AI Research Shows Outcome Evidence More Reliable Than Experiment Descriptions for Scientific Feasibility Assessment

A recent study available on arXiv (2604.18786v1) explores how large language models (LLMs) evaluate the scientific plausibility of claims, which is defined as their consistency with established knowledge and the potential for evidence to support or refute them. This research presents the evaluation as a diagnostic reasoning challenge, where models predict feasibility and provide justifications for their conclusions. Assessments were carried out on various LLMs using two datasets under controlled knowledge scenarios: hypothesis-only, with experimental data, with outcomes, or a combination of both. Results indicate that evidence from outcomes generally leads to more accurate evaluations compared to descriptions of experiments, while the latter can sometimes hinder performance in cases of incomplete context. The study methodically examines the robustness of these findings by systematically removing elements of experimental and outcome information, clarifying the conditions under which experimental evidence enhances LLM feasibility assessments and illustrating the varying effects of different types of evidence on model efficacy.