Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance | Paper Citation Record

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This paper cites NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations , year=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations , year=

Reference 1

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Observation 707c9492-9ecb-4b65-b14d-b9558a6f6ff3 · outbound

This paper cites Exploring Dimensionality Reduction Techniques in Multilingual Transformers.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Exploring Dimensionality Reduction Techniques in Multilingual Transformers

Reference 4

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This paper cites 2023 , eprint=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2023 , eprint=

Reference 6

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This paper cites 2025 , eprint=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2025 , eprint=

Reference 7

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This paper cites Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test

Reference 8

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This paper cites Independent component analysis , year =.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Independent component analysis , year =

Reference 11

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This paper cites Independent component analysis: Algorithms and applica- tions.Neural Networks, 13(4–5):411–430.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Independent component analysis: Algorithms and applica- tions.Neural Networks, 13(4–5):411–430

Reference 12

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This paper cites Fast and robust fixed-point algorithms for independent component analysis , year=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Fast and robust fixed-point algorithms for independent component analysis , year=

Reference 13

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Observation d6ea914c-d0ab-409c-80d2-5ef548067cea · outbound

This paper cites Scikit-learn: Machine Learning in.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Scikit-learn: Machine Learning in

Reference 14

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Observation eee6a4ed-7948-404c-8faf-d7989dbd9fd1 · outbound

This paper cites and Ames, K.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance and Ames, K

Reference 16

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This paper cites The Thirteenth International Conference on Learning Representations , year=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The Thirteenth International Conference on Learning Representations , year=

Reference 19

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Observation 0fd5eed5-2d2d-46a2-9247-6c863bfab5df · outbound

This paper cites Think you have solved question answering?.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Think you have solved question answering?

Reference 20

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Observation 83005573-285d-40ad-9943-898afacb4157 · outbound

This paper cites RAR-b: Reasoning as Retrieval Benchmark , year =.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance RAR-b: Reasoning as Retrieval Benchmark , year =

Reference 21

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This paper cites The Third.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The Third

Reference 22

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This paper cites Tatoeba: Collection of sentences and translations , year =.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Tatoeba: Collection of sentences and translations , year =

Reference 24

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This paper cites Forty-second International Conference on Machine Learning , year=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Forty-second International Conference on Machine Learning , year=

Reference 27

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This paper cites The Thirteenth International Conference on Learning Representations , year=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The Thirteenth International Conference on Learning Representations , year=

Reference 28

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

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Sentence- BERT : Sentence Embeddings using Siamese BERT -Networks

Reference 31

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This paper cites 2019 , eprint=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2019 , eprint=

Reference 32

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This paper cites Second Conference on Language Modeling , year=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Second Conference on Language Modeling , year=

Reference 33

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This paper cites Group information guided ICA for fMRI data analysis , journal =.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Group information guided ICA for fMRI data analysis , journal =

Reference 34

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This paper cites Tanskanen and Jarno E.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Tanskanen and Jarno E

Reference 35

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This paper cites Linguistic Regularities in Continuous Space Word Representations.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Linguistic Regularities in Continuous Space Word Representations

Reference 36

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This paper cites 2022 , eprint=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2022 , eprint=

Reference 37

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This paper cites 2023 , eprint=.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2023 , eprint=

Reference 38

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This paper cites Proceedings of the 41st International Conference on Machine Learning , articleno =.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Proceedings of the 41st International Conference on Machine Learning , articleno =

Reference 39

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Transactions on Machine Learning Research , issn=

Reference 41

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The Twelfth International Conference on Learning Representations , year=

Reference 42

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This paper cites most-common-words-by-language.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance most-common-words-by-language

Reference 43

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This paper cites A Text is Worth Several Tokens: Text Embedding from LLM s Secretly Aligns Well with The Key Tokens.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance A Text is Worth Several Tokens: Text Embedding from LLM s Secretly Aligns Well with The Key Tokens

Reference 44

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Pitfalls in the Evaluation of Sentence Embeddings

Reference 46

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This paper cites The Limitations of Cross-language Word Embeddings Evaluation.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The Limitations of Cross-language Word Embeddings Evaluation

Reference 47

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Second Conference on Language Modeling , year=

Reference 52

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This paper cites A Deep Dive into Multi-Head Attention and Multi-Aspect Embedding.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance A Deep Dive into Multi-Head Attention and Multi-Aspect Embedding

Reference 53

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2025 , eprint=

Reference 54

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This paper cites Proceedings of The 33rd International Conference on Machine Learning , pages =.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Proceedings of The 33rd International Conference on Machine Learning , pages =

Reference 57

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This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 58

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This paper cites C-Pack: Packed Resources For General Chinese Embeddings.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance C-Pack: Packed Resources For General Chinese Embeddings

Reference 59

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2023 , eprint=

Reference 60

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2023 , eprint=

Reference 61

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Multilingual

Reference 62

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Model2Vec: Fast State-of-the-Art Static Embeddings , year =

Reference 65

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2025 , eprint=

Reference 66

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Unresolved cited work

Reference 68

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2025 , eprint=

Reference 70

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2024 , eprint=

Reference 71

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance 2025 , eprint=

Reference 73

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This paper cites Towards identification of latent structures in language embeddings.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Towards identification of latent structures in language embeddings

Reference 76

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This paper cites SCDT our: Embedding axis ordering and merging for interpretable semantic change detection.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance SCDT our: Embedding axis ordering and merging for interpretable semantic change detection

Reference 77

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Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Granite Embedding Models

Reference 78

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This paper cites Llama-embed-nemotron-8b: A universal text embedding model for multilingual and cross-lingual tasks.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Llama-embed-nemotron-8b: A universal text embedding model for multilingual and cross-lingual tasks

Reference 79

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This paper cites Chang, Zhuowen Tu, and Benjamin K.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Chang, Zhuowen Tu, and Benjamin K

Reference 80

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Observation f76c89c5-52a6-45f2-a62d-4beffcd54b04 · outbound

This paper cites BGE M3 -embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance BGE M3 -embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.851755Z

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Observation 33c65767-6743-4a56-875d-c23f341c6156 · outbound

This paper cites The knowledge microscope: Features as better analytical lenses than neurons.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The knowledge microscope: Features as better analytical lenses than neurons

Reference 82

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.300519Z

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Observation f1ce1b40-6134-4893-ab7e-61017bfcc4f1 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.755258Z

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Observation a405c497-1a83-41e1-8dae-bb70540bd581 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.759907Z

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Observation e52897b0-c7e2-47b2-b190-9c642d5f5d40 · outbound

This paper cites Analyzing transformers in embedding space.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Analyzing transformers in embedding space

Reference 85

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.292406Z

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Observation c919bbc9-1b69-49bd-9cab-512ad5543de1 · outbound

This paper cites WebFAQ : A multilingual collection of natural Q&A datasets for dense retrieval.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance WebFAQ : A multilingual collection of natural Q&A datasets for dense retrieval

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.297613Z

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Observation 0bbf2426-483d-462d-82f9-b2f8aeada5f2 · outbound

This paper cites Toy Models of Superposition.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Toy Models of Superposition

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.776251Z

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Observation 715805e1-0211-4d42-a8c9-2aedf64d8904 · outbound

This paper cites O mer Veysel C a g atan, Akash Kundu, Martin Bernstorff, Shitao Xiao, Akshita Sukhlecha, Bhavish Pahwa, Rafa Po \'s wiata, Kranthi Kiran GV, Shawon Ashraf, Daniel Auras, Bj \.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance O mer Veysel C a g atan, Akash Kundu, Martin Bernstorff, Shitao Xiao, Akshita Sukhlecha, Bhavish Pahwa, Rafa Po \'s wiata, Kranthi Kiran GV, Shawon Ashraf, Daniel Auras, Bj \

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.892547Z

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Observation 3ff695f3-95ea-4e7d-a25f-edec46081da5 · outbound

This paper cites Fabbri, Wojciech Kry \'s ci \'n ski, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Fabbri, Wojciech Kry \'s ci \'n ski, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev

Reference 89

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.317404Z

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Observation d5d01a3d-925e-4f21-8a2d-9ca9b623b9d5 · outbound

This paper cites Language-agnostic BERT Sentence Embedding.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Language-agnostic BERT Sentence Embedding

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.845919Z

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Observation adaa36dc-0722-4e33-9647-eca01e51278d · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Gemini: A Family of Highly Capable Multimodal Models

