Inference Time Context Sparsity: Illusion or Opportunity?
Sparsity has long been a central theme in LLM efficiency, but its role in context processing remains unresolved. As LLM workloads shift toward longer contexts and agentic interactions, the compute and memory bottlenecks of attention become increasingly critical, raising the question of whether these constraints are fundamental. Our position is that these constraints are artificial and unnecessary, and that the future of LLM inference lies in extreme but principled sparsity along the context dimension. This position is supported by several strands of empirical and theoretical evidence. First, we find the insistence on dense attention unreasonable, since in a long context a query effectivel...
| Authors | Sahil Joshi, Prithvi Dixit, Agniva Chowdhury et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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