False Fixed Points: Kantian Feedback, Stable Miscalibration, and Representational Compression in LLMs
Learn how false fixed points in LLMs can lead to stable miscalibration and representational compression, and how to identify and address these issues using Kantian feedback and minimal linear feedback models
- Apply Kantian commitment-gate framing to identify potential false fixed points in LLMs
- Run minimal linear feedback models to analyze stability and correctness in LLMs
- Configure models to prioritize truth-tracking over robustness
- Test for stable miscalibration and representational compression in LLMs
- Compare performance of models with and without false fixed point mitigation
AI researchers and engineers working with large language models can benefit from understanding false fixed points and how to mitigate their effects, ensuring more accurate and reliable model performance
💡 False fixed points in LLMs can be locally stable and internally coherent, yet confidently wrong, highlighting the need to separate robustness from truth-tracking
🚨 False fixed points in LLMs can lead to stable miscalibration and representational compression! 🤖 Learn how to identify and address these issues using Kantian feedback and minimal linear feedback models #LLMs #AI
Key Takeaways
Learn how false fixed points in LLMs can lead to stable miscalibration and representational compression, and how to identify and address these issues using Kantian feedback and minimal linear feedback models
Full Article
Abstract:
arXiv:2510.14925v4 Announce Type: replace 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, overc
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