Paper Citation Record · LEDGER

Large Dual Encoders Are Generalizable Retrievers

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2112.07899.

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

pith.paper-citation-record.v1
2112.07899 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 41 of 41 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:33:47.369392Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:19:47.681833Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d280e20e-b0cb-406e-8474-8df3f02fd234 · inbound

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

Text Embeddings by Weakly-Supervised Contrastive Pre-training Large Dual Encoders Are Generalizable Retrievers

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:54:03.956020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ed506b3-5452-4bb8-9620-b5794bbe2df8 · inbound

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

C-Pack: Packed Resources For General Chinese Embeddings Large Dual Encoders Are Generalizable Retrievers

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-13T13:24:32.124775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8acb75bb-0e0d-41c5-8816-d82ed9af0fe6 · inbound

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

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation Large Dual Encoders Are Generalizable Retrievers

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:39:03.373070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d655e3b-3d06-435e-b9e6-17bd6101818c · inbound

Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment cites this paper.

Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment Large Dual Encoders Are Generalizable Retrievers

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:38:39.908596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 508a4925-b049-457e-9894-6f5c0c5ab47e · inbound

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models cites this paper.

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models Large Dual Encoders Are Generalizable Retrievers

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:15:16.327352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3091e1f7-985f-49cf-8c5c-7448981a64db · inbound

Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features cites this paper.

Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features Large Dual Encoders Are Generalizable Retrievers

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T10:24:06.775124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1ccdcef-c1b0-4cd4-8c28-a1c5d7675064 · inbound

A Distributed Collaborative Retrieval Framework Excelling in All Queries and Corpora based on Zero-shot Rank-Oriented Automatic Evaluation cites this paper.

A Distributed Collaborative Retrieval Framework Excelling in All Queries and Corpora based on Zero-shot Rank-Oriented Automatic Evaluation Large Dual Encoders Are Generalizable Retrievers

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T14:36:16.054224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 721066ad-c9ba-4567-9b56-3f85b831a781 · inbound

LLMs are Also Effective Embedding Models: An In-depth Overview cites this paper.

LLMs are Also Effective Embedding Models: An In-depth Overview Large Dual Encoders Are Generalizable Retrievers

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-11T13:59:01.821415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c7b9b02-5790-4afd-8d9b-fe30b38ab018 · inbound

MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge cites this paper.

MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge Large Dual Encoders Are Generalizable Retrievers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T05:55:14.748838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b674a6d1-61eb-4e93-b314-7e9c8d260341 · inbound

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval cites this paper.

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval Large Dual Encoders Are Generalizable Retrievers

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T05:44:00.091650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4b2597c-d3d5-497d-94e5-224435e3b648 · inbound

Jasper and Stella: distillation of SOTA embedding models cites this paper.

Jasper and Stella: distillation of SOTA embedding models Large Dual Encoders Are Generalizable Retrievers

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T01:03:28.413799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9c27761-4e94-40be-920e-0c83c63fa91c · inbound

LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models cites this paper.

LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models Large Dual Encoders Are Generalizable Retrievers

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:11.037119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c5fffd3-1d89-4d0e-85c1-eac5d1dc2d79 · inbound

Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width cites this paper.

Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width Large Dual Encoders Are Generalizable Retrievers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T13:37:35.365270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d65004a-f21e-4b08-82ce-bb1bf392e62e · inbound

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation cites this paper.

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation Large Dual Encoders Are Generalizable Retrievers

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T12:07:24.098612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2e5ce12-9695-475b-ad88-3ef62f53d681 · inbound

CliniQ: A Multi-faceted Benchmark for Electronic Health Record Retrieval with Semantic Match Assessment cites this paper.

CliniQ: A Multi-faceted Benchmark for Electronic Health Record Retrieval with Semantic Match Assessment Large Dual Encoders Are Generalizable Retrievers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T16:19:07.287985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21cdf5cc-470d-488d-afe4-60be7136d4a1 · inbound

Duluth at SemEval-2025 Task 7: TF-IDF with Optimized Vector Dimensions for Multilingual Fact-Checked Claim Retrieval cites this paper.

Duluth at SemEval-2025 Task 7: TF-IDF with Optimized Vector Dimensions for Multilingual Fact-Checked Claim Retrieval Large Dual Encoders Are Generalizable Retrievers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:33:47.369392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 261a37da-89c6-4cf4-83e0-86e9bdbd9157 · inbound

Efficient Data Selection at Scale via Influence Distillation cites this paper.

Efficient Data Selection at Scale via Influence Distillation Large Dual Encoders Are Generalizable Retrievers

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:22.342650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da04e81c-804e-4149-80cf-e6a275c11469 · inbound

LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval cites this paper.

LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval Large Dual Encoders Are Generalizable Retrievers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:15:55.922994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41a7b8d1-f28e-4264-8630-29a8c5495534 · inbound

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings cites this paper.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Large Dual Encoders Are Generalizable Retrievers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:38.568382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a8b9e65-cd40-462f-8b21-89cdff7e1ead · inbound

When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR cites this paper.

