Sparse attention accelerates long-context decoding by reading a subset of the KV cache, but existing methods are open-loop: the kernel commits to its selection with no signal indicating whether enough attention mass was preserved and no recovery path when it was not. The needed signal already exists inside the kernel. The fraction of softmax mass on the selected tokens, which we call coverage, satisfies an exact algebraic equality with per-step output error and serves a dual role as the acceptance criterion and the interpolation weight for recovery. Coverage-Verified Sparse Attention (CVSA) turns coverage into a training-free, closed-loop verify-then-correct decode loop that accepts high-coverage drafts and recovers only the heads that fall short, with no sampling, no draft model, and no speculative-decoding infrastructure. Quality varies by only $0.14$ points across the threshold sweep; budget and scheduling ablations characterize the deployed operating point. On LongBench at 7B and 70B, CVSA comes within $1.13$ points of dense attention, statistically matching it at 70B, and it loses $0.8$ points on RULER; measured median decode-step speedup reaches $6.21\times$ at 128K context.

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