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Paper Citation Record · LEDGER

Separating Representation from Reconstruction Enables Scalable Text Encoders

As of 20 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2607.04011.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.04011 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T22:20:52.422988Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved39
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ace548c8-16a9-42c0-9245-8188fc7159ff · outbound

This paper cites Training compute-optimal transformer encoder models.

Separating Representation from Reconstruction Enables Scalable Text Encoders Training compute-optimal transformer encoder models

Reference 1

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Source-reported events for the cited work

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Observation 03748d60-f13c-4598-b48c-78191ff8a90e · outbound

This paper cites Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 2

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Observation e81b4826-dc72-4069-a966-c9a4452c0966 · outbound

This paper cites NeoBERT: A Next-Generation BERT.

Separating Representation from Reconstruction Enables Scalable Text Encoders NeoBERT: A Next-Generation BERT

Reference 3

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Observation c267ec25-95f7-444e-9d2a-9b7912ed6ec3 · outbound

This paper cites Nomic Embed: Training a Reproducible Long Context Text Embedder.

Separating Representation from Reconstruction Enables Scalable Text Encoders Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 4

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Observation c93a426c-8097-4e02-bbc0-649ce50e6f05 · outbound

This paper cites an unresolved cited work.

Separating Representation from Reconstruction Enables Scalable Text Encoders Unresolved cited work

Reference 5

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Observation 7396c3bf-c2a6-4ce3-92bf-9225faedadf9 · outbound

This paper cites Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages=

Reference 6

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Observation 3cf1f46a-aac4-47b4-a04c-e97ef02f6780 · outbound

This paper cites Rethinking Patch Dependence for Masked Autoencoders.

Separating Representation from Reconstruction Enables Scalable Text Encoders Rethinking Patch Dependence for Masked Autoencoders

Reference 7

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Observation 544ba040-2d98-410e-945d-49933a15a2e2 · outbound

This paper cites Cluster and Predict Latent Patches for Improved Masked Image Modeling.

Separating Representation from Reconstruction Enables Scalable Text Encoders Cluster and Predict Latent Patches for Improved Masked Image Modeling

Reference 8

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Observation 79eeae20-b7f4-4d47-97e6-c518ae867bbf · outbound

This paper cites International conference on machine learning , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders International conference on machine learning , pages=

Reference 9

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Observation 6daafff7-c458-4ac8-a447-05af2d25aa18 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 10

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Observation 4c28df6a-9739-4792-af68-185d9b0e5f5e · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Separating Representation from Reconstruction Enables Scalable Text Encoders DINOv2: Learning Robust Visual Features without Supervision

Reference 11

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Observation 6379d943-2cca-4af0-98b6-1f3f65add56e · outbound

This paper cites DINOv3.

Separating Representation from Reconstruction Enables Scalable Text Encoders DINOv3

Reference 12

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Observation f3499700-1d26-4260-896f-19330642bd36 · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Separating Representation from Reconstruction Enables Scalable Text Encoders Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 13

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Observation 53857998-f47c-40b9-bfa8-8b1c804b38c0 · outbound

This paper cites RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder.

Separating Representation from Reconstruction Enables Scalable Text Encoders RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder

Reference 14

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Observation 69b00ae8-84c5-4040-831f-bce93611320b · outbound

This paper cites Proceedings of the 2018 EMNLP workshop BlackboxNLP: Analyzing and interpreting neural networks for NLP , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the 2018 EMNLP workshop BlackboxNLP: Analyzing and interpreting neural networks for NLP , pages=

Reference 15

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Observation 147c304b-e940-4abe-ba3c-dd86b45ca0da · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Separating Representation from Reconstruction Enables Scalable Text Encoders MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 16

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Observation 6081b905-7ad9-451e-bd7c-9665cf5fb463 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Advances in Neural Information Processing Systems , volume=

Reference 17

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Observation 08f24245-a9a4-4b5a-a830-758de003b8ff · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

Separating Representation from Reconstruction Enables Scalable Text Encoders Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 18

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Observation f5754cf5-b3f6-45bd-b967-f5a772023d9d · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 19

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Observation 1e6e4f8f-f1c6-4382-84b7-51b23a71b3e7 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 20

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Observation 2574f0cd-bd8d-4b30-aaf4-6750e568f37e · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Learning and Leveraging World Models in Visual Representation Learning

Reference 21

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Observation 16367a4d-aa9b-49df-a479-003bd4bd73bb · outbound

This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

Separating Representation from Reconstruction Enables Scalable Text Encoders LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 22

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Observation 44839e14-19b5-4577-a689-1ea2025bf5ff · outbound

This paper cites Perception Encoder: The best visual embeddings are not at the output of the network.

