This May 2026 arXiv preprint (not yet peer-reviewed) from Yaojian Chen proposes a six-layer system stack for real-time quantum error correction, shifting focus from algorithmic proofs to end-to-end latency budgets, tail latencies, and syndrome-to-logical-operation coordination. Unlike mainstream coverage fixated on Google's distance-5/7 surface code thresholds or Riverlane-Rigetti feedback demos, the work quantifies how QEC round times and data-path coordination—not raw decoder throughput—now dominate. Methodology relies on benchmarking mainstream decoders for surface and qLDPC codes against hardware-derived latency models, without new experimental runs or large-scale simulations; limitations include reliance on projected hardware parameters rather than live multi-qubit tests. It connects overlooked patterns: Google's 2023 Nature surface-code results and 2024 updates demonstrated below-threshold performance but left decoder integration gaps, while a 2025 Riverlane arXiv on low-latency loops highlighted similar coordination failures. The analysis reveals that closing these requires interface definitions between syndrome acquisition and logical gates, a foundational step toward FTQC that hype around logical qubit counts obscures. By modeling full-stack budgets, the blueprint directly informs hardware roadmaps at IBM and Quantinuum.