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Paper Citation Record · LEDGER

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding

As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.16316.

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

pith.paper-citation-record.v1
2607.16316 v1

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measured 23 of 23 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-02T05:15:08.455583Z

measured 23 of 23 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

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23 of 23 outbound references displayed

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Outbound references

Observation 154e6561-452b-4a39-b107-90ea207ebce3 · outbound

This paper cites Qwen3-VL Technical Report.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Qwen3-VL Technical Report

Reference 1

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source=pdf_text observed=2026-08-02T05:15:06.080203Z digest=sha256:7f31b748bb65fe502ba0a74af59b5f835f17b758677fc34b14833d5c5f530daa

Observation 67a9010d-4dd9-4805-993a-f877f53c109c · outbound

This paper cites Eddy-VL Embedding 1.9B.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Eddy-VL Embedding 1.9B

Reference 2

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Observation c7dd6949-ec8d-4e82-bbbb-66fb97fbc756 · outbound

This paper cites FlashAttention: Fast and memory-efficient exact attention with IO-awareness.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding FlashAttention: Fast and memory-efficient exact attention with IO-awareness

Reference 3

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Observation b46c1a4b-afd0-496e-a931-65d20a4d58d4 · outbound

This paper cites With limited data for multimodal alignment, let the STRUCTURE guide you.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding With limited data for multimodal alignment, let the STRUCTURE guide you

Reference 4

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source=pdf_text observed=2026-08-02T05:15:06.396884Z digest=sha256:bc9960394af3d3bdf6bcfd48b541a4a3298907850faf5be983a2f589ba72081a

Observation f574acd2-b5aa-4509-91ad-698254da94a6 · outbound

This paper cites Evaluation of deep convolu- tional nets for document image classification and retrieval.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Evaluation of deep convolu- tional nets for document image classification and retrieval

Reference 5

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Observation 13e44443-c9c9-4f71-8bbb-2cfde7a35629 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Distilling the Knowledge in a Neural Network

Reference 6

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Observation e3186d75-4cc9-4391-b154-e5fd8124c931 · outbound

This paper cites SugarCrepe: Fixing hackable benchmarks for vision-language compositionality.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding SugarCrepe: Fixing hackable benchmarks for vision-language compositionality

Reference 7

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source=pdf_text observed=2026-08-02T05:15:06.744074Z digest=sha256:04e2b93125a211696b1d2984823af342f452e1d43e9f2ed8e2c40bf37f554c86

Observation 563d6d1d-0ad0-45b8-b168-792c9ffef967 · outbound

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

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks

Reference 8

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Observation d28c07ae-dd08-4db2-9c2d-1b980ec138a8 · outbound

This paper cites Korean Image Captioning Dataset (ai hub dataset no.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Korean Image Captioning Dataset (ai hub dataset no

Reference 9

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Observation b2d70048-07b6-48b9-914f-a0080d4384ba · outbound

This paper cites Similarity of neural network representations revisited.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Similarity of neural network representations revisited

Reference 10

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Observation c9506c1e-5d5d-4ece-84e2-6236eea7b3f1 · outbound

This paper cites Matryoshka representation learning.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Matryoshka representation learning

Reference 11

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source=pdf_text observed=2026-08-02T05:15:07.075107Z digest=sha256:a94214a51ae8157176e609791fcc2b66db326ebf7e3962c49e1959b209ea23ae

Observation 97b1c02b-9a0a-4e70-a318-a840abea2f27 · outbound

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

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

Reference 12

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Observation 1359d9fe-fd01-46a4-823d-c3fbdbcb22f6 · outbound

This paper cites Lawrence Zitnick.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Lawrence Zitnick

Reference 13

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Observation 4696e332-5fff-4ef7-865f-d2c2a64da1f2 · outbound

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

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents

Reference 14

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Observation beb8be55-3efe-4a0b-8aa9-3b8ea445758b · outbound

This paper cites AI Hub (korean public ai training data portal).

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding AI Hub (korean public ai training data portal)

Reference 15

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Observation aa3e834b-9554-4aac-a4d8-c4d66fe6e90d · outbound

This paper cites CORD: A consolidated receipt dataset for post-OCR parsing.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding CORD: A consolidated receipt dataset for post-OCR parsing

Reference 16

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Observation 5fef0d74-1944-4c89-af3d-7a6c5afe9d85 · outbound

This paper cites Relational knowledge distillation.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Relational knowledge distillation

Reference 17

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Observation 4d1cb976-6544-40f9-9e55-0e55d54bc43e · outbound

This paper cites Qwen3-VL-Embedding-2B.https://huggingface.co/Qwen/ Qwen3-VL-Embedding-2B, 2025.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Qwen3-VL-Embedding-2B.https://huggingface.co/Qwen/ Qwen3-VL-Embedding-2B, 2025

Reference 18

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Observation 8ff266fe-52f5-456a-905b-6cec89546894 · outbound

This paper cites Winoground: Probing vision and language models for visio-linguistic compositionality.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Winoground: Probing vision and language models for visio-linguistic compositionality

Reference 19

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Observation 3bd65173-dc16-4d54-90d1-c01d724a06c5 · outbound

This paper cites SUN database: Large-scale scene recognition from abbey to zoo.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding SUN database: Large-scale scene recognition from abbey to zoo

Reference 20

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Observation 32c90110-76c5-45d2-9976-3c1f6309dc94 · outbound

This paper cites When and why vision-language models behave like bags-of-words, and what to do about it? InICLR, 2023.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding When and why vision-language models behave like bags-of-words, and what to do about it? InICLR, 2023

Reference 21

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Observation b70fe2da-0fe6-4071-a673-012155ebf6e8 · outbound

This paper cites MR2-bench: Going beyond matching to reasoning in multimodal retrieval.arXiv preprint arXiv:2509.26378, 2025.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding MR2-bench: Going beyond matching to reasoning in multimodal retrieval.arXiv preprint arXiv:2509.26378, 2025

Reference 22

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Observation ac9d0cfb-aad1-47cc-8bad-e0598e30e3e0 · outbound

This paper cites Rethinking Centered Kernel Alignment in Knowledge Distillation.

Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding Rethinking Centered Kernel Alignment in Knowledge Distillation

Reference 23

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