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Paper Citation Record · LEDGER

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2506.17781.

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

pith.paper-citation-record.v1
2506.17781 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:04:16.666931Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:18:28.864015Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:16:20.984883Z

Reference resolution

37 of 37 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ea532a5-6f99-4634-9b9b-9a21c9d5a46d · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 1

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source=arxiv_source observed=2026-08-15T19:04:16.511422Z digest=sha256:6fcd77889caada9951aa4f1cd6c7567a32665d8cbb31938d8b6a3ccc78a7446e

Observation 4753733e-7900-4321-b650-674467fdb60b · outbound

This paper cites Layer Normalization.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Layer Normalization

Reference 2

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source=arxiv_source observed=2026-08-15T19:04:16.515676Z digest=sha256:8467b87c86b5a48a915c383875996479cd4e48df798443a9ba61a6f5303bef92

Observation bbc529d0-0d41-4fbb-97b5-908c60bcb782 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-15T19:04:16.520065Z digest=sha256:16fa5f0b40c1a0a7ea993aa09d787456c8fc32b4b39709709089467a427deb6c

Observation bb52473c-6484-44d8-af05-a58454235a3c · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T19:04:16.524882Z digest=sha256:0071a0bc60317442507782ddc6b7d50de603c7c501c235a6dde9c2d45209e260

Observation 0fe9c641-22f3-4b6c-ae71-242e74f07dcf · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 5

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source=arxiv_source observed=2026-08-15T19:04:16.529287Z digest=sha256:83ebd0d373b3d638180176a8d812a18571d6fc86c8a5832ec78fe9728c3a766d

Observation d6b6fecd-81ef-4d06-af0d-2d0dfbbbf70e · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-15T19:04:16.534266Z digest=sha256:aeacadc0e46164f351d7b5b617e8c1bb3a6d1f5e1faada19783313e81b286118

Observation b1594f96-1861-4af1-b00d-ef89020cc435 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 7

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source=arxiv_source observed=2026-08-15T19:04:16.538886Z digest=sha256:365ca829f3f80ee656de61d92094e84324a4808948236432e857d3c6188b77e7

Observation d1effff1-a312-42c4-a746-1c4477ac2f63 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 8

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source=arxiv_source observed=2026-08-15T19:04:16.543466Z digest=sha256:c156ec5c0d29061370aeabc24c5ac68a5a40b735e9d91b10c507c561e2048dac

Observation 1312e39f-1b42-4200-9afa-5f4ea69f4a0f · outbound

This paper cites Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models

Reference 9

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source=arxiv_source observed=2026-08-15T19:04:16.547549Z digest=sha256:bd7882bbbf40945aab32f6567b732d43809d8dae37b17d337518854a9d812fd8

Observation 1740b463-56e5-4501-a7ab-8f6fa3a50da2 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T19:04:16.551971Z digest=sha256:d98d4d6712151e805816481c4fabe294eae0d4e02636cea3ade66f397da67f0b

Observation 169c5b97-1b61-47e1-a521-63ef42a88e7d · outbound

This paper cites Upcycling Large Language Models into Mixture of Experts.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Upcycling Large Language Models into Mixture of Experts

Reference 11

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source=arxiv_source observed=2026-08-15T19:04:16.556019Z digest=sha256:a0903ac1327a2a2ec55c2aa5ef60382e94f1b35e973dd21d707aabab90ac15cb

Observation a478797b-96a2-4096-9f69-349156bac4e1 · outbound

This paper cites Mixtral of Experts.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Mixtral of Experts

Reference 12

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source=arxiv_source observed=2026-08-15T19:04:16.560636Z digest=sha256:6d0d65c3b25d53d00f2e0daefb6560a7afc1de20d7495086da978596c2782ffb

Observation d5dcdc78-c5ab-4025-9050-0c6f9e1cc6f1 · outbound

This paper cites Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 13

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source=arxiv_source observed=2026-08-15T19:04:16.565265Z digest=sha256:584b2faf174001d5e599d2da28893b58b269154c564872ab03bd7f423b6c1f50

Observation 84ec9c00-a74d-4426-b91a-b1880c9f8329 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 14

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Observation 4ed120ab-ee2b-47f2-a832-c48dfc808c12 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 15

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source=arxiv_source observed=2026-08-15T19:04:16.573801Z digest=sha256:6e96ee8a1f3bbd122d6f8c0b0f689fefc81950163c4c2d6d0cb11f7d34ba8931

Observation be9bd6ac-f0fe-4891-b8a2-b4f1136996f1 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 16

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Observation 99cb1156-5dac-48dd-abed-75ed9553b0ef · outbound

This paper cites Generative Representational Instruction Tuning.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Generative Representational Instruction Tuning

Reference 17

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Observation c9484877-f797-4019-b783-6b57c64e6a8d · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models MTEB: Massive Text Embedding Benchmark

Reference 18

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source=arxiv_source observed=2026-08-15T19:04:16.586413Z digest=sha256:7d71bc7491d412fa5da1fa06b03bb59dff74059282c7f68270ebccc5ea2f2b61

Observation e5fcfaab-0ba4-4943-bae0-54f2afc2af10 · outbound

This paper cites Text and Code Embeddings by Contrastive Pre-Training.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Text and Code Embeddings by Contrastive Pre-Training

