arXiv Machine Learning August 13, 2026 By Kiyotaka Kasubuchi, Kazuo Fukiya

WavePhaseNet: A DFT-Based Method for Constructing Semantic Conceptual Hierarchy Structures (SCHS)

Read the original on arXiv Machine Learning →

arXiv:2602. 14419v2 Announce Type: cross Abstract: This paper reformulates Transformer/Attention mechanisms in Large Language Models (LLMs) through measure theory and frequency analysis, theoretically demonstrating that hallucination is an inevitable structural limitation.

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

arXiv Machine Learning

Jul 21

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

arXiv:2607. 16741v1 Announce Type: new Abstract: B\"urger et al.

By Francesco Karim Vicidomini

llms

arXiv AI

Jun 3

Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization

arXiv:2606. 03022v1 Announce Type: cross Abstract: Hallucination in Large Language Models (LLMs), characterized by the generation of content inconsistent with contextual facts or logical constraints -- remains a persistent challenge for reliable deployment.

By Mingkuan Zhao, Wentao Hu, Tianchen Huang, Yuheng Min, Suquan Chen, Yide Gao, Yanbo Zhai, Shuangyong Song, Xuelong Li

llms benchmarks safety

arXiv Machine Learning

Jul 14

One mechanism for many mental spaces: a shared router over a value slot in language models

arXiv:2607. 10248v1 Announce Type: cross Abstract: Language builds discourse contexts other than the actual: a painting, a belief, a memory, a hypothetical.

By Oliver Steele, Jiangtao Wen, Yuxing Han

llms safety

arXiv AI

Jul 14

Laguerre Geometry for Interpreting Large Language Models

arXiv:2607. 10578v1 Announce Type: new Abstract: Existing hypotheses represent a concept in an LLM as a single point, a linear direction, or a Gaussian cluster, yet it remains unclear how and why such structures emerge.

By Chunwei Ma, Russell Wolfinger

llms safety

arXiv Machine Learning

Jun 19

Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models

arXiv:2606. 19404v1 Announce Type: new Abstract: Hallucination detection in large language models (LLMs) is deployment-critical, and recent work shows that the spectrum of attention-derived graph Laplacians carries strong signal about reasoning quality.

By Salim Khazem

llms benchmarks safety

arXiv AI

Jul 28

D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

arXiv:2607. 24586v1 Announce Type: cross Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model.

By Bianca Raimondi, Davide Evangelista, Maurizio Gabbrielli, Elena Loli Piccolomini

llms safety

Related stories

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

Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization

One mechanism for many mental spaces: a shared router over a value slot in language models

Laguerre Geometry for Interpreting Large Language Models

Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models

D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models