Reference 91

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.768284Z

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Observation 5f1dad7e-19c1-4960-a613-d5486ca841b1 · outbound

This paper cites The third PASCAL recognizing textual entailment challenge.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The third PASCAL recognizing textual entailment challenge

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.835027Z

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Observation 41cde4b7-9228-4782-9f5b-69f69aa8c0f9 · outbound

This paper cites Language models represent space and time.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Language models represent space and time

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.840197Z

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Observation 3bd913f5-74d9-48cb-894a-a43f9323276f · outbound

This paper cites Finding neurons in a haystack: Case studies with sparse probing.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Finding neurons in a haystack: Case studies with sparse probing

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.934682Z

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Observation 0aedd0da-c7e4-461d-9054-9b2839e9bedb · outbound

This paper cites Jina embeddings: A novel set of high-performance sentence embedding models, 2023 a.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Jina embeddings: A novel set of high-performance sentence embedding models, 2023 a

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.937333Z

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Observation 92ba59e0-8afe-4423-b606-59c4033d988a · outbound

This paper cites Jina embeddings 2: 8192-token general-purpose text embeddings for long documents, 2023 b.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Jina embeddings 2: 8192-token general-purpose text embeddings for long documents, 2023 b

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.932123Z

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Observation 126e94f5-18f2-4f8d-960b-afc32e22fc85 · outbound

This paper cites Validating the independent components of neuroimaging time series via clustering and visualization.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Validating the independent components of neuroimaging time series via clustering and visualization

Reference 97

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.251743Z

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Observation a6a72cb1-5f44-49b0-98cc-bac044360334 · outbound

This paper cites KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.837100Z

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Observation a0fc749e-4f0d-474d-aad0-2d5340cb8986 · outbound

This paper cites Embedding-based retrieval in facebook search.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Embedding-based retrieval in facebook search

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.279008Z

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Observation a484dcd3-9fe5-4788-a446-b2aaf83ac54e · outbound

This paper cites Exploring dimensionality reduction techniques in multilingual transformers.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Exploring dimensionality reduction techniques in multilingual transformers

Reference 100

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.303951Z

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Observation 21ad70d6-dcb4-4892-b3de-3eaec6c1f3fc · outbound

This paper cites Fast and robust fixed-point algorithms for independent component analysis.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Fast and robust fixed-point algorithms for independent component analysis

Reference 101

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.340536Z

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Observation 015403d3-8eb0-4f5e-a8d1-7fbacc01dcf9 · outbound

This paper cites Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders

Reference 102

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.841953Z

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Observation bdd8ed60-8630-4427-b868-d759eae1e36c · outbound

This paper cites Shared global and local geometry of language model embeddings.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Shared global and local geometry of language model embeddings

Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.926200Z

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Observation f6bddfe9-805b-4ec8-bdbc-0b12796eccc2 · outbound

This paper cites Exploring intra and inter-language consistency in embeddings with ICA.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Exploring intra and inter-language consistency in embeddings with ICA

Reference 104

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.254874Z

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Observation cc12fc95-60d3-4ace-9fce-68ff8954ca94 · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 105

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.828271Z

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Observation a202d799-5380-4988-8557-54e6c744e35c · outbound

This paper cites Situating Sentence Embedders with Nearest Neighbor Overlap.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Situating Sentence Embedders with Nearest Neighbor Overlap

Reference 106

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.833029Z

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Observation bbf20b46-91bc-44ca-898c-68e7dde32670 · outbound

This paper cites Pruning Large Language Models by Identifying and Preserving Functional Networks.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Pruning Large Language Models by Identifying and Preserving Functional Networks

Reference 107

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.822948Z

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Observation 28dc4da2-b073-482d-955d-62419ed29fa4 · outbound

This paper cites How to dissect a M uppet: The structure of transformer embedding spaces.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance How to dissect a M uppet: The structure of transformer embedding spaces

Reference 108

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.260502Z

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Observation 231639ca-cc55-41fc-a555-befb52bd6ec6 · outbound

This paper cites Linguistic regularities in continuous space word representations.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Linguistic regularities in continuous space word representations

Reference 109

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.928938Z

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Observation 9fbf018e-5276-4e6c-ac60-a42ca6c8c071 · outbound

This paper cites MTEB : Massive text embedding benchmark.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance MTEB : Massive text embedding benchmark

