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Toward Computational Scientific Reasoning | Synapse

February 28, 2026Open Access

Toward Computational Scientific Reasoning

Key Points

This paper explores how AI models are shifting from predictive tasks to structured scientific reasoning.
Examines examples from various disciplines, focusing on ChatDIA in mass spectrometry analysis.
Analyzes the participation of AI in reasoning frameworks.
Discusses the implications for scientific inquiry.
AI models are beginning to form and evaluate scientific explanations.
Generative AI systems can contribute to scientific logic and reasoning.
The study presents a new paradigm in understanding AI's role in research.

Abstract

A perspective on the emerging paradigm of computational scientific reasoning, arguing that AI models are beginning to participate in structured reasoning, including the formation and evaluation of scientific explanations, rather than merely optimizing predictive tasks. Drawing on examples across disciplines, especially ChatDIA in mass spectrometry analysis, this article examines how generative AI systems may increasingly contribute to the logic of scientific inquiry.

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Authors

J

Jiayi Li

H

Hannes Röst

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University of Toronto

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Cite this study

Li et al. (Thu,) studied this question.

www.synapsesocial.com/papers/69a287a00a974eb0d3c037d0 — DOI: https://doi.org/10.5281/zenodo.18778839