The LLT Lab empirically investigates the argument structures actually found in a sample of decisions selected from the U.S. Court of Federal Claims. These decisions adjudicate claims for compensation for injuries due to vaccines in the United States. Traditional formal logic proves inadequate because it can represent very little of the natural language patterns found in actual decisions, and it does not provide useful heuristics for interpreting actual sentences in text as elements of argument structures. The Lab addresses both of these problems by proceeding from actual texts to develop patterns that explain all or most of the text. In the case of vaccine-injury compensation decisions, we find useful patterns not only characterized by the nature of the inference (deductive vs. probabilistic), but also characterized by types of evidence (e.g., legal precedent, legal policy, medical or scientific study) and by types of evidentiary discrepancies (e.g., expert vs. expert, fact witness vs. contemporaneous documentation). Of particular interest in this dataset is the reasoning based on the policies adopted by Congress in enacting the governing statute.