arXiv Machine Learning
Discovering Physical Representation Languages
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arXiv Machine Learning
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Blind Thermodynamic Ontology Discovery from Anonymous Experiments
arXiv:2609.23387v1 Announce Type: new Abstract: Before a machine learning model can learn a thermodynamic equation of state, it must discover what its measurements represent: which channels scale wit...
arXiv Machine Learning
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Data-driven discovery of governing differential equations across physical systems
arXiv:2606. 09638v1 Announce Type: new Abstract: Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena.
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Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models
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Intrinsic Structure: Spectral Identifiability for Mechanistic Interpretability
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Aug 7
SEAM: Global consistency beyond local accuracy in scientific machine learning
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arXiv AI
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SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
SCICONVBENCH is a benchmark designed to evaluate large language models (LLMs) on multi‑turn clarification tasks in computational science. It focuses on two key abilities: eliciting missing information (disambiguation) and resolving contradictory requests (inconsistency resolution) across four domains—fluid mechanics, solid mechanics, materials science, and partial differential equations. The benchmark pairs a structured task ontology with a rubric‑based evaluation framework, measuring LLM performance in clarification behavior, conversational grounding, and final‑specification fidelity, and reveals that even top models only resolve about 52.7% of disambiguation cases in fluid mechanics while often making ungrounded assumptions.