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The author discusses the concept of conviviality from Ivan Illich's 'Tools for Conviviality' and applies it to computational science, arguing that digital tools in research lack conviviality compared to pre-digital tools, which poses epistemic risks.
Introduces in-span learning, a method to adapt reduced-order models by streaming the model's own predictions through an incremental singular-value decomposition, reweighting and realigning the basis without changing the subspace. The approach is demonstrated on several dynamical systems and proposed as a computational-science analogue of in-context learning.
Science Superpowers is an open-source computational-science methodology for AI research agents, enforcing pre-registration and reproducible workflows to prevent p-hacking and HARKing.
SCICONVBENCH is a benchmark that evaluates LLMs on multi-turn clarification for ill-posed scientific queries across computational science domains, finding that even frontier models struggle with disambiguation and frequently make silent assumptions.