SPIN: Shadow Predictive Indexer for Sparse Attention
Spin reduces indexer overhead in large language models by using lightweight, history-based prediction instead of scoring every KV block at every step. It predicts important token blocks using exponential moving averages of past scores, skipping up to 40% of the input while maintaining high task quality and improving serving speed.