LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders | Paper Citation Record

Observation 981aadc1-a5bb-4c05-aa70-39b27e638f5c · 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 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 125

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Observation 7f6b63ca-d69c-406f-9e0f-85dcba94540e · inbound

VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks cites this paper.

VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:19:43.936821Z

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Observation d863794c-a206-4234-81ce-a148b90f115e · inbound

Conjuring Semantic Similarity cites this paper.

Conjuring Semantic Similarity LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:25:44.826574Z

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Observation a3202410-92f2-4e2e-af6a-1c9c5f31677c · inbound

Are Decoder-Only Large Language Models the Silver Bullet for Code Search? cites this paper.

Are Decoder-Only Large Language Models the Silver Bullet for Code Search? LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:43:19.173781Z

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Observation c4c56f93-3de8-484b-a1ad-86a340e98125 · inbound

FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training cites this paper.

FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T18:38:18.810790Z

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Observation f077c834-90c2-49f0-b762-498a3eeffb43 · 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 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T17:24:31.324152Z

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Observation 1aa3d470-b011-4e3b-82f5-12b41663e384 · inbound

Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems cites this paper.

Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T16:49:20.357002Z

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Observation 1b0194e5-a023-4391-b0b1-b46ebc115e2c · inbound

Adaptable Embeddings Network (AEN) cites this paper.

Adaptable Embeddings Network (AEN) LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T15:57:59.183196Z

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Observation 7f0432e0-a510-443f-8071-a958303f037c · inbound

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval cites this paper.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T14:02:28.546032Z

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Observation 31fbc9c4-a06d-4694-896e-45314cf6334a · inbound

Mimir: Improving Video Diffusion Models for Precise Text Understanding cites this paper.

Mimir: Improving Video Diffusion Models for Precise Text Understanding LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T22:51:11.626353Z

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Observation 779850de-bb37-4e0c-a8c2-94416504d7e9 · inbound

Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models cites this paper.

Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T21:49:26.479488Z

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Observation e0013d71-5215-48c4-94a1-0f6c85f251ff · inbound

GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model cites this paper.

GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:23:02.341587Z

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Observation 5bde0649-a4d5-4a59-b05e-7530f3345833 · inbound

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs cites this paper.

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2024

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Observation 5eeb78b2-bd0d-453b-bc48-c64ef524b3ea · inbound

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

LLMs are Also Effective Embedding Models: An In-depth Overview LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T13:59:01.515776Z

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Observation ebe71a4f-bfa8-415b-89d7-0fa2e098ce15 · inbound

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference cites this paper.

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-20T17:46:46.916596Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2eb7c9e4-c376-4fab-925e-eb7b2b211b1b · inbound

Efficient Long Context Language Model Retrieval with Compression cites this paper.

Efficient Long Context Language Model Retrieval with Compression LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:37.197776Z

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Observation 9aa73cc1-14ef-485d-bb15-c70063a7c3fb · 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 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:10.824082Z

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Observation 9cc407bd-e875-4d0e-b42b-19b254c8dbe4 · inbound

Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding cites this paper.

Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T22:19:56.198252Z

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Observation fef214e3-39dc-4364-8fd7-ce46d6853bbb · inbound

Multi-task retriever fine-tuning for domain-specific and efficient RAG cites this paper.

Multi-task retriever fine-tuning for domain-specific and efficient RAG LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:51.685006Z

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Observation df8b867d-55dd-4d24-bef0-2af521d9aef1 · inbound

Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models cites this paper.

Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T20:33:22.277886Z

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Observation 504eaec0-81d3-46e7-b498-2f6dd07df95b · inbound

Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning cites this paper.

Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T13:37:11.767243Z

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Observation 38c72a1e-06f0-4ffe-a68d-35db83dfe6d9 · 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 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T12:07:23.837097Z

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Observation 4ad6c6c8-a295-4d49-a59e-34bcb2f2eba4 · inbound

Context-Enhanced Contrastive Search for Improved LLM Text Generation cites this paper.

Context-Enhanced Contrastive Search for Improved LLM Text Generation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:19:39.650025Z

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Observation 6ccfb71c-904e-4c6e-9ad2-01f3f045e283 · inbound

CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass cites this paper.

CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T04:50:51.733796Z

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Observation a385e7cb-d4b9-4510-8355-8dc0aa96e912 · inbound

COSMOS: Predictable and Cost-Effective Adaptation of LLMs cites this paper.

COSMOS: Predictable and Cost-Effective Adaptation of LLMs LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:17:37.757884Z

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Observation 49bdac78-7b87-4a7e-8a5b-253aa81fc94e · inbound

Retrieval Augmented Generation Evaluation for Health Documents cites this paper.

Retrieval Augmented Generation Evaluation for Health Documents LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T23:29:50.488751Z

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Observation a6d2c961-28ab-4704-8e28-1f86be8f674e · inbound

Learning Item Representations Directly from Multimodal Features for Effective Recommendation cites this paper.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:53.894638Z

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Observation a42064f3-3b73-4413-ae38-14eb6218c682 · inbound

A Survey on Large Language Models in Multimodal Recommender Systems cites this paper.

A Survey on Large Language Models in Multimodal Recommender Systems LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 5

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Observation 35d76232-42b3-4af9-a7d1-2ff8df1d520e · inbound

The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks cites this paper.

The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T21:14:52.371339Z

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Observation c5b0f0b1-7f07-4c46-9135-5b7008db5811 · inbound

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering cites this paper.

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:29:59.591525Z

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Observation a4276df0-c917-4904-8060-67d8944eb415 · inbound

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion cites this paper.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:35.811186Z

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Observation 2efcdb27-183e-4d38-9bef-4d4d6bd073d9 · inbound

RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models cites this paper.

RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:41.865094Z

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Observation f2ef422d-cbbf-4a35-8b6e-f303bf4dd2c8 · inbound

Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization cites this paper.

Aligning Web Query Generation with Ranking Objectives via Direct Preference Optimization LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:52.214048Z

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Observation 75c105d8-9080-4f43-b9eb-44f8cfd06f79 · inbound

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning cites this paper.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:07:11.928833Z

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Observation 6faeae43-7882-4ac2-866a-d8d7d4ec0ae8 · inbound

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants cites this paper.

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 5

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Observation 4b9cca04-c59e-4eda-832f-df28a1e25d0b · inbound

Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis cites this paper.

Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:09.714262Z

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Observation 3f6fa476-e31e-4774-a2e8-819886b163df · inbound

Rethinking the Understanding Ability across LLMs through Mutual Information cites this paper.

Rethinking the Understanding Ability across LLMs through Mutual Information LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:37.128322Z

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Observation d623f535-7b51-44f7-9ac5-bc651b6c24d8 · inbound

Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine cites this paper.

Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:21.147556Z

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Observation 9700de77-2f49-4fa5-948a-12517ac0b089 · inbound

GEM: Empowering LLM for both Embedding Generation and Language Understanding cites this paper.

GEM: Empowering LLM for both Embedding Generation and Language Understanding LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.918489Z

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Observation 0093706d-8d60-4c5d-9726-d5d47a7c58f8 · inbound

Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation cites this paper.

Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:13.523925Z

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Observation b83b9bae-75a0-4cf2-a204-67258a38cd79 · inbound

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation cites this paper.

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:40.052145Z

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Observation ab40591d-d683-4c56-bf2c-45a6aee65fe5 · inbound

VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code cites this paper.

VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:43:40.436145Z

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Observation 351ca816-5b99-4256-b334-4fef06ce5261 · inbound

LGAI-EMBEDDING-Preview Technical Report cites this paper.

LGAI-EMBEDDING-Preview Technical Report LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:11.043339Z

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Observation 1634a2a4-269c-4570-bf60-f9799e1fad0e · inbound

A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation cites this paper.

A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:20:50.990041Z

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Observation 4b2b29b1-c641-49bc-a87b-36a201cb65c0 · 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 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:20.119684Z

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Observation 6f85c023-6ba3-4c4a-a1df-3a3ba1b66af0 · inbound

Build the web for agents, not agents for the web cites this paper.

Build the web for agents, not agents for the web LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:00.528944Z

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Observation 353b9b99-d5c4-4d5c-a71b-0a13aa93f396 · inbound

Maximally-Informative Retrieval for State Space Model Generation cites this paper.

