How Much Dense Attention is Necessary? Oracle-Guided Sparse Prefill for Full/GQA Layers in Hybrid Long-Context Models
arXiv:2606.07703v1 Announce Type: new Abstract: Long-context prefill remains expensive because full/GQA layers still score the historical sequence, even in hybrid models with local, sparse, linear, or recurrent components. We study how much dense attention is needed to preserve task-level behavior under explicit support granularity and top-k budgets. We introduce an attention-mass top-k oracle for existing GQA checkpoints: for each layer and query position, it computes dense attention, selects head-averaged token support, and recomputes attention only on that support. The oracle is a diagnosti
The increasing computational demands of long-context AI models necessitate more efficient attention mechanisms to scale capabilities without prohibitive resource costs.
This research directly addresses the computational bottleneck of long-context models, potentially making them more accessible and economical for broader applications.
The understanding of attention mechanisms in transformer models is refined, offering pathways to more efficient model architectures and training techniques.
- · AI model developers
- · Cloud providers
- · Hardware manufacturers (GPUs)
- · AI-driven application sectors
- · Inefficient model architectures
- · High-cost long-context AI infrastructure
More efficient and cost-effective deployment of long-context AI models.
Acceleration in the development of more capable and complex AI applications due to reduced computational overhead.
Enhanced competition among AI service providers as scaling becomes less resource-intensive, potentially lowering barriers to entry for advanced AI.
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Read at arXiv cs.LG