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    "headline": "The Wave Machine Calculated Its Own Gradient",
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    "dek": "A nonlinear multipath experiment extracted optimization sensitivities from the hardware itself, without a digital twin.",
    "summary": "A nonlinear multipath experiment extracted optimization sensitivities from the hardware itself, without a digital twin.",
    "body_text": "The platform uses incommensurate coaxial cables, T-junctions and one diode-loaded nonlinear cavity to create repeated scattering paths. A matched adjoint excitation lets measurements recover the gradient needed to optimize the system even though superposition no longer holds globally. The experiment turns multipath complexity and a localized nonlinearity into resources for control, pointing toward adaptive communications, imaging and analog intelligence in partially unknown environments rather than demonstrating those applications directly.",
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    "modified_at": "2026-08-16T15:43:36.912-04:00",
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    "tags": [
      "wave control",
      "adjoint optimization",
      "nonlinear systems",
      "analog computing"
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      "title": "arXiv preprint 2608.13503",
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    "title": "The Wave Machine Calculated Its Own Gradient",
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