[
    {
        "id": "osp-16600",
        "type": "article-journal",
        "title": "ProtocolMatch: Protocol-Dependent Model Selection for Scientific Dynamics Forecasting",
        "author": [
            {
                "family": "Wei",
                "given": "Lu"
            },
            {
                "family": "Wang",
                "given": "Yufeng"
            },
            {
                "family": "Ling",
                "given": "Haibin"
            }
        ],
        "URL": "https://omanscience.com/en/articles/protocolmatch-protocol-dependent-model-selection-for-scientific-dynamics-forecasting",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Scientific dynamics forecasting is often framed as an architecture choice, although deployment is also determined by observed history, rollout feedback, compute budget, physical objective, and test distribution. We formulate protocol-dependent model selection and introduce ProtocolMatch, a compute-matched, validation-selected, and failure-preserving evaluation framework. On driven quantum-spin dynamics, we compare recurrent, patched-attention, causal-attention, and low-rank linear predictors across three independently generated datasets. The causal-attention--recurrence ordering reverses as the training set grows within a fixed two-spin task, while a linear predictor has the lowest mean error in the six-spin local-observable comparison. Restricting observed history worsens every refreshed-history view but improves every closed-loop view in the four-spin study. A latest-state MLP has lower error than persistence on every dataset under state refresh across all five cells, yet its closed-loop rank varies by system and includes finite explosive errors. Physical penalties improve targeted consistency without reliably improving prediction error, and in-distribution intervals lose most coverage after a driving-frequency shift. Thus scientific model selection should return a predictor with its protocol and report accuracy, physical validity, and shifted-distribution reliability separately."
    }
]