We study algorithmic barriers to detecting and repairing a systematic form of structural overspecification in adaptive data-structure selection. An input instance induces an implied workload signature, such as ordering, sparsity, dynamism, locality, or substring structure, and candidate implementations may be preferred because they match that full signature even when the measured workload evidence supports only a strict subset of it. Under a model in which pairwise evaluators favor implementations that realize the implied signature, we show that this preference propagates through both benchmark aggregation and Bradley-Terry-Luce fitting. We then establish two main results. First, determining whether a representation-selection pipeline exhibits structural commitment beyond measured warrant is undecidable on unbounded input domains, by reduction from the halting problem, but decidable by exhaustive enumeration on finite domains. Second, under a conservative repair constraint requiring already evidence-aligned pipelines to remain unchanged, any total computable repair operator admits an overspecified fixed point via Kleene's recursion theorem. These barriers are qualitatively different from classical lower bounds in data-structure design: they do not limit efficiency on finite workloads, but the possibility of uniformly detecting and repairing overspecification across pipeline families.

翻译：我们研究了在自适应数据结构选择中检测和修复一种系统性结构性过度规范化的算法障碍。输入实例会引发隐含的工作负载特征（如有序性、稀疏性、动态性、局部性或子串结构），候选实现可能因匹配该完整特征而被优先选择，即使实测工作负载证据仅支持其严格子集。在成对评估器倾向于选择实现隐含特征的模型下，我们证明这种偏好会通过基准测试聚合和Bradley-Terry-Luce拟合过程传播。随后我们建立了两个主要结果：第一，通过停机问题的归约，确定表示选择流水线是否表现出超出测量依据的结构承诺在无界输入域上是不可判定的，但在有限域上可通过穷举枚举判定；第二，在保守修复约束（要求已证据对齐的流水线保持不变）下，任何全可计算修复算子都通过Kleene递归定理存在过度规范化的不动点。这些障碍与数据结构设计中的经典下界本质不同：它们并不限制有限工作负载上的效率，而是在流水线族之间实现统一检测和修复过度规范化的可能性。