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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-20T06:33:59.587034+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:ed9d7607b5aa17c02a7b0cb6271c852c8ffef0e768031bd51ebd8b565ef35cf2

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=sha256:176ab707961746582567228ce970fea0b6801982196beec145b2e1b4b8542714

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=sha256:7ad424aa1237dde269e242affeba8da4cfa1b0852b0247ba9412cbd6856f34c5

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T19:04:16.524882Z digest=sha256:12971f0fcd1d535edd4cfaae8bcdd3204545970cdceba5fd2680bfafec79abd7

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=sha256:368309afef92cb5d496cbe531d70d7e0a9bef89e0f35e302eeeab9213dbfdb0f

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:f2334e8744813b98eaf12feacf24934eedf5a5ccc887deb6c7d7c8aea0ccf6fb

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:aa766b89d21fbb4dfca936c73b4396cecc1a07a71dfe3e654122811363abe13c

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:e667f5c0e71a0a7eddba8d4ab84dc010b4555f1fa17e69e1ea4da4315cdff7ed

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=sha256:7ebb3efdefacf65acc7868f202933ac1563e70651197f145a5d1dc2e4142da4b

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 2026-08-20T06:33:59.587034+00:00.

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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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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:c7a7195b796b2706ed9c499fadc8686509a7de7b395af28c034f32472a3a6408

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:efe915fa1cf74dbd4d49f18aaf2528dbb77b45fb193d40680e6c1e4eb101f7a2

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=sha256:89734bd55f96cf21ed12d25cb33c8c699c3db7ac7c1775ae82a7d6ecdf04e6cb

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=sha256:7636f88929b49461de8cd8bdb14642ca176acfa3cbd84aeccdb419308849e776

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:cec7b70388c5111858ec2098c280d88c1d0b05d1d5dc50d7c844f31f0160c7fa

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=sha256:6f9754329ef5a520cb012ad9eabeaa8e84bd528507143a031c1e8093b25230d1

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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source=arxiv_source observed=2026-08-15T19:04:16.600381Z digest=sha256:74c57ac71d240f7e711d7402cc27390b2103d53747919373718e489f236e1ed7

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:bf410e518fa0ea7a493f6878a3d6b4d1099f595f2d33e6438af814183d61626c

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-15T19:04:16.608591Z digest=sha256:7effcc46c0a5f776f964d5495d9ae21a23c3d9cf68b741448c0c6f6474aea1b2

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=sha256:0d8ef75d8dbebe0aceb69271f54c7acc2fe2e75298ea723c67d9342df4895bf6

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=sha256:77c81ec560cb298c60b0a6c5c2813a32f109a4868736d27c31f9abee6193b8c5

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:bfdc17956f4f315efb6746aa4f82f66ed8e547373bfd44a16d6dbf677e006f21

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=sha256:7ab61bb300dc505e2ddfe6d690ee876dc2122f708b7d4c0ae602ea62863282fb

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=sha256:68386d8f3ce1fc93a5e20a90b5e34c088fd9ec201ffa02d39178d6c650aaa4ef

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 2026-08-20T06:33:59.587034+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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local_arxiv, observed 2026-08-15T19:04:16.875192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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:ed837e1825976e38d7494f0f4b06e1ceb8384eda603f42a2ae664ed26f3d6050

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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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:e3db8ef1f126a9a319a74dedcf4df5026c02f732a1888e544e7a0fd708b87977

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

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source=arxiv_source observed=2026-05-10T06:14:28.893342Z digest=sha256:117b2c9d019ab2e017132881334b7a5497c0b44b1f7c107e26d13f3367366391