Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regime of agent dynamics that has been overlooked previously. It appears robustly optimal for performance under many conditions, thus providing valuable insights into computation with physical systems more generally. The key to forming effective mechanisms for information processing appears in the system's intrinsic relaxation abilities. These are probed without actually enforcing a specific inference goal. The dynamical regime that achieves optimal computation is located just below a critical damping threshold, involving a relaxation with multiple stages, and is readable at the single-particle level. At the many-body level, it yields substrates robustly optimal for RC across varying physical parameters and inference tasks. A system in this regime exhibits a strong diversity of dynamic mechanisms under highly fluctuating driving forces. Correlations of agent dynamics can express a tight relationship between the responding system and the fluctuating forces driving it. As this model is interpretable in physical terms, it facilitates re-framing inquiries regarding learning and unconventional computing with a fresh rationale for many-body physics out of equilibrium.

翻译：在储层计算（RC）范式中研究了活性物质的信息处理能力，以推断混沌信号的未来状态。我们发现了一个先前被忽视的智能体动力学特殊区域。该区域在许多条件下表现出鲁棒最优的性能，从而为更广泛地利用物理系统进行计算提供了有价值的见解。形成有效信息处理机制的关键在于系统固有的弛豫能力。这些能力是在未强制执行特定推断目标的情况下探测到的。实现最优计算的动力学区域恰好位于临界阻尼阈值之下，涉及多阶段弛豫过程，并可在单粒子水平上解读。在多体水平上，该区域产生的基底在不同物理参数和推断任务下均对RC表现出鲁棒最优性。处于该区域的系统在高度波动的驱动力下展现出强烈的动力学机制多样性。智能体动力学的相关性能够表达响应系统与驱动其波动力之间的紧密联系。由于该模型可从物理角度进行解释，这有助于以非平衡多体物理的新原理重新构建关于学习和非常规计算的研究框架。