Paper Citation Record · LEDGER

NV-Retriever: Improving text embedding models with effective hard-negative mining

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 43 inbound Pith citation observations for arXiv:2407.15831.

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

pith.paper-citation-record.v1
2407.15831 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 43 of 43 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 43 of 43 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:22:45.665004Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 81a80292-58e2-41d4-a1eb-661b21b93488 · 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 NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:15:16.272036Z

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 8ae72314-0402-4410-beca-8afc89daffd4 · inbound

CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval cites this paper.

CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T17:24:31.533325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 389093d5-9ce0-4eff-be99-87006eb64d34 · inbound

CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking cites this paper.

CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T04:49:20.663007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06973556-c6f3-4e3f-b2fe-e17605bd06bc · inbound

OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation cites this paper.

OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T23:21:42.034842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19f94ed8-0294-41a5-89c4-e71eac7312aa · inbound

Arctic-Embed 2.0: Multilingual Retrieval Without Compromise cites this paper.

Arctic-Embed 2.0: Multilingual Retrieval Without Compromise NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T23:04:04.202870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cc42a85-7be8-416a-8b16-c6d911cb761a · inbound

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

LLMs are Also Effective Embedding Models: An In-depth Overview NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-11T13:59:01.798576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e035c6c2-8154-4e0b-8539-d85b840f0aea · inbound

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

Jasper and Stella: distillation of SOTA embedding models NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T01:03:27.690479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7969135d-66e8-4a9e-9995-4c7c43ed6abd · inbound

DIVE: Diversified Iterative Self-Improvement cites this paper.

DIVE: Diversified Iterative Self-Improvement NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T22:49:13.438989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c2d423-fe1a-4447-a50b-f3dceafbe8c5 · inbound

mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval cites this paper.

mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T20:48:29.934457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c6dc59c-17f1-4e46-a810-55f12db8d282 · inbound

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning cites this paper.

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T22:58:25.578683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0308a408-ecf0-4b7d-8d02-2f0350ce78d9 · inbound

QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines cites this paper.

QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:22:45.665004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb280214-212d-47b0-80e7-fbe246329df8 · inbound

Towards A Generalist Code Embedding Model Based On Massive Data Synthesis cites this paper.

Towards A Generalist Code Embedding Model Based On Massive Data Synthesis NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:34:54.341746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8fe9bc-9d30-40c2-b4fa-54f7cef170e9 · inbound

Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems cites this paper.

Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:10.377760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 952bd28a-2c51-4d5d-a230-44436fcf8239 · inbound

Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data cites this paper.

Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:40.517431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d12d582-e9fe-45b2-9d56-99203e5a4c1f · inbound

LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach cites this paper.

LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:34.892653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5043b857-ed03-4b01-905a-2a2d63864142 · inbound

Towards Better Instruction Following Retrieval Models cites this paper.

Towards Better Instruction Following Retrieval Models NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:49.317115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c8c0d4e-b367-4663-93eb-1233a01ea3dd · inbound

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning cites this paper.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:52.610720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e134b9-ead7-4762-9194-b70386c5198f · inbound

DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers cites this paper.

DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:08:54.972013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5999f1d-be40-4e91-92d1-7ab342cdcb7d · inbound

Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings cites this paper.

Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:49:10.489385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8abb780-f81e-475e-8322-58fcb101fc94 · inbound

LGAI-EMBEDDING-Preview Technical Report cites this paper.

LGAI-EMBEDDING-Preview Technical Report NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:14.613578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aa69b84-24ce-485e-ab82-5f8e226b508c · inbound

Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning cites this paper.

Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:26.492186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0078f67-5d0e-4c8b-b65b-9ceda893262f · inbound

Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model cites this paper.

Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:30:13.857779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 836ffbc9-c16d-4d12-9079-5be9be52b12a · inbound

Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging cites this paper.

Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:40:50.988161Z

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 0566f061-6446-4f5f-b2fe-f0a90e6c6e94 · inbound

SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension cites this paper.

SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T00:46:56.292651Z

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 981f691a-cd2b-4384-99b3-32be92be1f31 · inbound

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation cites this paper.

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T22:36:59.852304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17191518-9d7a-4ec5-b2ee-892c3d85825e · inbound

QZhou-Embedding Technical Report cites this paper.

QZhou-Embedding Technical Report NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:09.770786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53ed72a4-b2d7-4353-a500-e32a31bc84be · inbound

Boosting Data Utilization for Multilingual Dense Retrieval cites this paper.

Boosting Data Utilization for Multilingual Dense Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T19:07:21.385485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d77da2-bc7f-4bbb-bebb-40536718098c · inbound

EmbeddingGemma: Powerful and Lightweight Text Representations cites this paper.

EmbeddingGemma: Powerful and Lightweight Text Representations NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:07:20.981272Z

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 18b65a53-5e44-4c41-9285-2a563018490c · inbound

PRAGMA: Revolut Foundation Model cites this paper.

PRAGMA: Revolut Foundation Model NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T06:30:59.057731Z

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 78e4f2f7-164c-4698-9a96-cd31b573a0a5 · inbound

PRAGMA: Revolut Foundation Model cites this paper.

PRAGMA: Revolut Foundation Model NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T17:40:40.602308Z

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 022da305-5637-4aaf-b3e6-09cd0080a01b · inbound

ARHN: Answer-Centric Relabeling of Hard Negatives with Open-Source LLMs for Dense Retrieval cites this paper.

ARHN: Answer-Centric Relabeling of Hard Negatives with Open-Source LLMs for Dense Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:11:12.415411Z

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 1e2f48b2-0454-4e23-9fd3-7b1cb4b0cf83 · inbound

vstash: Local-First Hybrid Retrieval with Adaptive Fusion for LLM Agents cites this paper.

vstash: Local-First Hybrid Retrieval with Adaptive Fusion for LLM Agents NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:48:47.573769Z

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 e75e522d-30c3-4c02-8daf-7495de4a1533 · inbound

On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability cites this paper.

On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:18:32.448339Z

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 6a340bdf-cf52-4796-b609-e816fcec53c1 · inbound

Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com cites this paper.

Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:46:37.708360Z

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 63e89219-9978-4755-a8b5-31d151538a6e · inbound

MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal cites this paper.

MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:56.391131Z

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 bb4b2c6a-3a05-4e48-a1c5-504ff4dcc0ee · inbound

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation cites this paper.

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-06-28T20:42:37.979181Z

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 ec613387-82c1-4370-ad3c-eba66e6a79bd · inbound

Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval cites this paper.

Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T10:46:52.616225Z

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 ab4db2e6-78f7-4e68-a1a7-d043af9a2a49 · inbound

CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges cites this paper.

CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:59:32.645781Z

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 6b9bdfa6-9a43-4fee-a453-a0e99124cfda · inbound

Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression cites this paper.

Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-08T18:55:28.224229Z

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 ec26faaf-9f77-4e32-8b97-3f59fb0b7a1c · inbound

A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset cites this paper.

A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T07:59:44.458007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 308e440f-f577-4c79-918f-a7d451052064 · inbound

DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search cites this paper.

DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 128

Resolution: unresolved; no resolver link, observed 2026-07-30T11:17:16.871040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e7eaa0c-2550-44f2-8481-5cae4481c252 · inbound

DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search cites this paper.

DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T01:43:07.098063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e65ee079-6b4e-491f-ab4c-e4a9b03001c6 · inbound

KoVRE: Training an Efficient Embedding Model for Korean Visual Document Retrieval cites this paper.

KoVRE: Training an Efficient Embedding Model for Korean Visual Document Retrieval NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T00:17:09.248864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.