False Fixed Points: Kantian Feedback, Stable Miscalibration, and Representational Compression in LLMs

ArXi:2510.14925v4 Announce Type: replace High-confidence errors in large language models are often treated as fragile failures. We study an alternative: some errors may be false fixed points, locally stable, internally coherent, and confidently wrong. This separates robustness from truth-tracking. We develop the separation through a Kantian commitment-gate framing and a minimal linear feedback model in which stability and correctness can diverge.