When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR Large Dual Encoders Are Generalizable Retrievers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:39:46.437784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c9b3323-dbf0-40a4-b927-eb4d19b00bf6 · inbound

Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components cites this paper.

Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components Large Dual Encoders Are Generalizable Retrievers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:01:33.927499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f601dbc2-c496-4877-ae36-9eb5e10f0f02 · inbound

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation cites this paper.

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation Large Dual Encoders Are Generalizable Retrievers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:22.771521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27b1e83a-2e18-46da-ac61-f9f4464f1362 · inbound

Depth Gives a False Sense of Privacy: LLM Internal States Inversion cites this paper.

Depth Gives a False Sense of Privacy: LLM Internal States Inversion Large Dual Encoders Are Generalizable Retrievers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T15:18:13.013704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99c891d3-1f09-4256-b771-7c4484cf0fd2 · inbound

Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment cites this paper.

Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment Large Dual Encoders Are Generalizable Retrievers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:18:35.406639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 387529d9-e197-4da2-a2e2-c7d462807e17 · inbound

A Multi-Task Evaluation of LLMs' Processing of Academic Text Input cites this paper.

A Multi-Task Evaluation of LLMs' Processing of Academic Text Input Large Dual Encoders Are Generalizable Retrievers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T19:49:51.176303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c533a42-6bc2-4ee7-8bcf-8db07f7f83f2 · inbound

QZhou-Embedding Technical Report cites this paper.

QZhou-Embedding Technical Report Large Dual Encoders Are Generalizable Retrievers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:05.380759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 231e7d46-5f65-4d6f-aa4d-cd113d1ed0d3 · inbound

From Attack Descriptions to Vulnerabilities: A Sentence Transformer-Based Approach cites this paper.

From Attack Descriptions to Vulnerabilities: A Sentence Transformer-Based Approach Large Dual Encoders Are Generalizable Retrievers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T12:00:33.249198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2960ee1f-8fca-48cd-ba3f-af617f45a0c9 · inbound

IDEAlign: Comparing Large Language Models to Human Experts in Open-ended Interpretive Annotations cites this paper.

IDEAlign: Comparing Large Language Models to Human Experts in Open-ended Interpretive Annotations Large Dual Encoders Are Generalizable Retrievers

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T11:26:27.452127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b786ae7-8ce7-4caa-942c-19dd94d2a756 · inbound

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models cites this paper.

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models Large Dual Encoders Are Generalizable Retrievers

Reference 193

Resolution: unresolved; no resolver link, observed 2026-08-15T15:56:21.833969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c288c99-8200-4d52-976e-4bb453d44e2a · inbound

EmbeddingGemma: Powerful and Lightweight Text Representations cites this paper.

EmbeddingGemma: Powerful and Lightweight Text Representations Large Dual Encoders Are Generalizable Retrievers

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:07:21.065198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 07eebbaa-db92-45a1-bdc3-d8655468210c · inbound

Scaling Laws for Cross-Encoder Reranking cites this paper.

Scaling Laws for Cross-Encoder Reranking Large Dual Encoders Are Generalizable Retrievers

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T16:10:09.203457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b57cbb5f-f6d4-49cc-ac0a-68b90fecc8c9 · inbound

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation cites this paper.

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation Large Dual Encoders Are Generalizable Retrievers

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:45:21.498311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e48a87f6-2e91-48d4-9c61-ec8ad81626f7 · inbound

Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems cites this paper.

Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems Large Dual Encoders Are Generalizable Retrievers

Reference 178

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:03:08.852748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 83b255a5-9e5b-4f92-8604-13d724d73d89 · inbound

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval cites this paper.

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval Large Dual Encoders Are Generalizable Retrievers

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:24:40.428706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2633759-29b0-45c6-94f9-c3e0b664c9da · inbound

Semantic Retrieval for Product Search in E-Commerce cites this paper.

Semantic Retrieval for Product Search in E-Commerce Large Dual Encoders Are Generalizable Retrievers

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:56:15.920210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3f1ddf17-85ba-48d5-9e2d-57c366a56b93 · inbound

Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA cites this paper.

Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA Large Dual Encoders Are Generalizable Retrievers

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:46:29.346079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b99e1216-15dd-43fb-9a8d-02c078a60362 · inbound

KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking cites this paper.

KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking Large Dual Encoders Are Generalizable Retrievers

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:19:47.683163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 875d394e-2d23-4317-a925-d276e78757ad · inbound

KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking cites this paper.

KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking Large Dual Encoders Are Generalizable Retrievers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T12:48:54.633720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b04aa8e-46c3-46f4-a182-850efc0518d2 · 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 Large Dual Encoders Are Generalizable Retrievers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T05:55:32.761512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework cites this paper.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcc33d91-e5ce-449d-954c-36d460b11ac6 · inbound

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models cites this paper.

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models Large Dual Encoders Are Generalizable Retrievers

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-02T12:15:44.937361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.