Paper Citation Record · LEDGER

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training

As of 17 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2506.10952.

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

pith.paper-citation-record.v1
2506.10952 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:11.124746Z

measured 56 of 56 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T00:56:04.958757Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T00:58:25.710039Z

Reference resolution

55 of 55 outbound references displayed

verified exact3
verified fuzzy21
unresolved31
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5150f381-6e00-4937-a600-9f7df201e13c · outbound

This paper cites Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Reference 1

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Observation 20a927a0-ef93-482f-9dce-ace099b5b482 · outbound

This paper cites and Vassilvitskii, S.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training and Vassilvitskii, S

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:19.885756Z

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Observation e622fe09-9584-446d-8696-f8235fbc7bf7 · outbound

This paper cites L., Gao, J., and Choi, Y.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training L., Gao, J., and Choi, Y

Reference 3

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Observation ca118c46-9f2f-4d5f-8f25-5724cf2b0f37 · outbound

This paper cites Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Data

Reference 4

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Observation 6e43ce2c-2b94-47ff-88f7-1355d77d3822 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67c0f7c-b241-45bd-9131-ad2d9b7e24a1 · outbound

This paper cites Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 6

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Source-reported events for the cited work

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Observation 4fc679f5-287e-45e9-84f6-f825520e8cd5 · outbound

This paper cites DOGE : Domain reweighting with generalization estimation.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training DOGE : Domain reweighting with generalization estimation

Reference 7

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Source-reported events for the cited work

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Observation d7b03bc9-e2ae-4f46-a8bf-54061c6bc317 · outbound

This paper cites Unearthing Large Scale Domain-Specific Knowledge from Public Corpora.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Unearthing Large Scale Domain-Specific Knowledge from Public Corpora

Reference 8

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Observation 795ba27a-4d48-4890-8f46-906a2005cd65 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:05.821830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb5ad669-b4d0-44a5-9ca1-045173519542 · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training A framework for few-shot language model evaluation, 07 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:05.974554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee14808c-4cd9-46ba-ba7e-91720ffc2156 · outbound

This paper cites BiMix: A Bivariate Data Mixing Law for Language Model Pretraining.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training BiMix: A Bivariate Data Mixing Law for Language Model Pretraining

Reference 11

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Source-reported events for the cited work

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Observation 6b3fe130-c88d-48fb-acfe-7a3ef6219159 · outbound

This paper cites S em E val-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training S em E val-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning

Reference 12

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Source-reported events for the cited work

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Observation 1a311c98-2493-46fa-a0c2-74b275e9c9fd · outbound

This paper cites The Llama 3 Herd of Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training The Llama 3 Herd of Models

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fa1f33a-c468-46fb-8b96-451521052e48 · outbound

This paper cites CMR scaling law: Predicting critical mixture ratios for continual pre-training of language models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training CMR scaling law: Predicting critical mixture ratios for continual pre-training of language models

Reference 14

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Observation c9b8c866-aae0-4ab3-bb09-4e4a55895a47 · outbound

This paper cites Data Selection via Optimal Control for Language Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Data Selection via Optimal Control for Language Models

Reference 15

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Observation 3037b8d0-57e6-4420-bf01-73a4c64c7fc2 · outbound

This paper cites V., and Smith, K.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training V., and Smith, K

Reference 16

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Source-reported events for the cited work

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Observation bc0ecc10-2344-4fb4-bd57-c67cda0083c9 · outbound

This paper cites H., and Friedman, J.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training H., and Friedman, J

Reference 17

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Source-reported events for the cited work

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Observation 2b4c0efb-c935-4e9f-9c93-bdc89412ff13 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Training Compute-Optimal Large Language Models

Reference 18

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Source-reported events for the cited work

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Observation a61d6b29-1bab-42bc-8290-daa94f7ee6d0 · outbound

