RelSciFM 2026: The workshop on Reliable Scientific Foundation Models: Design, Training, Grounding, and Verification

Le résumé fourni par la source

Scientific Foundation Models (SciFMs) are emerging as a general-purpose substrate for scientific computing, integrating heterogeneous scientific data and interacting with computational tools to support generation, reasoning, and decision-making. However, their real-world scientific impact is increasingly limited by reliability: outputs can be plausible yet incorrect, weakly grounded in evidence, inconsistent with domain constraints, and difficult to reproduce or audit. RelSciFM 2026 provides a focused forum to advance Reliable Scientific Foundation Models through four tightly connected pillars: Design, Training, Grounding, and Verification. We invite contributions spanning architectures and inductive biases for scientific structure, data curation and scalable learning, retrieval- and tool-grounded modeling with traceable provenance, and rigorous verification protocols including calibration, constraint checking, stress testing, and reproducibility-oriented benchmarks. The workshop aims to bring together AI researchers and domain scientists to distill shared challenges, surface best practices, and produce community artifacts such as benchmark specifications and evaluation checklists for trustworthy SciFMs.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.