The paper focuses on designing new neural architectures; it presents a new search space and new optimization criteria. The new search space includes tensor-to-tensor operators integrating activation and normalization functions; the criteria involve an early performance indicator (this is classical) and a stability indicator (this is new). The rebuttal addressed nearly all reviewers' concern: * about the significance of the performance gains; * about the generality of the approach when applied to other architectures; * about the fair evaluation (with a hold-out); * about the impact of the stability indicator (lesion study). The AC would like the computational cost of the evolution to be spelled out in the revised paper (beyond "a relatively large number of CPUs" ..); how many tournaments? As a suggestion, it might be interesting to see whether (and how) scale insensitivity (E.2) could be used as a 3rd rejection criterion.

The energy transition introduces more volatile energy sources into the power grids. In this context, power transfer between different synchronous areas through High Voltage Direct Current (HVDC) links becomes increasingly important. Such links can balance volatile generation by enabling long-distance transport or by leveraging their fast control behavior. Here, we investigate the interaction of power imbalances - represented through the power grid frequency - and power flows on HVDC links between synchronous areas in Europe. We use explainable machine learning to identify key dependencies and disentangle the interaction of critical features. Our results show that market-based HVDC flows introduce deterministic frequency deviations, which however can be mitigated through strict ramping limits. Moreover, varying HVDC operation modes strongly affect the interaction with the grid. In particular, we show that load-frequency control via HVDC links can both have control-like or disturbance-like impacts on frequency stability.