Paper Citation Record · LEDGER

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models

As of 15 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2604.23321.

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

pith.paper-citation-record.v1
2604.23321 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T15:38:00.408861Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:43:21.605201Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T13:08:08.703056Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy0
unresolved47
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e02b702a-b898-4081-8952-d92c921d1c2c · outbound

This paper cites PeerQA: A Scientific Question Answering Dataset from Peer Reviews.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models PeerQA: A Scientific Question Answering Dataset from Peer Reviews

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T15:37:59.601400Z

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Observation 5ee6f910-eab1-43f8-b805-f8b1247f76b8 · outbound

This paper cites Crema-d: Crowd-sourced emotional multimodal actors dataset.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Crema-d: Crowd-sourced emotional multimodal actors dataset

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T15:37:59.667837Z

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Observation be97176b-8999-4c0a-a844-fa4983f5c2e1 · outbound

This paper cites Clotho: An audio captioning dataset.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Clotho: An audio captioning dataset

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T15:37:59.804490Z

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Observation 5a01987c-b7af-4985-85d8-36dc2de9fffb · outbound

This paper cites Neural audio synthesis of musical notes with wavenet autoencoders, 2017.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Neural audio synthesis of musical notes with wavenet autoencoders, 2017

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T15:37:59.972570Z

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Observation 2c7d8de9-115b-4ac2-bcb8-24e954380f03 · outbound

This paper cites Finevideo.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Finevideo

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.080293Z

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Observation 567df291-9769-4a3b-9e87-6e39f217372c · outbound

This paper cites SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.186498Z

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Observation 7d38c8be-f610-428c-bb6a-caada6e2a3fe · outbound

This paper cites Omniret: Efficient and high-fidelity omni modality retrieval, 2026.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Omniret: Efficient and high-fidelity omni modality retrieval, 2026

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.216508Z

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Observation 88c07958-e6a2-462e-b220-5fb7fbe8ae45 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Scaling up visual and vision-language representation learning with noisy text supervision

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.221185Z

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Observation 4dce2d9f-e9a3-4a27-a9b4-baf509a641f9 · outbound

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

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.232600Z

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Observation 035bed14-8822-48bd-a885-6511ad96c66d · outbound

This paper cites Sophia Koepke, Andreea-Maria Oncescu, João F.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Sophia Koepke, Andreea-Maria Oncescu, João F

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.240182Z

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Observation 67175c60-b490-4eb0-8278-5b2a3856a764 · outbound

This paper cites REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.244597Z

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Observation a9735ae1-a460-4008-b20c-7a75cc8a287e · outbound

This paper cites QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.249144Z

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Observation 5014bc2a-f5d9-459a-8106-4024e52514c0 · outbound

This paper cites R2MED: A Benchmark for Reasoning-Driven Medical Retrieval.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models R2MED: A Benchmark for Reasoning-Driven Medical Retrieval

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.253808Z

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Observation 8f307f57-2ddc-4367-bdfb-511119464ef7 · outbound

This paper cites Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.258413Z

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Observation d30ecb05-8785-4015-a30b-1b2b6393d385 · outbound

This paper cites MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.262447Z

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Observation f8adaf1d-1718-4987-a065-31a019237d68 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Microsoft COCO: Common Objects in Context

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.267326Z

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Observation f7a3bb33-fcb2-4121-acc5-9fa0b2785631 · outbound

This paper cites M ulti C on IR : Towards multi-condition information retrieval.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models M ulti C on IR : Towards multi-condition information retrieval

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.271648Z

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Observation 28e923bb-262d-4c16-a02c-98b0422e43e6 · outbound

This paper cites Tools are under-documented: Simple document expansion boosts tool retrieval.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Tools are under-documented: Simple document expansion boosts tool retrieval

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.275761Z

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Observation 0ff0073b-c7b4-4c3a-955f-8a4799f8490a · outbound

This paper cites Rethinking reasoning in document ranking: Why chain-of-thought falls short.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Rethinking reasoning in document ranking: Why chain-of-thought falls short

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.279937Z

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Observation 67e29c44-eacc-4732-8ca3-6005752eb411 · outbound

This paper cites Beyond global similarity: Towards fine-grained, multi-condition multimodal retrieval, 2026 c.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Beyond global similarity: Towards fine-grained, multi-condition multimodal retrieval, 2026 c

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.284151Z

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Observation 6272ba0f-a9fa-4ace-b6d9-f7b5cf48e8cd · outbound

This paper cites Vidore benchmark v2: Raising the bar for visual retrieval, 2025.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Vidore benchmark v2: Raising the bar for visual retrieval, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.293086Z

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Observation 184c72b8-3913-44a7-ab61-9ec3da40697f · outbound

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

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.297302Z

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Observation 01e57d65-5c97-4053-922b-5dd1d66e6266 · outbound

This paper cites Tut database for acoustic scene classification and sound event detection.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Tut database for acoustic scene classification and sound event detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.302040Z

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Observation 028ba7fe-b448-4655-b5e7-d6060ebd2eae · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models MTEB: Massive Text Embedding Benchmark

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.306722Z

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Observation 39293f5e-88a8-4c26-bf5d-490f9f208b6f · outbound

This paper cites Esc: Dataset for environmental sound classification.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Esc: Dataset for environmental sound classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.311152Z

