arXiv:2606.05014v1 Announce Type: new Abstract: Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference--a cost that becomes increasingly salient as modern LLMs compress the cache with grouped-query and multi-head latent attention. We introduce Depth-Attention, which performs this selecti
The continuous growth in LLM complexity and the increasing computational cost of inference necessitate novel architectural improvements to maintain performance while managing resource demands.
Improving the efficiency and effectiveness of attention mechanisms directly impacts the scalability and capability of large language models, affecting their deployment and potential applications.
This research proposes a new architectural component, Depth-Attention, that selectively reuses earlier layer representations, potentially leading to more efficient and powerful LLMs without increasing inference costs associated with traditional cross-layer methods.
- · AI researchers
- · LLM developers
- · Cloud providers
- · AI-powered applications
- · Inefficient LLM architectures
- · Hardware providers specialized only in traditional Transformer scaling
More powerful and efficient language models will become available for various tasks.
Reduced operational costs for deploying large language models could accelerate their adoption across industries.
Increased accessibility to advanced AI capabilities might accelerate the development of complex AI agents and integrated systems.
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