Reference 19

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source=arxiv_source observed=2026-08-15T19:04:16.590563Z digest=sha256:9dda1d83574bc71cdc75ab8ac23c3f55f03db7e7ef79c03dcddccbc3be445419

Observation 48984a02-610f-44f6-a6c5-87059c8f454d · outbound

This paper cites Nomic Embed: Training a Reproducible Long Context Text Embedder.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 20

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source=arxiv_source observed=2026-08-15T19:04:16.595386Z digest=sha256:8fdc8d51348a93ea5a4817558f18c69520f8a7e6b870e1651c07fc2f9a02bf2c

Observation c0f37475-2620-47f7-8648-cac033056891 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 21

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Observation 103c5f08-4f8b-415e-897c-b59acb181ee6 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Representation Learning with Contrastive Predictive Coding

Reference 22

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source=arxiv_source observed=2026-08-15T19:04:16.604497Z digest=sha256:56b8c5de4d5b29ea33e68baa22431952bdb0da9ffb449607c443c780e674e5ec

Observation 114d2067-af20-480e-888c-2ccd5098632f · outbound

This paper cites Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T19:04:16.608591Z digest=sha256:cf546cd85e53fd6e62284b62ebfb52ddfc9c9bbc3e38621be2f3335424590934

Observation 6670f640-e5a4-4183-9ca3-80af65013b1d · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 24

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source=arxiv_source observed=2026-08-15T19:04:16.612633Z digest=sha256:84ec511a3aa29d681b77e578fa71a137a1dfdf97aee193d59cd6ddcc315b7900

Observation 93fdc0ae-c6b8-4c5e-b550-4a3f12548709 · outbound

This paper cites One Embedder, Any Task: Instruction-Finetuned Text Embeddings.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models One Embedder, Any Task: Instruction-Finetuned Text Embeddings

Reference 25

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source=arxiv_source observed=2026-08-15T19:04:16.616530Z digest=sha256:820ed7ee9c07872bce49258b02c7454ec3d8d20d84fec93ddb5ca407d25c9877

Observation 3e4b247e-f54f-4d6b-88ab-ed5ca114cdfc · outbound

This paper cites Understanding LLM Embeddings for Regression.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Understanding LLM Embeddings for Regression

Reference 26

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source=arxiv_source observed=2026-08-15T19:04:16.621321Z digest=sha256:d091617ccf57eb11438afc2e504282eb04484dca44191b13d3a0465d1c2c02d7

Observation 431eda90-0064-4b1e-84d9-4db53301e06b · outbound

This paper cites The information bottleneck method.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models The information bottleneck method

Reference 27

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source=arxiv_source observed=2026-08-15T19:04:16.625478Z digest=sha256:03b2b760c1c609e23faed267d6f627725078b844b8ba78527b6065eddf1857aa

Observation b4e8a1d6-f50c-4dda-82f9-b303f83e4eef · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 28

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source=arxiv_source observed=2026-08-15T19:04:16.630076Z digest=sha256:5d7038dd4fd2f888d55a2519179c19e9087c6ca8bd29a781664b24947074227c

Observation c022c87b-a518-4d5b-a1de-d435c5b35115 · outbound

This paper cites Improving Text Embeddings with Large Language Models.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Improving Text Embeddings with Large Language Models

Reference 29

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Observation 155f2d82-08f4-408c-befc-228f76f9d5de · outbound

This paper cites Multilingual E5 Text Embeddings: A Technical Report.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Multilingual E5 Text Embeddings: A Technical Report

Reference 30

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Observation cca53b07-7e74-4714-8b26-b0fb928a4387 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 31

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Observation 16fdde60-9b68-41a7-ac61-dc06271d4a4e · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cf89cc58-8960-41c0-9346-09098c8a073c · outbound

This paper cites COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9fbeef19-8a33-469c-9f4a-02906850a957 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 34

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Observation eb4d1faf-76fc-41e4-891e-8f4584374ff9 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 35

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Observation a9e7bb63-be4d-40f3-8c76-5445833863af · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 36

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d438bcb3-7c3c-45ae-8db5-78ece9a241b8 · outbound

This paper cites an unresolved cited work.

Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models Unresolved cited work

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:04:16.666931Z digest=sha256:b5dc8862a5715f9e30ac4d6f672ada648b90be7a4f4ccec6de4d3d7d7fe8f694

Pith citing papers

Observation 87b462eb-8bd6-4926-afd0-7df29d3c4777 · 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 Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models

Reference 86

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unresolved
no resolver link, observed 2026-08-07T05:18:28.864015Z

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source=pdf_text observed=2026-08-07T05:18:28.864015Z digest=sha256:dd079c4de38782ca3616c5ff4d813a8d6d069a268808b580e5fd38a67fb60293

Observation d267bc57-a095-484e-b70b-527100b79053 · inbound

REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning cites this paper.

REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models

Reference 59

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verified exact
arxiv_id, observed 2026-05-10T06:16:20.986162Z

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source=arxiv_source observed=2026-05-10T06:14:28.893342Z digest=sha256:d5ef55d72d5ae89cbf14455fc821ba8517340c6653858654cf5ab08fb494ebd9