What is the sparse window approach in Kaspa's difficulty calculation?

The sparse window is Kaspa's method for calculating mining difficulty by sampling a well-distributed subset of past blocks rather than examining every block in history. Instead of a complete scan, the sparse window is defined by two parameters — length (the time period in seconds) and size (the number of blocks to sample) — and the subset it selects must be deterministic (calculated identically by all nodes), uniformly distributed (spread evenly across the window), incremental (inheritable by future blocks), and secure (too costly for a miner to game). A previous approach that used block hashes for sampling was found to be gameable; the current design makes manipulation as expensive as discarding a valid block. For a beginner, this matters because it means Kaspa's difficulty adjustment stays accurate and manipulation-resistant even as the network grows and block production rates change.