False Fixed Points: Kantian Feedback, Stable Miscalibration, and Representational Compression in LLMs
arXiv:2510.14925v4 Announce Type: replace-cross 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 item
The accelerating deployment and integration of LLMs necessitates a deeper understanding of their failure modes beyond simplistic 'fragile errors,' especially as they are tasked with increasingly critical functions.
Understanding 'false fixed points' in LLMs reveals that high-confidence errors can be stable and internally coherent, highlighting a fundamental challenge to robustness and truth-tracking in AI systems.
The conventional view of LLM errors as easily correctable 'fragile failures' is nuanced by the concept of stable miscalibration, requiring new approaches to AI safety, validation, and explainability.
- · AI safety researchers
- · LLM evaluators and auditors
- · Developers of robust AI systems
- · Applications reliant on unverified LLM outputs
- · Simple 'trust but verify' approaches to AI
- · AI development shortcuts ignoring fundamental error modes
This research provides a theoretical framework for understanding persistent, high-confidence errors in large language models.
It will drive the development of new diagnostic tools and mitigation strategies to address stable miscalibration in AI systems.
The concept of 'false fixed points' could lead to a rethinking of AI alignment and the inherent limitations of current model architectures.
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Read at arXiv cs.CL