Detecting Research Gaps with LLMs

The acceleration of research publication is reaching a historical saturation point. As of 2026, millions of scholarly papers are published annually, making it humanly impossible for even the most dedicated researcher to maintain a holistic view of several fields.

The Information Horizon

How many times has a researcher embarked on a three-year PhD project only to find, halfway through, that a team in Zurich or Singapore had already published the same findings three years prior—buried under a slightly different keyword or within a different discipline?

"The biggest research risk today is not failure—it's redundant discovery."

Semantic Synthesis: Beyond Keywords

Traditional literature review relies on keyword matching. If you're researching "distributed ledger efficiency" but someone else calls it "decentralized transaction consensus optimization," you will miss it. Large Language Models (LLMs) solve this by mapping research into a vibrant semantic vector space.

Hiatus uses a proprietary synthesis agent that doesn't just read summaries—it maps the logical claims of each paper. When these claim structures fail to connect, or when they contradict each other without rebuttal, the 'gap' is automatically flagged. These are the frontiers of science.