Hallucination as a Latent Space Signal: Knowledge Boundary Detection and Cross-Model Routing via Latent Vectors

A conceptual note on interpreting LLM hallucination as a latent-space boundary signal rather than mere error. The work introduces the idea of hallucination as a “gradient of ignorance” and sketches an experimental protocol for boundary detection and cross-model routing via latent vectors.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-10
DOI
https://doi.org/10.5281/zenodo.20102211
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Hallucination as a Latent Space Signal: Knowledge Boundary Detection and Cross-Model Routing via Latent Vectors

Pavel Scurin
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Hallucination as a Latent Space Signal: Knowledge Boundary Detection and Cross-Model Routing via Latent Vectors

Pavel Scurin
article en

Abstract

A conceptual note on interpreting LLM hallucination as a latent-space boundary signal rather than mere error. The work introduces the idea of hallucination as a “gradient of ignorance” and sketches an experimental protocol for boundary detection and cross-model routing via latent vectors.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Openalex Percentile: Top 3%
Adversarial Robustness in Machine Learning
DOIOpen Access PDFOpenAlex
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Hallucination as a Latent Space Signal: Knowledge Boundary Detection and Cross-Model Routing via Latent Vectors — Pavel Scurin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS