---
title: "Riemannian Geometry for Pre-trained Language Model Embeddings — Stuff That Spins"
description: "arXiv:2607.07047v1 Announce Type: new Abstract: Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:03:47.886611+00:00"
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# Riemannian Geometry for Pre-trained Language Model Embeddings

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.07047  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.07047v1 Announce Type: new Abstract: Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and safety. We ask whether sentence-level classification signal lives in the Riemannian geometry of contextual token embeddings, and probe it by extracting per-token pullback metrics from a learned encoder's analytical Jacobian and aggregating them with the Fr\'echet mean on the symmetric positive definite (SPD) manifold; we call this procedu

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