Keeping pace with the rapidly expanding body of scientific research is a growing challenge for researchers, as millions of new papers are published annually. While artificial intelligence holds promise for efficiently processing and summarizing vast amounts of information, many existing models struggle with accuracy—often generating false or fabricated details, a phenomenon known as "hallucination." A new AI model called OpenScholar is changing that. Designed specifically for scientific literature, OpenScholar excels at synthesizing complex research across disciplines while accurately citing sources. In tests, it has demonstrated performance on par with human experts in identifying relevant studies, extracting key findings, and attributing information correctly. Unlike many general-purpose AI systems, OpenScholar is trained on a curated dataset of peer-reviewed scientific papers, enabling it to understand technical language, recognize proper citation formats, and distinguish between established facts and speculative claims. Its architecture includes mechanisms to verify references and cross-check claims against original sources, significantly reducing the risk of hallucination. Researchers using OpenScholar can ask it to summarize recent breakthroughs in a field, compare different approaches to a problem, or identify gaps in the literature—all with traceable, reliable citations. Early adopters report that the tool not only saves time but also improves the rigor of their literature reviews. The model’s success highlights a shift toward specialized AI systems built for scientific work, where accuracy and credibility are paramount. As research accelerates across fields like medicine, climate science, and materials engineering, tools like OpenScholar could become essential for ensuring that scientific progress is built on solid, verifiable foundations.