Riemannian Geometry for Pre-trained Language Model Embeddings | Paper Citation Record

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This paper cites Understanding intermediate layers using linear classifier probes.

Riemannian Geometry for Pre-trained Language Model Embeddings Understanding intermediate layers using linear classifier probes

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Riemannian Geometry for Pre-trained Language Model Embeddings Unresolved cited work

Reference 2

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This paper cites Latent Space Oddity: on the Curvature of Deep Generative Models.

Riemannian Geometry for Pre-trained Language Model Embeddings Latent Space Oddity: on the Curvature of Deep Generative Models

Reference 4

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This paper cites Barachant et al.,Pyriemann, version 0.11, 2026.

Riemannian Geometry for Pre-trained Language Model Embeddings Barachant et al.,Pyriemann, version 0.11, 2026

Reference 5

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Reference 8

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This paper cites Riemannian batch normalization for SPD neural networks.

Riemannian Geometry for Pre-trained Language Model Embeddings Riemannian batch normalization for SPD neural networks

Reference 9

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This paper cites RMLR: Extending Multinomial Logistic Regression into General Geometries.

Riemannian Geometry for Pre-trained Language Model Embeddings RMLR: Extending Multinomial Logistic Regression into General Geometries

Reference 10

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This paper cites Emergence of a High-Dimensional Abstraction Phase in Language Transformers.

Riemannian Geometry for Pre-trained Language Model Embeddings Emergence of a High-Dimensional Abstraction Phase in Language Transformers

Reference 11

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This paper cites The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark.

Riemannian Geometry for Pre-trained Language Model Embeddings The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark

Reference 12

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This paper cites Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning.

Riemannian Geometry for Pre-trained Language Model Embeddings Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning

Reference 13

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This paper cites Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review.Brain-Computer Interfaces, 4(3):155–174.

Riemannian Geometry for Pre-trained Language Model Embeddings Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review.Brain-Computer Interfaces, 4(3):155–174

Reference 14

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This paper cites Truth as a trajectory: What internal representations reveal about large language model reasoning.

Riemannian Geometry for Pre-trained Language Model Embeddings Truth as a trajectory: What internal representations reveal about large language model reasoning

Reference 15

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Riemannian Geometry for Pre-trained Language Model Embeddings BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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This paper cites The curved spacetime of transformer architectures.arXiv preprint arXiv:2511.03060.

Riemannian Geometry for Pre-trained Language Model Embeddings The curved spacetime of transformer architectures.arXiv preprint arXiv:2511.03060

Reference 17

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This paper cites Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows.

Riemannian Geometry for Pre-trained Language Model Embeddings Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows

Reference 18

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This paper cites How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.

Riemannian Geometry for Pre-trained Language Model Embeddings How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings

Reference 19

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This paper cites IEEE Transactions on Medical Imaging , author =.

Riemannian Geometry for Pre-trained Language Model Embeddings IEEE Transactions on Medical Imaging , author =

Reference 20

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Reference 21

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Reference 22

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This paper cites Language Models Represent Space and Time.

Riemannian Geometry for Pre-trained Language Model Embeddings Language Models Represent Space and Time

Reference 23

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Riemannian Geometry for Pre-trained Language Model Embeddings Unresolved cited work

Reference 24

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Riemannian Geometry for Pre-trained Language Model Embeddings Unresolved cited work

Reference 25

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This paper cites Ji.RiemannFormer: A Framework for Attention in Curved Spaces.

Riemannian Geometry for Pre-trained Language Model Embeddings Ji.RiemannFormer: A Framework for Attention in Curved Spaces

Reference 26

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This paper cites SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges, January 2026.

Riemannian Geometry for Pre-trained Language Model Embeddings SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges, January 2026

Reference 27

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Observation 60188531-584e-4dd6-84fd-0b0691c2a0b6 · outbound

This paper cites Measuring Intrinsic Dimension of Token Embeddings.

Riemannian Geometry for Pre-trained Language Model Embeddings Measuring Intrinsic Dimension of Token Embeddings

Reference 28

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Riemannian Geometry for Pre-trained Language Model Embeddings Unresolved cited work

Reference 29

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This paper cites Curvature-aware Manifold Learning.

Riemannian Geometry for Pre-trained Language Model Embeddings Curvature-aware Manifold Learning

Reference 30

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This paper cites Differentiating through the Fr\'echet Mean.

Riemannian Geometry for Pre-trained Language Model Embeddings Differentiating through the Fr\'echet Mean

Reference 31

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This paper cites Curved Inference: Concern-Sensitive Geometry in Large Language Model Residual Streams.

Riemannian Geometry for Pre-trained Language Model Embeddings Curved Inference: Concern-Sensitive Geometry in Large Language Model Residual Streams

Reference 32

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This paper cites Journal of Neuroscience Methods164(1), 177–190 (2007).

Riemannian Geometry for Pre-trained Language Model Embeddings Journal of Neuroscience Methods164(1), 177–190 (2007)

Reference 33

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This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Riemannian Geometry for Pre-trained Language Model Embeddings The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 34

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Riemannian Geometry for Pre-trained Language Model Embeddings UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 35

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This paper cites The geometry of truth: Layer-wise semantic dynamics for hallucination detection in large language models.

