Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 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

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measured 41 of 41 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 41 of 41 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:24:31.533325Z

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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QZhou-Embedding Technical Report cites this paper.

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

Reference 47

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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

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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

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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

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PRAGMA: Revolut Foundation Model cites this paper.

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

Reference 4

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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