Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
arXiv:2602.01203v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often assign disproportionate attention to the first token, a phenomenon known as the attention sink. Several recent approaches aim to address this issue, including Sink Attention in GPT-OSS and Gated Attention in Qwen3-Next. However, a comprehensive analysis of the relationship among these attention mechanisms is lacking. In this work, we provide both theoretical and empirical evidence demonstrating that the sink in Vanilla Attention and Sink Attention naturally construct a Mixture-of-Experts (MoE) mechanis
This research emerges as the AI community continues to refine LLM architectures, addressing known limitations like attention allocation to improve efficiency and performance.
Improving attention mechanisms in Large Language Models directly impacts their efficiency, scalability, and ability to process longer contexts, which is crucial for advanced AI applications.
New theoretical and empirical understanding of attention sinks and their unintentional formation of Mixture-of-Experts (MoE) in LLMs will lead to more optimized model designs and training strategies.
- · AI researchers
- · Large Language Model developers
- · AI consulting firms
- · Inefficient LLM architectures
More robust and efficient Large Language Models will be developed with improved attention mechanisms.
The enhanced performance of LLMs could accelerate the development and deployment of AI-driven applications and agentic systems.
Increased efficiency in AI model training may reduce computational resource requirements, potentially spreading AI development capabilities to a broader range of actors.
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Read at arXiv cs.LG