arXiv:2604.07753v2 Announce Type: replace-cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts. While existing paradigms like Mixture-of-Transformers (MoT) mitigate this conflict through structural isolation, they fundamentally sever cross-modal synergy and suffer from capacity fragmentation. In this work, we present Symbiotic-MoE, a unified pre-training framework that resolves task interference within a native multimodal Mixture-of-Experts (MoE) Transformers architecture w
The accelerating development of Large Multimodal Models (LMMs) is highlighting fundamental architectural challenges in integrating diverse AI capabilities without compromising performance.
This work directly addresses a core technical hurdle in scaling AI models for broad real-world applications, potentially leading to more efficient and capable general-purpose AI.
The proposed Symbiotic-MoE framework offers a new architectural paradigm for LMMs, aiming to resolve prior issues of catastrophic forgetting and capacity fragmentation when combining generation and understanding tasks.
- · AI researchers
- · Multimodal AI developers
- · Cloud AI providers
- · Users of general-purpose AI
- · Traditional isolated multimodal model approaches
- · Researchers focused solely on separate generation or understanding models
Improved performance and efficiency in integrated multimodal AI systems.
Faster development and deployment of advanced AI applications across various industries.
Enhanced AI capabilities contributing to broader societal impacts, including autonomous agents and human-AI collaboration.
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Read at arXiv cs.LG