This paper cites A., Welbl, J., Clark, A., Hennigan, T., Noland, E., Millican, K., van den Driessche, G., Damoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., Vinyals, O., Rae, J.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training A., Welbl, J., Clark, A., Hennigan, T., Noland, E., Millican, K., van den Driessche, G., Damoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., Vinyals, O., Rae, J

Reference 19

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Observation b0aca12f-c72f-4a13-a262-ec07c2960b10 · outbound

This paper cites an unresolved cited work.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 5db010b9-0585-4256-a111-bd59bea0bf09 · outbound

This paper cites S., Schmidt-Thieme, L., and Grabocka, J.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training S., Schmidt-Thieme, L., and Grabocka, J

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:17.661402Z

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Observation 3d05bc31-1160-47d7-85c3-f0b2bef852bc · outbound

This paper cites Scaling Laws for Neural Language Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Scaling Laws for Neural Language Models

Reference 22

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Observation 28b1bdb6-0513-417c-a542-9ff30d953169 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Lightgbm: A highly efficient gradient boosting decision tree

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:17.348865Z

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Observation fa5eec87-5d99-4c32-adfa-3dd4131dab22 · outbound

This paper cites Looking beyond the surface: A challenge set for reading comprehension over multiple sentences.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Looking beyond the surface: A challenge set for reading comprehension over multiple sentences

Reference 24

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Observation d515f4e5-b32b-4dbf-80d4-dad73e26adaf · outbound

This paper cites RACE : Large-scale R e A ding comprehension dataset from examinations.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training RACE : Large-scale R e A ding comprehension dataset from examinations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:07.967559Z

Source-reported events for the cited work

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Observation 2a9418f5-9f61-4c41-aca4-fbc309ea8ab9 · outbound

This paper cites Not all tokens are what you need for pretraining.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Not all tokens are what you need for pretraining

Reference 26

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Observation 3641c731-e492-4180-a9ec-9cab909a1615 · outbound

This paper cites Logiqa: a challenge dataset for machine reading comprehension with logical reasoning.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Logiqa: a challenge dataset for machine reading comprehension with logical reasoning

Reference 27

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Observation a96b27b8-a264-4bd2-9990-f3f620b06f1f · outbound

This paper cites RegMix: Data Mixture as Regression for Language Model Pre-training.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training RegMix: Data Mixture as Regression for Language Model Pre-training

Reference 28

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Observation 15ebcb6b-aab6-4b5b-b14e-3ffaaee57b9a · outbound

This paper cites Decoupled Weight Decay Regularization.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Decoupled Weight Decay Regularization

Reference 29

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Source-reported events for the cited work

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Observation e81c65ec-f9c6-461e-842d-d440cb8af013 · outbound

This paper cites Some methods for classification and analysis of multivariate observations.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Some methods for classification and analysis of multivariate observations

Reference 30

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Observation cda02639-cf84-467b-b204-4ac489126ff3 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.337236Z

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Observation 3986d171-07d7-4d1c-bec3-6d88314a4849 · outbound

This paper cites GPT-4 Technical Report.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training GPT-4 Technical Report

Reference 32

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Observation e8bb77d4-9e4d-41cc-918e-7953c2e48fe8 · outbound

This paper cites Q., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fern \'a ndez, R.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Q., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fern \'a ndez, R

Reference 33

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Observation 909bae5a-a467-4000-9335-6ef00d7e9f3e · outbound

This paper cites B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.079631Z

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Observation 51f31caa-db4d-4702-af24-3e3dcc179a9e · outbound

This paper cites D-cpt law: Domain-specific continual pre-training scaling law for large language models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training D-cpt law: Domain-specific continual pre-training scaling law for large language models

Reference 35

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Observation df65be68-87cf-4d85-b930-c297b992b76f · outbound

This paper cites Qwen2 Technical Report.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Qwen2 Technical Report

Reference 36

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Observation 128ba91b-2acf-4fdf-93c8-924dedff6d98 · outbound

