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These results motivate, but do not establish, high signal-to-noise (high-SNR) inertia and representational compression as possible mechanisms for stable miscalibratio","title":"False Fixed Points: Kantian Feedback, Stable Miscalibration, and Representational Compression in LLMs","url":"https://arxiv.org/abs/2510.14925","vendor":"arxiv_cs_ai"},"summary":"arXiv:2510.14925v4 Announce Type: replace \nAbstract: 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. Across three open-weight models, overconfident wrong items are not systematically more locally fragile than confidently correct items under our hidden-state sensitivity probes. 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