Author shows non-monotonic LLM reasoning in context order experiments

The author argues that large language models exhibit non-monotonic reasoning because their entire context acts as a single premise set, meaning adding or reordering information can unpredictably alter outputs. Because models lack a rulebook for retraction, deterministic harnesses such as independent test runners and human-reviewed specifications are required to enforce reliable software behavior. Open article →