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

Akira Okutomi

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arXiv · 2510.14925

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

Akira Okutomi

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Abstract

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. Abstention-aware self-critique reduces overconfident wrong commitments by sacrificing coverage, and C3-R, a rule-based explicit feedback gate, sharpens that tradeoff rather than eliminating it. These results motivate, but do not establish, high signal-to-noise (high-SNR) inertia and representational compression as possible mechanisms for stable miscalibration.

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Akira Okutomi. 2025-10-16. False Fixed Points: Kantian Feedback, Stable Miscalibration, and Representational Compression in LLMs. https://arxiv.org/abs/2510.14925

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