Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery

• URL: http://arxiv.org/abs/2606.02632v1
• Date: Sat, 30 May 2026 15:21:58 GMT
• Title: Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
• Abstract summary: Modern Machine Learning (ML) and Artificial Intelligence (AI) models are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data.<n>This paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined.<n>This underdetermination becomes uniquely hazardous with large language models (LLMs), which tend to collapse large equivalence classes of explanations into a single fluent narrative.
• Score: 1.3875545441867139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data. This position paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined: many incompatible mechanisms induce essentially the same observational relationships on the support of the data, so predictive success and coherent explanations are insufficient evidence of mechanism discovery. This underdetermination becomes uniquely hazardous with large language models (LLMs), which tend to collapse large equivalence classes of explanations into a single fluent narrative. This paper proposes concrete standards for ``mechanistic ML,'' and argues these norms are necessary if LLM-centered workflows are to support science rather than merely simulate it.

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