The Adversarial Loop: A Cross Disciplinary Method for High Rigor Human AI Reasoning

Adversarial reasoning has become an important component of Large Language Model research, particularly in multi agent debate, robustness evaluation, and red teaming. However, existing work focuses almost exclusively on model to model adversarial dynamics or on adversarial attacks used to evaluate model safety. This paper introduces a human centered adversarial method, the adversarial loop, designed to increase the rigor, stability, and verifiability of knowledge produced in collaboration with LLMs. The loop consists of five phases: generative structuring, adversarial interrogation, structural repair, re interrogation, and verification. Unlike model centric adversarial frameworks, the adversarial loop positions the human researcher as the primary generator, interrogator, and verifier of claims. The method is domain agnostic and applicable across anthropology, computer science, OSINT, art history, archaeology, and other fields where arguments must withstand scrutiny. This paper situates the adversarial loop within existing adversarial reasoning literature, defines the method formally, and outlines its epistemic rationale and cross disciplinary applicability.