What Can Large Language Models Discover from Experiments? Scientific Claims and Discovery Value across Multiple Worlds

Large language models are increasingly used as scientific agents, yet successful prediction alone cannot establish whether they infer mechanisms or merely fit observations. We investigate this distinction through a controlled multiworld framework for mechanism identification, active experimental discrimination, and conditional deduction beyond observed data. The environment spans ten physics topics and thirty synthetic worlds: worlds within each topic share experimental interfaces but differ in hidden generative rules. Models select experiments, commit to predictions before receiving feedback, and state the assumptions and limits of their conclusions. We analyze 90 final answers from GPT-6, Claude, and Gemini together with their experimental trajectories. Our evaluation separates empirical, mechanistic, and deductive evidence along six claim-level dimensions and relates synthetic rules to established physics, distinguishing mechanistic correspondences, effective approximations, and counterfactual constructions. The results reveal meaningful but bounded achievements. In stochastic motion, calibrated temporal statistics and force interventions constrain hidden dynamics without uniquely identifying them. In lattice systems, closed-path invariants support a complete four-class classification under explicit assumptions, although the apparatus prepares only two classes. Across models, valid local findings coexist with unsupported or falsified stronger explanations. These results support within-task reconstruction and conditional mathematical discovery, but do not establish reliable autonomous discovery or previously unknown physical laws. Evaluating machine scientific discovery therefore requires jointly examining experimental discrimination, generalizable implications, and validity boundaries rather than accuracy or plausibility alone.