arXiv Machine Learning July 31, 2026 By Seonglae Cho, Zekun Wu, Kleyton Da Costa, Adriano Koshiyama

The Confidence Manifold: Geometric Structure of Correctness Representations in Language Models

Read the original on arXiv Machine Learning →

arXiv:2602. 08159v2 Announce Type: replace Abstract: When a language model asserts that "the capital of Australia is Sydney," does it know this is wrong?

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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arXiv AI

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arXiv AI

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arXiv Machine Learning

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arXiv AI

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Unsupervised Features Mining via Activation Geometry

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Latent Fact-Checking: Detecting Misinformation through Activation Engineering

MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models

From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

The Anatomy of a Truth Direction: Knowledge-Dependent Dimensionality, a Relational Law, and a Convergent Category Geometry in Small Language Models

Unsupervised Features Mining via Activation Geometry

The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMs