Scientific Reasoning in Language Models

The field studies whether language models can do more than recall scientific facts: interpret data, connect evidence, design tests, explain mechanisms, and generate useful hypotheses. Common approaches include chain-of-thought and structured reasoning traces, retrieval and tool use, multi-agent collaboration, and training with expert examples, simulators, or reinforcement learning with verifiable rewards. Evaluation spans scientific question answering, mathematics and physics problems, papers and tables, multimodal data, and interactive experimental workflows. Models are improving on specialized tasks, but reliable evidence use, robust execution, and mechanistic understanding remain difficult, and correct answers do not always show that a model reasoned scientifically.