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    "headline": "One Neural Pipeline Chooses the Reaction Path and Its Rates",
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    "dek": "Shared sequence encoders support both model selection and parameter inference for competing chemical networks.",
    "summary": "Shared sequence encoders support both model selection and parameter inference for competing chemical networks.",
    "body_text": "The architecture combines one-dimensional convolution, set attention, a model-selection network and conditional invertible networks. A protodeboronation example shows how it handles ambiguous catalyst-deactivation pathways. The paper demonstrates inference on the studied system rather than general chemical identification.",
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      "chemical reactions",
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      "model selection"
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      "title": "arXiv preprint 2610.18825, original v1",
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