Semantic Reward Collapse and the Preservation of Epistemic Integrity in Adaptive AI Systems

Recent advances in reinforcement learning from human feedback (RLHF) and preference optimization have substantially improved the usability, coherence, and safety of large language models. However, recurring behaviors such as performative certainty, hallucinated continuity, calibration drift, sycopha...