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Paper Citation Record · LEDGER

A Comparative Study of Specialized LLMs as Dense Retrievers

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.03958.

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

pith.paper-citation-record.v1
2507.03958 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:02:17.579702Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

39 of 39 outbound references displayed

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  • unresolved29
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External citation measurements

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Outbound references

Observation 854ebd98-5a8b-4819-8084-2373e1ac9ced · outbound

This paper cites GPT-4 Technical Report.

A Comparative Study of Specialized LLMs as Dense Retrievers GPT-4 Technical Report

Reference 1

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Observation 442a03af-8834-4075-beb5-808e332791f1 · outbound

This paper cites Qwen Technical Report.

A Comparative Study of Specialized LLMs as Dense Retrievers Qwen Technical Report

Reference 2

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Observation 85fcf197-7ed4-4f6e-9db9-41b56a4998e2 · outbound

This paper cites Qwen2.5-VL Technical Report.

A Comparative Study of Specialized LLMs as Dense Retrievers Qwen2.5-VL Technical Report

Reference 3

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Observation 2568b648-a23e-4253-8467-396b7b21aff0 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

A Comparative Study of Specialized LLMs as Dense Retrievers LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

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Observation f031fbda-1606-4f42-996e-f51da30b3914 · outbound

This paper cites In: Advances in Information Retrieval: 38th European Conference on IR Research.

A Comparative Study of Specialized LLMs as Dense Retrievers In: Advances in Information Retrieval: 38th European Conference on IR Research

Reference 5

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Observation 782d666d-0c22-4f5a-be24-b4b23c80962d · outbound

This paper cites Pre-training Tasks for Embedding-based Large-scale Retrieval.

A Comparative Study of Specialized LLMs as Dense Retrievers Pre-training Tasks for Embedding-based Large-scale Retrieval

Reference 6

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Observation 821fab9f-c30e-4a1d-945f-c60c95e91154 · outbound

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

A Comparative Study of Specialized LLMs as Dense Retrievers SPECTER: Document-level Representation Learning using Citation-informed Transformers

Reference 7

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Observation aa52116a-6c79-4f07-9587-f1bb80831c44 · outbound

This paper cites Overview of the TREC 2019 deep learning track.

A Comparative Study of Specialized LLMs as Dense Retrievers Overview of the TREC 2019 deep learning track

Reference 8

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Observation 4f1b0bb5-1ece-43d3-bc62-2da0b30ac764 · outbound

This paper cites Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval.

A Comparative Study of Specialized LLMs as Dense Retrievers Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval

Reference 9

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Observation 635d2b74-bdc5-49a0-afc9-1125d7d68ad7 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

A Comparative Study of Specialized LLMs as Dense Retrievers DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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Observation 4b0a2e28-b1ba-4f94-a6b6-8f41dc9ca005 · outbound

This paper cites ACM Transactions on Information Systems (TOIS) 40(4), 1–42 (2022).

A Comparative Study of Specialized LLMs as Dense Retrievers ACM Transactions on Information Systems (TOIS) 40(4), 1–42 (2022)

Reference 11

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 828e5854-a771-42c8-ba38-e8301f4a5603 · outbound

This paper cites In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management.

A Comparative Study of Specialized LLMs as Dense Retrievers In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation eb2ed3e7-5690-41ed-b2a7-23b6d2001876 · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

A Comparative Study of Specialized LLMs as Dense Retrievers Measuring Coding Challenge Competence With APPS

Reference 13

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Observation e330e38d-9028-4140-9315-1f93a01225f5 · outbound

This paper cites CoSQA: 20,000+ Web Queries for Code Search and Question Answering.

A Comparative Study of Specialized LLMs as Dense Retrievers CoSQA: 20,000+ Web Queries for Code Search and Question Answering

Reference 14

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Observation 231c3b37-334b-471c-8790-764fba8d7ce1 · outbound

This paper cites Qwen2.5-Coder Technical Report.

A Comparative Study of Specialized LLMs as Dense Retrievers Qwen2.5-Coder Technical Report

Reference 15

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Observation a885932a-c481-429c-a065-c8b4ff2331ac · outbound

This paper cites CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.

A Comparative Study of Specialized LLMs as Dense Retrievers CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 16

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Observation ff1a4a88-5efc-41aa-aab4-85cc7e92ef59 · outbound

This paper cites In: EMNLP (1).

A Comparative Study of Specialized LLMs as Dense Retrievers In: EMNLP (1)

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3a919080-33dd-415b-aa22-1dc15f126c9c · outbound

This paper cites In: Proceedings of the 62nd Annual MeetingoftheAssociationforComputationalLinguistics(Volume1:LongPapers).

A Comparative Study of Specialized LLMs as Dense Retrievers In: Proceedings of the 62nd Annual MeetingoftheAssociationforComputationalLinguistics(Volume1:LongPapers)

Reference 18

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4aee729d-b5c4-48cb-a4a1-c6a3b465c109 · outbound

This paper cites CoIR: A Comprehensive Benchmark for Code Information Retrieval Models.

A Comparative Study of Specialized LLMs as Dense Retrievers CoIR: A Comprehensive Benchmark for Code Information Retrieval Models

Reference 19

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Observation cc30461a-a760-4c12-91cc-f12ca4ed34cd · outbound

This paper cites DeepSeek-V3 Technical Report.

A Comparative Study of Specialized LLMs as Dense Retrievers DeepSeek-V3 Technical Report

Reference 20

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Observation 6cb00321-186f-4ed3-91ff-413dbd228898 · outbound

This paper cites In: Proceedings of the 14th ACM international conference on web search and data mining.

A Comparative Study of Specialized LLMs as Dense Retrievers In: Proceedings of the 14th ACM international conference on web search and data mining

Reference 21

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Observation 676ee17f-ef23-4ab2-b5f7-9dec4f94e8a9 · outbound

This paper cites In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.

A Comparative Study of Specialized LLMs as Dense Retrievers In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Reference 22

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Observation 73bf5755-79d0-4808-b843-43d965e8d04f · outbound

This paper cites In: Companion proceedings of the the web conference 2018.

A Comparative Study of Specialized LLMs as Dense Retrievers In: Companion proceedings of the the web conference 2018

Reference 23

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Observation 6f3bfc7c-a70b-4f2b-b225-09996e9e046b · outbound

This paper cites an unresolved cited work.

A Comparative Study of Specialized LLMs as Dense Retrievers Unresolved cited work

Reference 24

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Observation ff23b9f7-be72-4646-8054-f3142a55b78d · outbound

This paper cites RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering.

A Comparative Study of Specialized LLMs as Dense Retrievers RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 25

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Observation 9137360b-40ce-4309-a23e-7472a8a64c0c · outbound

This paper cites Foundations and Trends® in Information Retrieval 3(4), 333–389 (2009).

A Comparative Study of Specialized LLMs as Dense Retrievers Foundations and Trends® in Information Retrieval 3(4), 333–389 (2009)

Reference 26

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Observation 03d33564-cf56-463a-9820-41613b9a77df · outbound

This paper cites Repetition Improves Language Model Embeddings.

A Comparative Study of Specialized LLMs as Dense Retrievers Repetition Improves Language Model Embeddings

Reference 28

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Observation 056a2f6c-d168-459a-8fcc-fc94e34b9720 · outbound

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

A Comparative Study of Specialized LLMs as Dense Retrievers BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 29

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Observation 72686ba4-8562-4587-b90d-6dd389bb346d · outbound

This paper cites Advances in neural information processing systems30 (2017).

A Comparative Study of Specialized LLMs as Dense Retrievers Advances in neural information processing systems30 (2017)

Reference 30

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Observation 46c7383a-abd4-4cdc-a123-9c2854382687 · outbound

This paper cites In: Proceedings of the 56th Annual Meeting of the Associ- ation for Computational Linguistics (Volume 1: Long Papers).

A Comparative Study of Specialized LLMs as Dense Retrievers In: Proceedings of the 56th Annual Meeting of the Associ- ation for Computational Linguistics (Volume 1: Long Papers)

Reference 31

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Observation 270c3903-7e25-44da-9f8f-da99973dd403 · outbound

This paper cites ArXiv pp.

A Comparative Study of Specialized LLMs as Dense Retrievers ArXiv pp

Reference 32

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Observation 603beeba-0ff1-42ae-8345-fc056fe0308c · outbound

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

A Comparative Study of Specialized LLMs as Dense Retrievers RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder

Reference 33

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Observation 3720365f-149c-4d0e-bcee-875018218545 · outbound

This paper cites CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation.

A Comparative Study of Specialized LLMs as Dense Retrievers CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation

Reference 34

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Observation 3716bc0e-dbc5-4272-90fb-58de0a8acf26 · outbound

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A Comparative Study of Specialized LLMs as Dense Retrievers Unresolved cited work

Reference 35

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Observation eb79e5d9-0fa8-44d4-ba4f-954ba4973e3a · outbound

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A Comparative Study of Specialized LLMs as Dense Retrievers Unresolved cited work

Reference 36

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Observation 841405b3-7dfa-4620-8e67-f5c3d5a98698 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

A Comparative Study of Specialized LLMs as Dense Retrievers Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 37

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Unavailable: canonical work link unavailable.

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Observation 057b6639-3b32-42f4-93a3-4592e5fe93d2 · outbound

This paper cites Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling.

A Comparative Study of Specialized LLMs as Dense Retrievers Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling

Reference 38

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no resolver link, observed 2026-08-06T20:02:17.573803Z

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source=pdf_text observed=2026-08-06T20:02:17.573803Z digest=sha256:dda33fcef50be43ae3cdba948a1584a9a7a88095af9a0d8ff803ced9c3a44749

Observation 1dbd9ed9-4ea8-47bf-916a-e11d9a3c8de1 · outbound

This paper cites OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement.

A Comparative Study of Specialized LLMs as Dense Retrievers OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

Reference 39

Resolution
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no resolver link, observed 2026-08-06T20:02:17.576803Z

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source=pdf_text observed=2026-08-06T20:02:17.576803Z digest=sha256:4bca25c8b13a9d16a84242c9b073cc47e794ea22b0c58d48d80ed0bb364d429a

Observation f48b94ca-507d-4191-a4ed-859f9afd502c · outbound

This paper cites PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval.

A Comparative Study of Specialized LLMs as Dense Retrievers PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval

Reference 40

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unresolved
no resolver link, observed 2026-08-06T20:02:17.579702Z

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source=pdf_text observed=2026-08-06T20:02:17.579702Z digest=sha256:8e9e602e86681ebe43857ad98038d27520efa6ec5902bf0e1c0ec4641c03f12d

Pith citing papers

No inbound Pith citation observations are available.