arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the Selective Scan Structured State-Space Model, or Mamba, addresses this computational challenge effectively. This paper explores a query-based cross-modal projector designed to bolster Mamba's efficiency for vision-language modeling by compressing visual tokens based on input through the cross-attention mechanism. This innovative projector also removes the need for manually designing the 2D scan orde
The increasing computational demands of large multimodal models necessitate more efficient architectures like Mamba to overcome Transformer limitations.
This development offers a potential path to scaling multimodal LLMs more efficiently, reducing compute costs and enabling new capabilities in vision-language understanding.
The computational bottleneck of quadratic complexity in multimodal LLMs is being directly addressed, potentially accelerating the development and deployment of more capable AI.
- · AI developers
- · Cloud providers (via efficiency gains)
- · Hardware manufacturers (new demand for Mamba-optimized chips)
- · Industries adopting multimodal AI
- · Inefficient Transformer-based multimodal LLMs
- · Companies heavily invested in quadratic complexity systems
More efficient and scalable multimodal AI models become available for broader use cases.
Reduced computational costs could democratize access to advanced AI capabilities and accelerate specialized AI development.
A shift towards Mamba-like architectures could reshape the competitive landscape for AI model development and infrastructure.
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Read at arXiv cs.CL