Long conversations with large language models degrade in reasoning quality over time. Extending context windows has not solved this problem. This paper proposes the Balloon Architecture — a sequential chain of independent augmentation units attached to the LLM's extended thinking layer that continuously identifies gaps in the model's reasoning, fills those gaps through parallel targeted retrieval, and injects corrections into the backend reasoning state between outputs. The architecture introduces three core original mechanisms: CARA (Contextual Adversarial Reasoning Auditor), a user-profile-driven adversarial challenge mechanism; similarity-triggered passive trickle injection; and frequency-threshold earned memory solidification. This is a theoretical architectural proposal with a defined validation roadmap. No experiments are reported