This paper cites Improving language understanding by generative pre-training.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Improving language understanding by generative pre-training

Reference 37

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Observation 7ae6989d-d90e-4cca-80c3-d7965b74c2c7 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 38

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Observation 9bb47e5a-a264-4656-84c5-30e91f78cbdd · outbound

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Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Unresolved cited work

Reference 39

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Observation c7c0f0f9-6a3a-437a-9426-cb6192962d62 · outbound

This paper cites W., Hashimoto, T.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training W., Hashimoto, T

Reference 40

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Observation 02d10b5b-4148-4fd0-9184-c40bedc80ebd · outbound

This paper cites L., Bhagavatula, C., and Choi, Y.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training L., Bhagavatula, C., and Choi, Y

Reference 41

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Observation 68496c35-c5de-45b2-9d60-9de0dc544f55 · outbound

This paper cites Social IQ a: Commonsense reasoning about social interactions.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Social IQ a: Commonsense reasoning about social interactions

Reference 42

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Observation b7c78fb7-fa74-402b-a247-29b0773f37ec · outbound

This paper cites Self-influence guided data reweighting for language model pre-training.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Self-influence guided data reweighting for language model pre-training

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.942881Z

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Observation 453e0082-ae34-4f93-85eb-2bc47ef8a6ce · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 44

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Observation 567f32ab-526b-40cb-b391-f820d1d0e369 · outbound

This paper cites and Hinton, G.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training and Hinton, G

Reference 45

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Observation f881e0e2-8d5b-4e42-93fc-65d87c471d6c · outbound

This paper cites Learning Dynamics in Continual Pre-Training for Large Language Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Learning Dynamics in Continual Pre-Training for Large Language Models

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:19:11.911632Z

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Observation 2a633f51-b59d-4224-8915-6a1fad4c7ad1 · outbound

This paper cites RedPajama: an Open Dataset for Training Large Language Models.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training RedPajama: an Open Dataset for Training Large Language Models

Reference 47

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Observation 94fc3b50-0d5a-4d2e-846a-11952fb1e08a · outbound

This paper cites F., and Gardner, M.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training F., and Gardner, M

Reference 48

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Source-reported events for the cited work

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Observation 5c98e9f7-60a3-4e21-8647-30badaad4d2f · outbound

This paper cites C-pack: Packaged resources to advance general chinese embedding, 2023.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training C-pack: Packaged resources to advance general chinese embedding, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.606366Z

Source-reported events for the cited work

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Observation 2f36a394-6086-419d-92fd-cff21e86821e · outbound

This paper cites M., Pham, H., Dong, X., Du, N., Liu, H., Lu, Y., Liang, P., Le, Q.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training M., Pham, H., Dong, X., Du, N., Liu, H., Lu, Y., Liang, P., Le, Q

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.317126Z

Source-reported events for the cited work

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Observation 968da2f7-e792-43ea-8804-346f988730db · outbound

This paper cites M., Santurkar, S., Ma, T., and Liang, P.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training M., Santurkar, S., Ma, T., and Liang, P

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:13.932011Z

Source-reported events for the cited work

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Observation 024a254e-8ef7-4f2c-b59d-0c32fcacb218 · outbound

This paper cites Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

Reference 52

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This paper cites Hellaswag: Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics, 2019.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training Hellaswag: Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics, 2019

Reference 53

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Observation dbc2900e-8802-4a8e-b9b1-867931298325 · outbound

This paper cites LIMA : Less is more for alignment.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training LIMA : Less is more for alignment

Reference 54

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This paper cites write newline.

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training write newline

Reference 55

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Pith citing papers

Observation 75014b2e-a9cb-4c56-b770-895e9292b063 · inbound

Data Mixing for Large Language Models Pretraining: A Survey and Outlook cites this paper.

Data Mixing for Large Language Models Pretraining: A Survey and Outlook Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training

Reference 65

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