How can science free itself from colonial legacies embedded in its practices? This is the central question driving a new project by Professor Catarina Dutilh Novaes, who joins NIAS in the first semest...

JGPS has been a forum for original research and discussion in the philosophy of science since the publication of its first volume in 1970. The journal’s scope ...

This paper explores the development and evaluation of physics-specific large-scale AI models, which we refer to as large physics models (LPMs). These models, based on foundation models such as large language models (LLMs) are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining large physics models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

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<div style="font-size: 40px;"><h1 class="fr-text">Philosophy of Science Around the World - 2025</h1></div>

Details: Elisabeth Bik: “Errors and Misconduct in Biomedical Research”Abstract: Science builds upon science. Even after peer-review and publication, science papers could still contain images or oth…

On Elephants and Octopuses: Two Models of Pluralistic Policy Advice