Paper Citation Record · LEDGER

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework

As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.23507.

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

pith.paper-citation-record.v1
2607.23507 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T20:41:37.343881Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

34 of 34 outbound references displayed

verified exact0
verified fuzzy0
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d064fd9-9d3d-4acb-b3e8-28f06b5cb920 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework MTEB: Massive Text Embedding Benchmark

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.449255Z

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Observation e07beb99-bb00-4d55-b2ee-289e20592eb1 · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.518571Z

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Observation 140b8f5b-5cb2-417e-96e3-89b62e0ef602 · outbound

This paper cites WWW’18 Open Challenge: Financial Opinion Mining and Question Answering,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework WWW’18 Open Challenge: Financial Opinion Mining and Question Answering,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.630153Z

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Observation 4a9b1b1f-738a-44a9-b829-b2ec7f9e3c89 · outbound

This paper cites A Full-Text Learning to Rank Dataset for Medical Information Retrieval,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework A Full-Text Learning to Rank Dataset for Medical Information Retrieval,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.693173Z

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Observation e3fcf4f5-fc52-4f2d-9023-8b2dff3b6437 · outbound

This paper cites Fact or Fiction: Verifying Scientific Claims,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Fact or Fiction: Verifying Scientific Claims,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.763412Z

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Observation 57f60392-889c-4c62-9a5d-90bfb2fc7273 · outbound

This paper cites TREC-COVID: Constructing a Pandemic Information Retrieval Test Collec- tion,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework TREC-COVID: Constructing a Pandemic Information Retrieval Test Collec- tion,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.797908Z

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Observation 54edffe0-8902-4500-a336-8dced7fa3322 · outbound

This paper cites Cumulated Gain-Based Evaluation of IR Techniques,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Cumulated Gain-Based Evaluation of IR Techniques,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.887800Z

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Observation 2d5de710-20e3-40ef-bf1d-4e703d7fecd5 · outbound

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:34.947396Z

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Unavailable: canonical work link unavailable.

Observation c26f2b5b-dced-4e69-b04e-1d4c70b9db49 · outbound

This paper cites Multilingual E5 Text Embeddings: A Technical Report.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Multilingual E5 Text Embeddings: A Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.055490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52c93f20-a801-4eff-b4c2-2f5f11a7b041 · outbound

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework C-Pack: Packed Resources For General Chinese Embeddings

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.115835Z

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Observation 4e4aa7f3-24b2-4f77-a2f7-a5399f36ffe8 · outbound

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.233975Z

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Observation 2894f04f-c521-4578-97bd-eaf78197a3e9 · outbound

This paper cites Language-agnostic BERT Sentence Embedding,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Language-agnostic BERT Sentence Embedding,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.314097Z

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Observation 11a2264a-fb9f-4638-9ee8-8c6aa79c1213 · outbound

This paper cites MPNet: Masked and Permuted Pre-training for Language Understanding,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework MPNet: Masked and Permuted Pre-training for Language Understanding,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.425576Z

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Observation e5944273-48d3-45f4-80ae-ecacd5dc0bca · outbound

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Sentence-BERT: Sentence Embeddings using Siamese BERT- Networks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.540258Z

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Observation dd6c9b64-2ad8-4e22-893c-8dedc4b20925 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework SimCSE: Simple Contrastive Learning of Sentence Embeddings,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.653913Z

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Observation c790f91f-1923-4a68-895d-541bb8547c50 · outbound

This paper cites Unsuper- vised Dense Information Retrieval with Contrastive Learning,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Unsuper- vised Dense Information Retrieval with Contrastive Learning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.764104Z

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Observation c8bc7451-924d-49c2-8ec4-ac9d32e22fb4 · outbound

This paper cites SPECTER: Document-level Representation Learning using Citation-informed Transformers,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework SPECTER: Document-level Representation Learning using Citation-informed Transformers,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.931908Z

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Observation 97f0ddc0-ea66-4810-95fe-e6f7ea9411ce · outbound

This paper cites Large Dual Encoders Are Generalizable Retrievers.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Large Dual Encoders Are Generalizable Retrievers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:35.991806Z

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Observation f06bcd2c-5584-4a66-ba5f-bb8046dca091 · outbound

This paper cites Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.099940Z

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Observation 04495888-35ad-4922-9dc7-14702b88c2b2 · outbound

This paper cites SGPT: GPT Sentence Embeddings for Semantic Search.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework SGPT: GPT Sentence Embeddings for Semantic Search

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.268379Z

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Unavailable: canonical work link unavailable.

Observation e2d387e2-79b5-4364-a78c-1069edec0481 · outbound

This paper cites GloVe: Global Vectors for Word Representation,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework GloVe: Global Vectors for Word Representation,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.329572Z

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Observation d58c7700-925f-4649-bb5b-e95c5fef19b8 · outbound

This paper cites Dependency Based Embeddings for Sentence Classification Tasks,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Dependency Based Embeddings for Sentence Classification Tasks,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.438222Z

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Observation aa5f4157-69da-4a70-b891-40aeaefa474b · outbound

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.545485Z

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Observation a22d034e-6f3f-4171-8945-526c27b3f8cd · outbound

This paper cites Massively Multilingual Sentence Embeddings for Zero-Shot Cross- Lingual Transfer and Beyond,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Massively Multilingual Sentence Embeddings for Zero-Shot Cross- Lingual Transfer and Beyond,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.651523Z

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Observation 9489c6a4-0025-4c58-9b00-7532c7a8e513 · outbound

This paper cites ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.684091Z

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Observation bbd152ba-ca2c-49bc-bfcb-42eccea733c9 · outbound

This paper cites SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.687535Z

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Observation 1d3bbf2c-8ef6-4163-86e6-7ab59266b934 · outbound

This paper cites The Probabilistic Relevance Framework: BM25 and Beyond,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework The Probabilistic Relevance Framework: BM25 and Beyond,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.699706Z

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Observation 4585e848-d6bd-42e4-85a4-ad1c35e1caaf · outbound

This paper cites Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.789057Z

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Observation 570030f1-8286-4044-89b9-28c14c9b9452 · outbound

This paper cites Product Quantization for Nearest Neighbor Search,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Product Quantization for Nearest Neighbor Search,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.894541Z

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Observation c2c36fdf-2c53-40bc-b573-85d74c6bffda · outbound

This paper cites Billion-scale Similarity Search with GPUs,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Billion-scale Similarity Search with GPUs,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:36.989530Z

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Observation de474351-df9f-43e1-a13b-008c31bff9c2 · outbound

This paper cites Milvus: A Purpose-Built Vector Data Management System,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Milvus: A Purpose-Built Vector Data Management System,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:37.053029Z

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Observation 0ddd7bba-4c6b-47f2-8a62-e276303a5817 · outbound

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:37.169734Z

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Observation 8dbb9f31-31e0-403d-a5f8-45029a03a766 · outbound

This paper cites Qdrant: Vector Database for the Next Generation of AI Applications,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Qdrant: Vector Database for the Next Generation of AI Applications,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:37.283607Z

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Observation f4f8036e-9d1d-42d3-b8b5-47793e213c65 · outbound

This paper cites Pinecone Vector Database,.

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework Pinecone Vector Database,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-30T20:41:37.343881Z

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Pith citing papers

No inbound Pith citation observations are available.