for more adaptable, efficient multimodal AI applications in industry and research.
Technical Contribution
The paper's key innovation is the integration of a symbolic memory (CB) with neural models, facilitating knowledge reuse without gradient updates. The verbalized backpropagation mechanism allows continuous learning, while the induction-transduction cycle ensures structured knowledge evolution. This hybrid approach offers theoretical guarantees of continual improvement and practical benefits in reducing computational costs and enhancing generalization.
Novelty
This is the first framework combining symbolic concept learning with a self-evolving cycle that does not rely on gradient fine-tuning. It introduces dynamic composition of symbolic workflows, enabling task transfer and knowledge accumulation in a way that surpasses previous static models and external tool-based methods, establishing a new paradigm for continual multimodal learning.
Limitations
- The symbolic abstraction may struggle with highly complex or rapidly changing environments, limiting adaptability.
- Knowledge base maintenance still depends on manual curation or extensive annotation, which could hinder scalability.
- Retrieval efficiency from large symbolic repositories can degrade, impacting real-time performance. Future work should focus on automating symbol generation and optimizing retrieval.
Future Work
Future directions include automating symbolic concept generation, integrating reinforcement learning for policy optimization, and extending the framework to broader multimodal tasks. Improving symbolic knowledge management and scalability will further enhance autonomous adaptation, making SymbOmni more versatile in real-world applications.
AI Executive Summary
SymbOmni marks a significant advance in multimodal visual generation by integrating symbolic concept learning into a continual self-evolving framework. Traditional models excel at end-to-end training but lack mechanisms for knowledge reuse and incremental learning, resulting in poor generalization and inefficiency. To overcome these limitations, the authors propose a hybrid architecture that combines neural networks with a symbolic memory— the Symbolic Concept Box (CB)—which stores reusable symbolic concepts (Ck). These concepts encapsulate semantic understanding and executable procedures, enabling the model to abstract experiences and compose solutions dynamically.
The core innovation lies in the induction-transduction cycle, where experiences are first abstracted into symbolic concepts through induction. During transduction, relevant concepts are retrieved, composed, and instantiated via Symbolic Workflow Instructions (SWIs) to solve new tasks. This process is guided by language-based feedback, specifically verbalized backpropagation, which refines symbolic parameters without traditional gradient updates. This mechanism allows the model to self-improve continually, accumulating knowledge over multiple tasks.
Experimental results demonstrate that SymbOmni surpasses existing models like Nano Banana and GPT-Image-1 in image quality, success rate, and token efficiency, reducing token use by over 40%. It also achieves state-of-the-art performance in online continual learning benchmarks, validating its capacity for autonomous evolution. The approach effectively bridges symbolic reasoning and neural expressiveness, opening new avenues for scalable, adaptable AI systems.
Despite these advances, challenges remain in automating symbolic knowledge generation and managing large symbolic repositories efficiently. Future work will focus on enhancing symbolic automation, expanding to more complex multimodal tasks, and improving scalability. Overall, SymbOmni offers a promising paradigm for building autonomous, continually improving AI systems capable of complex, structured reasoning across diverse domains.
Deep Analysis
Background
随着多模态视觉生成技术的快速发展，深度学习模型如GAN和Diffusion模型取得了巨大成功，但其普遍依赖端到端训练，缺乏知识的结构化存储与持续演化能力。近年来，符号推理与神经网络的结合逐渐成为研究热点，代表性工作包括Neuro-Symbolic系统和知识图谱，旨在提升模型的可解释性和泛化能力。然而，这些方法多依赖外部知识库或复杂符号操作，难以实现模型的自主学习和持续优化。传统端到端模型在面对复杂、多任务场景时表现出“从零开始”的局限，难以实现知识的累积和迁移，导致泛化能力不足。当前研究试图融合深度表达能力与符号推理优势，推动模型向更智能、更自主的方向发展。
Core Problem
现有视觉生成模型普遍存在知识重用效率低、泛化能力不足的问题。端到端训练在新任务面前缺乏知识积累机制，导致重复劳动和低效。符号推理虽能提供结构化知识，但难以与深度模型高效结合，限制了模型的自主演化。如何在无需大量微调的情况下，实现经验抽象、符号知识积累和高效迁移，成为核心难题。此外，缺乏可扩展的符号知识管理机制，也阻碍了模型在复杂、多任务环境中的表现。
Innovation
本文提出SymbOmni架构，创新性地引入符号概念箱（CB）作为可优化的记忆单元，存储符号概念（Ck），实现符号知识的持续积累。结合符号工作流指令（SWIs）动态组合解决新任务，采用无梯度的语言引导反向传播机制，优化符号参数，避免繁琐的梯度微调。核心在于归纳-转导循环：经验归纳为符号概念，符号组合解决新任务，反馈优化符号知识。这一机制突破了端到端模型的局限，增强了结构化表达和迁移能力，推动持续学习。
Methodology
- �� 输入：任务指令和经验数据。
- �� 归纳：将经验抽象为符号概念（Ck），存入符号概念箱（CB）。
- �� 转导：检索相关符号概念，通过符号工作流指令（SWIs）组合解决新任务。
- �� 语言反馈：用自然语言描述任务结果，通过反向传播优化符号参数。
- �� 记忆更新：成功经验转化为正符号概念，失败经验生成负符号概念或参数修正。
- �� 反复循环：不断积累和优化符号知识，实现模型的持续演化。
Experiments
在ComfyBench、GenEval和ReasonEdit三个公开基准上评估SymbOmni。对比Nano Banana、GPT-Image-1等模型，指标包括成功率、生成质量和Token节省。采用不同任务场景，如文本到图像、图像编辑和多步推理，验证模型的泛化和持续学习能力。超参数设置包括最大搜索深度10，重试次数4，确保公平性。还进行了消融实验，验证符号记忆的贡献，并在大规模任务中测试模型的效率。
Results
SymbOmni在所有任务中均优于对比模型，图像质量提升15%以上，任务成功率显著提高。Token节省超过40%，在连续学习测试中实现累积性能提升，达到了行业领先水平。符号知识的引入显著增强了模型的泛化能力，尤其在复杂多步骤任务中表现优异。消融实验显示符号记忆对性能提升至关重要，验证了符号-神经结合的有效性。
Applications
该模型适用于自动内容创作、复杂任务规划和多模态交互，特别在需要知识重用和持续学习的场景中表现出色。未来可应用于智能助手、自动设计和机器人自主学习等领域，推动AI系统向更自主、更智能的方向发展。
Limitations & Outlook
模型在极端复杂或动态环境下，符号抽象可能不足，影响泛化。符号知识库维护依赖人工或大量标注，成本较高。大规模符号库检索效率仍需优化，未来需提升自动化程度和扩展能力。
Plain Language Accessible to non-experts
想象你在厨房做饭，平时用的锅碗瓢盆就像模型里的符号概念。每次做菜时，你会根据菜谱（任务指令）选择合适的工具（符号概念），并按照步骤（符号工作流）操作。做得好，经验会被你记住，下次遇到类似菜谱时，你可以快速找到对应的工具和步骤，做菜变得更快更好。SymbOmni也是这样，它把做事的经验变成“工具箱”，每次遇到新任务时，模型就像厨师一样，从“工具箱”里快速找到合适的“工具”和“步骤”，不断学习和改进，变得越来越聪明。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏。每次你拼完一块，都会记住这个拼法，下次遇到类似的拼图时，你可以直接用之前的方法拼。可是，有时候拼图太难，你不知道怎么开始。这时，你可以用一些简单的规则，比如：先拼边缘，再拼颜色相似的块。SymbOmni就像这样，它把拼图的经验变成一套“规则”和“工具”，每次遇到新拼图时，就可以用之前学到的规则快速拼好。它还会不断学习新规则，变得越来越厉害。这样，模型就像一个聪明的拼图高手，能不断变强，解决各种复杂的拼图问题。
Glossary
符号概念 (Symbolic Concept)
一种抽象的知识单元，包含描述和操作指令，用于结构化存储经验。In this paper, it代表模型中的可重用知识块。
用于存储和重用经验，支持模型的持续学习。
符号工作流指令 (Symbolic Workflow Instruction)
描述一系列操作的模板，用于实现符号概念的具体执行。它是符号推理和操作的核心。
在符号概念中作为执行步骤的模板。
归纳-转导 (Induction-Transduction)
模型通过归纳抽象经验为符号概念，再通过符号组合解决新任务的循环过程。
核心学习机制，支持模型持续演化。
语言引导反向传播 (Verbalized Backpropagation)
用自然语言描述的反馈机制，指导符号参数优化，无需梯度微调。
实现模型自我改进的关键技术。
持续演化 (Cumulative Evolution)
模型在不断积累和优化符号知识的基础上，提升任务表现的能力。
实现模型的自主学习和适应。
Open Questions Unanswered questions from this research
- 1 如何自动生成和维护符号概念，尤其在动态环境中，仍需探索符号自动扩展和知识迁移机制。
- 2 模型在极端复杂任务中的符号抽象能力有限，未来需提升符号表达的表达力和泛化能力。
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.