Physical selectors (lasers choosing a mode, Ising machines settling on a ground state, condensates occupying a spin state) produce high-dimensional signatures at the moment of decision: full field amplitudes, multimode interference patterns, or scattering responses. These signatures are richer than the winner's index, yet they are routinely discarded. We show that when the signatures are repeatable across trials (stereotyped) and linearly independent across routes, a single linear decoder compiled from calibration data maps them to arbitrary payloads, merging selection and memory access into one event and eliminating the fetch that dominates latency and energy in sparse routing architectures. The construction requires one SVD of measured device responses, which certifies capability and bounds worst-case error for any downstream payload before the task is chosen. Runtime error separates into two independently diagnosable channels, decoding fidelity (controlled by dictionary conditioning $σ_{\min}(Φ)$) and routing reliability (controlled by the margin-to-noise ratio $Δ/T_{\mathrm{eff}}$), each with a distinct physical origin and targeted remedy. We derive the full error decomposition, give Ising-machine selector constructions, and validate the predicted scalings on synthetic speckle-signature simulations across three measurement modalities. No hardware demonstration exists; we provide a falsifiable four-step experimental protocol specifying what a first experiment must measure. Whether real device signatures satisfy stereotypy is the central open question.

翻译：物理选择器（激光选择模式、伊辛机稳定于基态、凝聚体占据自旋态）在决策时刻会产生高维特征：完整场振幅、多模干涉图样或散射响应。这些特征比获胜者索引更为丰富，却常被丢弃。我们证明，当这些特征在多次实验中具有可重复性（模式化）且在不同路径间线性无关时，通过校准数据构建的单一线性解码器即可将其映射至任意有效载荷，从而将选择与内存访问合并为单一事件，并消除稀疏路由架构中主导延迟和能耗的数据获取过程。该构建仅需对测量设备响应进行一次奇异值分解，即可在任务选定前验证系统能力并为任意下游有效载荷提供最坏情况误差界。运行时误差可分解为两个独立可诊断的通道：解码保真度（由字典条件数$σ_{\min}(Φ)$控制）和路由可靠性（由信噪比裕度$Δ/T_{\mathrm{eff}}$控制），二者具有不同的物理起源和针对性改进方案。我们推导了完整的误差分解公式，给出了伊辛机选择器的具体构建方法，并通过三种测量模态下的合成散斑特征仿真验证了预测的标度律。目前尚无硬件演示；我们提出了可证伪的四步实验协议，明确了首次实验必须测量的内容。真实设备特征是否满足模式化要求，是当前亟待解决的核心问题。