Riemannian Geometry for Pre-trained Language Model Embeddings The geometry of truth: Layer-wise semantic dynamics for hallucination detection in large language models

Reference 36

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Riemannian Geometry for Pre-trained Language Model Embeddings Unresolved cited work

Reference 37

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This paper cites Poincar\'e Embeddings for Learning Hierarchical Representations.

Riemannian Geometry for Pre-trained Language Model Embeddings Poincar\'e Embeddings for Learning Hierarchical Representations

Reference 38

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Observation d14198f5-2ad5-473d-87b3-f071d6e3f489 · outbound

This paper cites Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry.

Riemannian Geometry for Pre-trained Language Model Embeddings Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

Reference 39

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This paper cites CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge.

Riemannian Geometry for Pre-trained Language Model Embeddings CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge

Reference 40

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This paper cites The Geometry of Categorical and Hierarchical Concepts in Large Language Models.

Riemannian Geometry for Pre-trained Language Model Embeddings The Geometry of Categorical and Hierarchical Concepts in Large Language Models

Reference 41

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This paper cites The Linear Representation Hypothesis and the Geometry of Large Language Models.

Riemannian Geometry for Pre-trained Language Model Embeddings The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.244246Z

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This paper cites Pedregosa, G.

Riemannian Geometry for Pre-trained Language Model Embeddings Pedregosa, G

Reference 43

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Observation 04187d1f-2cdd-44ae-b5ec-efc101a1d5e6 · outbound

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Riemannian Geometry for Pre-trained Language Model Embeddings Unresolved cited work

Reference 44

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This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Riemannian Geometry for Pre-trained Language Model Embeddings Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.164121Z

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Observation 887e0efa-e7ad-4f4c-b316-817dfb36d381 · outbound

This paper cites Token embeddings violate the manifold hypothesis.arXiv preprint arXiv:2504.01002.

Riemannian Geometry for Pre-trained Language Model Embeddings Token embeddings violate the manifold hypothesis.arXiv preprint arXiv:2504.01002

Reference 46

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Observation 6b77cb11-b8b3-42fa-a8a1-a7a449e2cb11 · outbound

This paper cites A Primer in BERTology: What we know about how BERT works.

Riemannian Geometry for Pre-trained Language Model Embeddings A Primer in BERTology: What we know about how BERT works

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.220963Z

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Observation 7e8c3432-5c5b-4068-8a4e-81b8daf6c1a6 · outbound

This paper cites Representation Tradeoffs for Hyperbolic Embeddings.

Riemannian Geometry for Pre-trained Language Model Embeddings Representation Tradeoffs for Hyperbolic Embeddings

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.236587Z

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Observation 76231bdd-10f1-4cc7-85e8-e9da0aafcdc0 · outbound

This paper cites Towards Debiasing Fact Verification Models.

Riemannian Geometry for Pre-trained Language Model Embeddings Towards Debiasing Fact Verification Models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.211092Z

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Observation d0e421b2-0cc2-45a0-a560-832964ee3d27 · outbound

This paper cites From graph to manifold Laplacian: the convergence rate.Applied and Computa- tional Harmonic Analysis, 21(1):128–134.

Riemannian Geometry for Pre-trained Language Model Embeddings From graph to manifold Laplacian: the convergence rate.Applied and Computa- tional Harmonic Analysis, 21(1):128–134

Reference 50

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Observation 319461e8-2a1a-429a-8c8e-c66154322a08 · outbound

This paper cites Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models.

Riemannian Geometry for Pre-trained Language Model Embeddings Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models

Reference 51

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Observation c51e3263-944c-4cd8-969a-4505b4a41f12 · outbound

This paper cites Mechanistic Decomposition of Sentence Representations.

Riemannian Geometry for Pre-trained Language Model Embeddings Mechanistic Decomposition of Sentence Representations

Reference 52

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Observation e470a26e-a10a-4b4d-98e9-ee44bd8b9a1a · outbound

This paper cites FEVER: a large-scale dataset for Fact Extraction and VERification.

Riemannian Geometry for Pre-trained Language Model Embeddings FEVER: a large-scale dataset for Fact Extraction and VERification

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.244501Z

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Observation e0c7e869-80a5-4c0a-b917-84c11321def0 · outbound

This paper cites The geometry of hidden representations of large transformer models.

Riemannian Geometry for Pre-trained Language Model Embeddings The geometry of hidden representations of large transformer models

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.195918Z

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Observation 9138b701-1159-4669-b278-eb44e32578b0 · outbound

This paper cites Proceedings of the 2018.

Riemannian Geometry for Pre-trained Language Model Embeddings Proceedings of the 2018

Reference 55

Resolution: verified exact; doi, observed 2026-07-09T21:16:34.064030Z

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Observation 3a8cd40f-d486-4e07-9006-bd4e5cf6934e · outbound

This paper cites Neural Network Acceptability Judgments.

Riemannian Geometry for Pre-trained Language Model Embeddings Neural Network Acceptability Judgments

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.238910Z

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