Beyond Error Rates: Separating Difficulty from Decision-Rule Shift in Neural Quantum Error Correction

Quantum error correction (QEC) uses measured error signatures to infer and correct physical faults. Neural decoders learn a syndrome-to-prediction mapping from samples generated under one noise model, but real quantum hardware does not operate under a fixed noise distribution. Existing evaluations typically report how the logical error rate changes with noise, but this cannot tell whether the problem has become harder or the optimal decoding decisions have changed. We introduce NSBench, a circuit-level benchmark that evaluates neural QEC decoders on shared syndrome streams across 18 streaming scenarios, a 75-configuration noise grid, and mechanism-specific strength sweeps. In tractable settings, exact source- and target-optimal references separate target difficulty from the cost of retaining old decoding decisions, which we call the shift cost. Broad multiplier-grid perturbations substantially increase the logical error rate of seven trained neural decoders at distance three and two released pre-decoders. In the shallow exact settings, the grid's shift cost remains small relative to target difficulty, whereas sufficiently strong local hotspots and correlated pairs have much larger relative shift costs. In absolute terms, the hardest grid targets can cost as much as structured faults. Adaptation recovers part of the shift cost when syndrome history reveals the change, but can also degrade performance when little shift exists. An exact Bayesian filter further shows that syndrome history alone cannot identify some noise changes. The logical error rate alone is therefore insufficient to determine the need for adaptation, and neural QEC decoders should be evaluated by how physical noise changes the decoding problem itself.