AI RESEARCH
Semantic Reward Collapse and the Preservation of Epistemic Integrity in Adaptive AI Systems
arXiv CS.AI
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ArXi:2605.12406v1 Announce Type: new 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, sycophancy, and suppression of visible uncertainty suggest unresolved structural issues within scalarized preference optimization systems.