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Observation 202cfc34-a5a6-4797-831f-5ca4484e8045 · outbound

This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.315093Z

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Observation 20d2c1cc-7012-4b0c-b2fd-6d0f6a92d898 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Learning Transferable Visual Models From Natural Language Supervision

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.320072Z

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Observation 3a4cc936-d497-4b17-90c6-f22841fed508 · outbound

This paper cites A dataset and taxonomy for urban sound research.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models A dataset and taxonomy for urban sound research

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.324739Z

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Observation 43faea2c-487f-4e3a-aa09-2d2cd1a698ec · outbound

This paper cites BRIGHT : A realistic and challenging benchmark for reasoning-intensive retrieval.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models BRIGHT : A realistic and challenging benchmark for reasoning-intensive retrieval

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.328699Z

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Observation dea70d04-a3ea-4622-9e84-82c5112ba115 · outbound

This paper cites Wave: Learning unified & versatile audio-visual embeddings with multimodal llm, 2025.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Wave: Learning unified & versatile audio-visual embeddings with multimodal llm, 2025

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.333316Z

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Observation d951e97b-9864-476e-a8c4-630e725a7077 · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.337307Z

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Observation c5f4bc92-9a9f-499f-b50c-e30dd4651448 · outbound

This paper cites Audio-Visual Event Localization in Unconstrained Videos.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Audio-Visual Event Localization in Unconstrained Videos

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.345758Z

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Observation 9b9f247d-fe91-4b4a-ba01-daca43612339 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.350293Z

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Observation c2568888-543b-4bd7-84e6-0b4341796ece · outbound

This paper cites UniIR: Training and Benchmarking Universal Multimodal Information Retrievers.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models UniIR: Training and Benchmarking Universal Multimodal Information Retrievers

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.354435Z

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Observation 5391b229-b3a2-490f-a66a-8c0073909e17 · outbound

This paper cites Followir: Evaluating and teaching information retrieval models to follow instructions.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Followir: Evaluating and teaching information retrieval models to follow instructions

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.358529Z

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Observation 36e29bc2-19e1-4721-ae95-4bfb2024f01b · outbound

This paper cites KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.362244Z

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Observation 464c09f7-1feb-4ce5-afee-c9f0d26788fa · outbound

This paper cites Omni-embed-nemotron: A unified multimodal retrieval model for text, image, audio, and video, 2025.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Omni-embed-nemotron: A unified multimodal retrieval model for text, image, audio, and video, 2025

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.366089Z

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Observation 2b672c38-bce8-4dd8-9242-2a6211c585da · outbound

This paper cites GME: Improving Universal Multimodal Retrieval by Multimodal LLMs.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models GME: Improving Universal Multimodal Retrieval by Multimodal LLMs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.370131Z

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Observation c7ea29bb-2682-4a44-91af-89cce3ac53e8 · outbound

This paper cites Universal retrieval for multimodal trajectory modeling.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Universal retrieval for multimodal trajectory modeling

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.374424Z

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Observation 3cfab254-6dca-4b8c-85c5-cd7196019487 · outbound

This paper cites Deepplanning: Benchmarking long-horizon agentic planning with verifiable constraints, 2026.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Deepplanning: Benchmarking long-horizon agentic planning with verifiable constraints, 2026

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.378633Z

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Unavailable: canonical work link unavailable.

Observation 3fc5ca10-0107-49ff-b286-cfa9ff315ec6 · outbound

This paper cites LMEB: Long-horizon Memory Embedding Benchmark.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models LMEB: Long-horizon Memory Embedding Benchmark

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.382531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e3d54b0-ccb2-4b41-a7d4-705b55731d85 · outbound

This paper cites Beyond content relevance: Evaluating instruction following in retrieval models.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Beyond content relevance: Evaluating instruction following in retrieval models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.386782Z

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Observation 45a057c9-7b32-491a-a426-4369c336c0ce · outbound

This paper cites Longembed: Extending embedding models for long context retrieval.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Longembed: Extending embedding models for long context retrieval

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.391174Z

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Observation c40114be-62df-4879-a0a3-9c32f514c65b · outbound

This paper cites write newline.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models write newline

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.395123Z

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Observation e4e4c121-51aa-4709-887a-9bd4237aa105 · outbound

This paper cites @esa (Ref.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models @esa (Ref

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.399852Z

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Observation 27276313-a837-4dc6-a01f-9404f16bcdf8 · outbound

This paper cites an unresolved cited work.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.404485Z

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Observation 541c426a-6413-4678-822d-15a9127f99ae · outbound

This paper cites J]m | 3M6SL1 `֭N; ޤ Q4 t<묳q >d DF6I>T[ f v-֫ ?xj fڵ] bպFފ 8 Z.

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models J]m | 3M6SL1 `֭N; ޤ Q4 t<묳q >d DF6I>T[ f v-֫ ?xj fڵ] bպFފ 8 Z

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T15:38:00.408861Z

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Pith citing papers

Observation 8a761262-7854-419e-b4e3-46198bf851f4 · inbound

CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring cites this paper.

CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-03T13:08:08.705595Z

Source-reported events for the cited work

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Observation f56c67d7-d7e8-412b-aefc-7ba796178cab · inbound

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding cites this paper.

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T11:43:21.605201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.