Separating Representation from Reconstruction Enables Scalable Text Encoders Perception Encoder: The best visual embeddings are not at the output of the network

Reference 23

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Observation e5d8635c-6d00-4c5c-9886-d8d9a71eeca5 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Separating Representation from Reconstruction Enables Scalable Text Encoders EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 24

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Observation 3e42ed0a-fbbf-4cfb-abf2-0b802c7f26de · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 25

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Observation 08cc1f4a-46c8-4d2a-8827-2988d0a8adeb · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Separating Representation from Reconstruction Enables Scalable Text Encoders RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 26

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Observation 5c9fb3e8-4364-4fda-9516-886ce5659f61 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Advances in neural information processing systems , volume=

Reference 27

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Observation e2c902cc-df0e-41b8-8594-f4c2d509056f · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Unresolved cited work

Reference 28

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Observation 5216b7e0-d826-4e48-8605-124bd966a756 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 29

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Observation 1a909210-018b-4196-98e4-82de565646d5 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Scaling Laws for Neural Language Models

Reference 30

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Observation f4da7a38-a7ed-47fe-a9f3-3c585fcb5f48 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders and Sifre, Laurent , title =

Reference 31

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Observation e2d2898d-a5c9-4571-a03a-3a4bc30675f3 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 32

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Observation 5afe7ad2-b0fc-424d-afc7-d3a3a7d81422 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Language models scale reliably with over-training and on downstream tasks

Reference 33

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Observation 5020cd9c-3c71-4129-8527-b8b8db863df2 · outbound

This paper cites Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 34

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Observation 88cd7eef-cdf1-4551-b21a-74539cf44f51 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Journal of machine learning research , volume=

Reference 35

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Observation 5230b686-ce9a-4045-99ae-c79d157e59ce · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 36

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Observation d74ece71-b0d8-4cef-9942-b4e02ae7c108 · outbound

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Separating Representation from Reconstruction Enables Scalable Text Encoders Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 37

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Observation 93981c5f-898f-4d7b-be0f-97b0420b5cc2 · outbound

This paper cites Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages=.

Separating Representation from Reconstruction Enables Scalable Text Encoders Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages=

Reference 38

Resolution
unresolved
no resolver link, observed 2026-07-11T22:20:52.422988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T22:20:52.422988Z digest=sha256:7021b4b09e9f098d7991bde78128f418715f5a4cea4d3ec8496191e4999a4e0a

Observation 4ab70ef9-b7ae-4f03-8ce0-fa9ce811bac7 · outbound

This paper cites 2020 , eprint=.

Separating Representation from Reconstruction Enables Scalable Text Encoders 2020 , eprint=

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-11T22:20:52.422988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T22:20:52.422988Z digest=sha256:d8121fb75ec2caa5de7562d0deaf935b0d69b599d77556f73d31bdc2cd6cc737

Observation bcc744ac-c545-4b43-b3b9-4e68fe6ca1a8 · outbound

This paper cites 2023 , eprint=.

Separating Representation from Reconstruction Enables Scalable Text Encoders 2023 , eprint=

Reference 40

Resolution
unresolved
no resolver link, observed 2026-07-11T22:20:52.422988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T22:20:52.422988Z digest=sha256:f6354ffadb44f39e1a5f5a9ff317210e90b193c3987381544846b8e37429b8e3

Observation 046de455-fdae-4f44-b8df-ac7a9ef7fda9 · outbound

This paper cites Are ELECTRA ' s Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity.

Separating Representation from Reconstruction Enables Scalable Text Encoders Are ELECTRA ' s Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity

Reference 41

Resolution
verified exact
doi, observed 2026-07-11T22:28:20.487537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-07-11T22:20:52.422988Z digest=sha256:67cfd3c4579124397db1d7fb919c734e95ad2842b7dc5ddd04fc5b840dddc6db

Pith citing papers

No inbound Pith citation observations are available.