Maximally-Informative Retrieval for State Space Model Generation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:44.435338Z

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Observation 2fde75fb-a65f-4be2-9504-c391948ee8a7 · inbound

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach cites this paper.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.529674Z

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Observation 1377ea72-eaeb-4af4-a808-8d6eb4083db8 · inbound

DeepRTL2: A Versatile Model for RTL-Related Tasks cites this paper.

DeepRTL2: A Versatile Model for RTL-Related Tasks LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:18:29.941299Z

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Observation 256f10be-97c1-4856-a0c5-3e9623b7c98d · inbound

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows cites this paper.

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:25:14.817524Z

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Observation 4de17cf5-7582-4c24-b873-a982e76b35cc · inbound

AI-Generated Song Detection via Lyrics Transcripts cites this paper.

AI-Generated Song Detection via Lyrics Transcripts LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:35.248071Z

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Observation 0154a9d2-35b0-4961-8bd2-5473cd39f33c · 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 LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:22.610379Z

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Observation 435d780e-2ef9-4dae-8ff7-148ce382aeef · inbound

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings cites this paper.

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:21.664520Z

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

A Comparative Study of Specialized LLMs as Dense Retrievers cites this paper.

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

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:17.475130Z

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Observation b70ffd83-42af-4b5e-97e6-ddfb9241c86d · inbound

VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents cites this paper.

VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:10:15.091863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 275cb846-a7a8-4bcc-be1c-af531d939f5c · inbound

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems cites this paper.

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T05:32:05.797719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e22b1de-5a12-42b0-a751-1a61a256b7b8 · inbound

Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding cites this paper.

Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:23.414675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87b4797a-80fd-40fe-b084-9d62a56e0fe9 · inbound

Learning Robust Negation Text Representations cites this paper.

Learning Robust Negation Text Representations LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:22.786772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5e5cc4a-67ba-4cb0-80c5-a35b539de76b · inbound

Closing the Modality Gap for Mixed Modality Search cites this paper.

Closing the Modality Gap for Mixed Modality Search LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:09:45.094199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ca77f80-95db-4f52-934b-76f5a24ccb5f · inbound

HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens cites this paper.

HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T05:40:40.506023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d459a743-4364-42f5-9dd6-2deee0ca27bb · inbound

ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors cites this paper.

ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T23:01:43.083376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5b2bb0b-a9d4-493f-965c-4defdf5ed287 · inbound

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

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T22:36:59.833869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7905d273-54a3-478d-8e35-cb004aa44dc6 · inbound

SemSR: Semantics aware robust Session-based Recommendations cites this paper.

SemSR: Semantics aware robust Session-based Recommendations LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T16:46:20.797729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f98fc966-2678-4c02-83fa-25460abe227f · inbound

Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval cites this paper.

Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T13:50:09.579096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2468202-98ee-4903-bab5-b43edfdd0f9f · inbound

Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings cites this paper.

Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T13:16:26.726312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88c899ea-67f1-44f1-abdf-aa60c151c162 · inbound

Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation cites this paper.

Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T05:10:59.772222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 154bf04b-67f3-4a23-8773-7b730e855051 · inbound

HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data cites this paper.

HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T23:41:24.787406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84482a07-86cd-402f-b69b-e7bf781c5ddb · inbound

Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization cites this paper.

Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T18:37:54.946479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce10fec5-2717-4232-ad73-6adee0f3c208 · inbound

Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays cites this paper.

Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T16:33:14.837168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb0415c-1d99-40a5-9801-a667f3920d8d · inbound

Unpacking Hateful Memes: Presupposed Context and False Claims cites this paper.

Unpacking Hateful Memes: Presupposed Context and False Claims LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T10:26:19.643324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7694661a-6ef7-446a-8bea-4fe6d638d1aa · inbound

Enhancing next token prediction based pre-training for jet foundation models cites this paper.

Enhancing next token prediction based pre-training for jet foundation models LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T18:42:09.832935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8ca5363-7a78-41d4-885a-2a8be1e2e25d · inbound

STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation cites this paper.

STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T15:43:38.958278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7846c4d4-c0c6-4d35-93e9-3712c3353134 · inbound

Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning cites this paper.

Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T19:41:30.601697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff9c7b95-8e6c-4db7-b043-f189efbe22f1 · inbound

SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation cites this paper.

SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T18:16:44.854459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29735991-3e6a-4325-b222-e3c8dae88b54 · inbound

InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories cites this paper.

InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:13:01.294624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d3542ab1-a2b6-4bfb-bd69-0c5426622ea2 · inbound

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation cites this paper.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:00.039662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d95bd3d-6d53-462d-ac25-43bdb82aacc6 · inbound

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization cites this paper.

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:06:03.148741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c9b37868-7d61-406a-9328-849016668053 · inbound

mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval cites this paper.

mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:51:46.470826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 99cb1066-d728-4998-b85f-fe5b05359b8c · inbound

RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models cites this paper.

RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 115

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:35:19.255548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02633d5e-7cc5-4598-bc26-a71f1c5adbc9 · inbound

Latent Abstraction for Retrieval-Augmented Generation cites this paper.

Latent Abstraction for Retrieval-Augmented Generation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:01:05.024948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f27cc4e3-fa63-4fe5-a3ce-4abe6f753f44 · inbound

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations cites this paper.

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:06:04.683141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ccb06467-0240-44d4-9e3d-3c20a5cf92da · inbound

AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce cites this paper.

AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:59:49.721292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 013bc601-0685-4daa-a026-7cb7fc5bee2f · inbound

Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues cites this paper.

Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:06:20.339469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d1464a5b-aeca-40be-a7dd-e295164ad2c7 · inbound

Anticipating Innovation Using Large Language Models cites this paper.

Anticipating Innovation Using Large Language Models LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:46:08.276282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d3eb9b61-dd0a-413b-b66d-f29fe0365f3b · inbound

Think When Needed: Adaptive Reasoning-Driven Multimodal Embeddings with a Dual-LoRA Architecture cites this paper.

Think When Needed: Adaptive Reasoning-Driven Multimodal Embeddings with a Dual-LoRA Architecture LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:53:33.616969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3bf6a398-5bed-479c-9a54-6162933fd109 · inbound

MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis cites this paper.

MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:43:44.211209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a35ba5d6-146e-4470-9743-c499b40ef316 · inbound

Towards Generalizable and Efficient Large-Scale Generative Recommenders cites this paper.

Towards Generalizable and Efficient Large-Scale Generative Recommenders LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:56:36.903000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80e437a7-0e59-4b47-8e7e-cd1b88c16a5f · inbound

HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval cites this paper.

HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:26:35.875141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 216b06a3-8ca7-459e-be9c-aadd45e4b950 · inbound

OmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation cites this paper.

OmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:13:48.582735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 259f2aa6-9079-48ff-bb81-37dd63403034 · inbound

On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets cites this paper.

On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:26:00.236607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6fb80816-b5b8-47d0-be37-046f52943cf6 · inbound

Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues cites this paper.

Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:26:47.955642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6daadeeb-063a-4b28-808f-69fc282c03be · inbound

Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddings cites this paper.

Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddings LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:27:15.416381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c53ba7eb-472d-474d-b90f-19cb97f03c12 · inbound

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs cites this paper.

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:03.235501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c299702f-3c7d-43de-b72b-7073b2c1402e · inbound

Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation cites this paper.

Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:19:13.214539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 81bfadde-716b-4849-9e78-031f7d186b65 · inbound

One Generator, Any Process: LLM-Conditioning for the LHC cites this paper.

One Generator, Any Process: LLM-Conditioning for the LHC LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 240

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T11:39:46.504860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4cb0717f-073d-40a4-800b-c056085a29f0 · inbound

One Generator, Any Process: LLM-Conditioning for the LHC cites this paper.

One Generator, Any Process: LLM-Conditioning for the LHC LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 244

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T10:14:36.036714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d7776bc-1a5b-46d6-b175-f5d537560f09 · inbound

Probe, Don't Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAG cites this paper.

Probe, Don't Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAG LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-11T22:57:36.974470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7607ba1a-784f-4d99-9fce-0bc796f2af15 · inbound

IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment cites this paper.

IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T02:03:23.538054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dd964af-ecc0-4d72-b6a0-0b501e81ebdb · inbound

Illuminating Visual Identity in Universal Multimodal Embeddings cites this paper.

Illuminating Visual Identity in Universal Multimodal Embeddings LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T20:43:13.611224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48a36bdc-d2e1-49d6-a339-1d6a8777b9e8 · inbound

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes cites this paper.

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T07:49:39.864281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers