Accelerated Scan is a focused, high-performance library for solving first-order linear recurrences (associative scans) on GPU. It provides a pure CUDA kernel (`warp`) that uses warp shuffle intrinsics, shared memory, and a hierarchical chunked algorithm to achieve state-of-the-art speed, and a Triton-based implementation (`scalar`) that leverages `tl.associative_scan` for portability. A reference PyTorch implementation (`ref`) implements the classic Blelloch tree scan for validation. The library is designed for integration into state space models and linear RNNs, where the recurrence `x[t] = gate[t] * x[t-1] + token[t]` is central. The codebase is small but exceptionally well-crafted, with clear separation of concerns, comprehensive tests, and a clean API. The CUDA kernel in particular is a standout example of GPU optimization, using warp-level primitives, shared memory, and chunking to handle sequences of arbitrary length (up to 65536) with minimal overhead.