Paper Citation Record · LEDGER

Investigating Mixture of Experts in Dense Retrieval

As of 13 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2412.11864.

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

pith.paper-citation-record.v1
2412.11864 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:33:34.675073Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:55:33.698459Z

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 exact2
verified fuzzy15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a8e67e4-6cdf-435c-a9b1-b13db591c013 · outbound

This paper cites In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management.

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Reference 1

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Observation 669dddaf-cd68-4e91-bc8d-ddca0ea599f9 · outbound

This paper cites In: The Eleventh Interna- tional Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023.

Investigating Mixture of Experts in Dense Retrieval In: The Eleventh Interna- tional Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023

Reference 2

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Observation 489ac3b3-f9c8-44ed-83a2-34d1f621fa05 · outbound

This paper cites Advances in Neural Information Processing Systems14 (2001).

Investigating Mixture of Experts in Dense Retrieval Advances in Neural Information Processing Systems14 (2001)

Reference 3

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Observation 4c860e64-009f-4166-95e2-c79b11209096 · outbound

This paper cites In: Natural Language Process- ing and Chinese Computing (2022), https://api.semanticscholar.org/CorpusID: 248218762.

Investigating Mixture of Experts in Dense Retrieval In: Natural Language Process- ing and Chinese Computing (2022), https://api.semanticscholar.org/CorpusID: 248218762

Reference 4

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This paper cites In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Reference 5

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Observation 33ac51cb-fe11-4d59-a21a-01de4d128ce1 · outbound

This paper cites Learning Factored Representations in a Deep Mixture of Experts.

Investigating Mixture of Experts in Dense Retrieval Learning Factored Representations in a Deep Mixture of Experts

Reference 6

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This paper cites In: Proceedings of the 60th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers).

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 60th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers)

Reference 7

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This paper cites ACM Trans.

Investigating Mixture of Experts in Dense Retrieval ACM Trans

Reference 8

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Observation ad327a17-d4de-4f49-ae9b-f4fd607e3fc4 · outbound

This paper cites In: Proceedings of the 36th International Conference on Machine Learning.

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 36th International Conference on Machine Learning

Reference 9

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This paper cites Transac- tions on Machine Learning Research (2022), https://openreview.net/forum?id= jKN1pXi7b0.

Investigating Mixture of Experts in Dense Retrieval Transac- tions on Machine Learning Research (2022), https://openreview.net/forum?id= jKN1pXi7b0

Reference 10

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Observation bd3b412c-7325-4dc3-8e5c-56dc47d62a0d · outbound

This paper cites In: Findings of the Association for Computational Linguistics: EMNLP 2020.

Investigating Mixture of Experts in Dense Retrieval In: Findings of the Association for Computational Linguistics: EMNLP 2020

Reference 12

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Observation 75370e83-3fe1-4c98-a17d-1829cbba0b85 · outbound

This paper cites Neural Computation 6(2), 181–214 (03 1994).

Investigating Mixture of Experts in Dense Retrieval Neural Computation 6(2), 181–214 (03 1994)

Reference 13

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Investigating Mixture of Experts in Dense Retrieval Unresolved cited work

Reference 14

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Observation 95e72e04-b5d4-4ad2-aebe-0793529cb4f8 · outbound

This paper cites In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Reference 15

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Observation 4c69fc71-6b6c-496e-80c6-046d4e0fd9de · outbound

This paper cites In: Advances in Information Retrieval.

Investigating Mixture of Experts in Dense Retrieval In: Advances in Information Retrieval

Reference 16

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Observation 590b0beb-ac34-41bd-8a7e-37ba26b4b4ff · outbound

This paper cites Transactions of the Association for Computational Linguistics7, 453–466 (08 2019).

Investigating Mixture of Experts in Dense Retrieval Transactions of the Association for Computational Linguistics7, 453–466 (08 2019)

Reference 17

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This paper cites In: Findings of the Asso- ciation for Computational Linguistics: EMNLP 2021.

Investigating Mixture of Experts in Dense Retrieval In: Findings of the Asso- ciation for Computational Linguistics: EMNLP 2021

Reference 18

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Observation c497ad80-852f-46ea-bc2f-f67ca974f6b3 · outbound

This paper cites In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy.

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy

Reference 19

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Observation 6c43937c-122b-4359-bb7e-583b9cec5073 · outbound

This paper cites Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective.

Investigating Mixture of Experts in Dense Retrieval Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective

Reference 20

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Observation bfee9b9e-d475-41bb-86c2-2e641606d66f · outbound

This paper cites CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval.

Investigating Mixture of Experts in Dense Retrieval CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval

Reference 21

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Observation c85683c0-19aa-4a9e-9a5c-52a4f95a2d59 · outbound

This paper cites Founda- tions and Trends® in Information Retrieval13(1), 1–126 (2018).

Investigating Mixture of Experts in Dense Retrieval Founda- tions and Trends® in Information Retrieval13(1), 1–126 (2018)

Reference 22

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This paper cites In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Reference 23

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Observation fcdeb45c-e2fc-408c-97f4-c78f065b7013 · outbound

This paper cites Nist Special Publication Sp109, 109 (1995).

Investigating Mixture of Experts in Dense Retrieval Nist Special Publication Sp109, 109 (1995)

Reference 24

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Observation 197c824c-79c1-4a7b-a068-2260a61c5d82 · outbound

This paper cites arXiv (12 2014).

Investigating Mixture of Experts in Dense Retrieval arXiv (12 2014)

Reference 25

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This paper cites In: International Conference on Learning Representations (2017), https://openre view.net/forum?id=B1ckMDqlg.

Investigating Mixture of Experts in Dense Retrieval In: International Conference on Learning Representations (2017), https://openre view.net/forum?id=B1ckMDqlg

Reference 26

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This paper cites In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?i d=6mLjDwYte5.

Investigating Mixture of Experts in Dense Retrieval In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?i d=6mLjDwYte5

Reference 27

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This paper cites In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G.

Investigating Mixture of Experts in Dense Retrieval In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G

Reference 28

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This paper cites In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021), https://openreview.net/forum?id=wCu6T5 xFjeJ.

Investigating Mixture of Experts in Dense Retrieval In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021), https://openreview.net/forum?id=wCu6T5 xFjeJ

Reference 29

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This paper cites In: Proceed- ings of the 2022 Conference on Empirical Methods in Natural Language Process- ing.

Investigating Mixture of Experts in Dense Retrieval In: Proceed- ings of the 2022 Conference on Empirical Methods in Natural Language Process- ing

Reference 30

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This paper cites In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Reference 31

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This paper cites In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

Investigating Mixture of Experts in Dense Retrieval In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Reference 32

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This paper cites In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.

Investigating Mixture of Experts in Dense Retrieval In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024

Reference 33

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Investigating Mixture of Experts in Dense Retrieval Unresolved cited work

Reference 34

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This paper cites https://doi.org/10.18653/v1/2022.acl-long.203.

Investigating Mixture of Experts in Dense Retrieval https://doi.org/10.18653/v1/2022.acl-long.203

Reference 2853

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Pith citing papers

Observation 2b969732-cd84-40ed-be09-93768f1879c7 · inbound

Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees cites this paper.

Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees Investigating Mixture of Experts in Dense Retrieval

Reference 48

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