Sparse attention changes which prompt content influences model answers, an effect that can cancel out in aggregate accuracy and is visible through a dense-calibrated counterfactual audit.
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Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation
Sparse attention changes which prompt content influences model answers, an effect that can cancel out in aggregate accuracy and is visible through a dense-calibrated counterfactual audit.