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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Identifying machine learning model lineage is crucial for understanding model development and cost-efficiency.
    • Existing lineage determination methods are empirical, lack theoretical foundations, and struggle with high-impact modifications.
    • Measuring the degree of modification (lineage closeness) between models remains an unaddressed challenge.

    Purpose of the Study:

    • To reformulate model lineage determination based on the loss landscape's local optima.
    • To develop a theoretically grounded method for accurate model lineage determination.
    • To propose a novel, task-agnostic, and modification-agnostic approach for quantifying lineage closeness.

    Main Methods:

    • Reframe lineage determination as models' parameters residing in the same loss landscape local optimum.
    • Analyze the impact of modifications on decision boundaries to infer lineage closeness.
    • Quantify lineage closeness using mean adversarial distance to decision boundaries and prediction matching rates.
    • Employ an efficient data point sampling strategy to reduce computational cost.

    Main Results:

    • Achieved 100% accuracy in model lineage determination across diverse scenarios.
    • Provided precise, quantitative measurements of lineage closeness.
    • Demonstrated the effectiveness of decision boundary changes as a metric for lineage closeness.

    Conclusions:

    • The proposed method offers a theoretically sound and highly effective solution for model lineage determination.
    • The approach accurately quantifies lineage closeness, addressing a significant gap in current research.
    • This work advances the understanding and practical application of model modification techniques.