AI for science needs reasoning, not just data

While large language models are increasingly used to digest scientific data, researchers argue that current systems lack the causal reasoning necessary to achieve genuine breakthroughs. Future advancements in scientific AI will require architectures that prioritize logical inference over simple pattern recognition to move beyond summarizing existing knowledge.