Reference 110

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.267178Z

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Observation a11e2934-a5f6-4d2b-9705-c2419a6e8b1c · outbound

This paper cites Exploring interpretability of independent components of word embeddings with automated word intruder test.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Exploring interpretability of independent components of word embeddings with automated word intruder test

Reference 111

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.939915Z

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Observation ecec0c7d-8d0b-4833-ac4f-fc01610d1312 · outbound

This paper cites Emergent linear representations in world models of self-supervised sequence models.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Emergent linear representations in world models of self-supervised sequence models

Reference 112

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.244267Z

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Observation dec5f723-6f94-4eed-97c4-89a95ab2a4da · outbound

This paper cites most-common-words-by-language.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance most-common-words-by-language

Reference 113

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.918327Z

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Observation c81ad1ba-1cee-4c2b-945e-02771fdfbd27 · outbound

This paper cites Kakade, and Stephanie Gil.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Kakade, and Stephanie Gil

Reference 114

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.920987Z

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Observation 0d47e027-a082-4e3d-9791-c58fc9edf5f5 · outbound

This paper cites The linear representation hypothesis and the geometry of large language models.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The linear representation hypothesis and the geometry of large language models

Reference 115

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.923677Z

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Observation 27fda349-5c2b-4f97-9057-2acfdf143a00 · outbound

This paper cites The geometry of categorical and hierarchical concepts in large language models.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance The geometry of categorical and hierarchical concepts in large language models

Reference 116

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.912851Z

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Observation b9ff0fac-2d32-4712-b858-41fdd8bc1f33 · outbound

This paper cites Scikit-learn: Machine learning in P ython.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Scikit-learn: Machine learning in P ython

Reference 117

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.915575Z

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Observation ffb21e1e-48d5-495b-b9fe-1632052acf5f · outbound

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

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 118

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.817050Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b0bb8a6-de91-42cc-99d9-fee3fcee9fc3 · outbound

This paper cites All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations

Reference 119

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:24:40.798119Z

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Observation 47ca93c6-ba16-4248-abbc-e167e2262622 · outbound

This paper cites Large language models encode semantics and alignment in linearly separable representations.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Large language models encode semantics and alignment in linearly separable representations

Reference 120

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.333626Z

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Observation 6709a866-1dec-4b0e-850e-c4ec052d78cb · outbound

This paper cites Layer by layer: Uncovering hidden representations in language models.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Layer by layer: Uncovering hidden representations in language models

Reference 121

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.907838Z

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Observation 08c6a57e-17ef-4d7b-9f58-34001b7a278a · outbound

This paper cites an unresolved cited work.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Unresolved cited work

Reference 122

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.257633Z

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Observation e75590c8-e90a-496d-9aa4-194ff84263d5 · outbound

This paper cites Tatoeba: Collection of sentences and translations.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Tatoeba: Collection of sentences and translations

Reference 123

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.905527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d63c7e01-3949-45eb-838e-f68fcea6c51d · outbound

This paper cites A deep dive into multi-head attention and multi-aspect embedding.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance A deep dive into multi-head attention and multi-aspect embedding

Reference 124

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.903009Z

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Observation c937e42f-4120-40f1-a218-05c38871c904 · outbound

This paper cites All bark and no bite: Rogue dimensions in transformer language models obscure representational quality.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance All bark and no bite: Rogue dimensions in transformer language models obscure representational quality

Reference 125

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.321427Z

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Observation a7999128-a44a-42e6-83ee-6b7b0ffeb3e7 · outbound

This paper cites Model2vec: Fast state-of-the-art static embeddings.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Model2vec: Fast state-of-the-art static embeddings

Reference 126

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T06:24:41.910183Z

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Observation 5fc75b00-63c7-484f-ae60-68fefd6eaa9f · outbound

This paper cites EmbeddingGemma: Powerful and Lightweight Text Representations.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance EmbeddingGemma: Powerful and Lightweight Text Representations

Reference 127

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:24:40.807607Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 341aab49-e9c9-4307-8395-02cff44ed42f · outbound

This paper cites Jay Kuo, and Haizhou Li.

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance Jay Kuo, and Haizhou Li

Reference 128

Resolution: verified exact; doi, observed 2026-05-22T06:24:40.273677Z

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