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Paper Citation Record · LEDGER

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2607.18130.

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

pith.paper-citation-record.v1
2607.18130 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:58:13.200554Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

41 of 41 outbound references displayed

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Outbound references

Observation 0c0650aa-3c57-4249-82f5-fee5411af4da · outbound

This paper cites Dua, D., Wang, Y ., Dasigi, P., Stanovsky, G., Singh, S., and Gardner, M.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Dua, D., Wang, Y ., Dasigi, P., Stanovsky, G., Singh, S., and Gardner, M

Reference 6

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source=pdf_text observed=2026-08-01T15:58:13.089531Z digest=sha256:29ee2039742c83cddcb4fbd3aad51a1c22ef891944eebe0d12f60f4299545c5f

Observation 558ecc3a-2ea7-4016-8d85-9b4cd7498c1d · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 7

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source=pdf_text observed=2026-08-01T15:58:13.093283Z digest=sha256:18608b753b4a34b543f398a2040ab4298c2344c1662f25f1f8a46331f14c0e3f

Observation 1a02a1a2-e0d7-4a29-b49f-d6514b60a057 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 8

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Observation c3e38934-2bce-45f8-be97-8e43b183d524 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Parameter-Efficient Transfer Learning for NLP

Reference 10

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source=pdf_text observed=2026-08-01T15:58:13.104431Z digest=sha256:e815d56aca40dfa1f8989d934a69d9eb8777e978a58b04301c7a3b597ada9d43

Observation 1635c512-e13b-4406-9f11-dcc9442ebd1f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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source=pdf_text observed=2026-08-01T15:58:13.107849Z digest=sha256:65567ed56a3383980fee20f638f8a5d26542fcc450cbc2d788b32d490b02176b

Observation def92357-f6f5-44af-803d-c9935d971c91 · outbound

This paper cites doi: 10.1073/pnas.1611835114.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning doi: 10.1073/pnas.1611835114

Reference 13

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source=pdf_text observed=2026-08-01T15:58:13.114575Z digest=sha256:c42e69e56e834f1ec23f7fadc964d14a1b00c6e0b3b423b66f381b31ebd36c3e

Observation daf8f12a-41a1-4b2d-ab02-109315fd7d7d · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning VeRA: Vector-based Random Matrix Adaptation

Reference 14

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source=pdf_text observed=2026-08-01T15:58:13.117726Z digest=sha256:3b09d51cf0c4f521f84c992422a657c1f776455cd7582d3e5392b40cf88e342f

Observation cffe9752-8e84-4174-af6d-aed9c2f7ef1d · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 16

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source=pdf_text observed=2026-08-01T15:58:13.124501Z digest=sha256:1f1063a4d98e9007f46a2c085b5a80e1f6c1e232f424e40045fcc84b5040a0ae

Observation 431f8f65-d10d-4912-beab-b490da2e36c7 · outbound

This paper cites Loshchilov, I.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Loshchilov, I

Reference 17

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source=pdf_text observed=2026-08-01T15:58:13.127410Z digest=sha256:40f160d405c8a6a14c24fa33a4728d1be18c7ab609c048c9491c6282a8d09b60

Observation b9949903-1213-4ad7-86e5-a698b8a6717d · outbound

This paper cites Decoupled Weight Decay Regularization.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Decoupled Weight Decay Regularization

Reference 18

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source=pdf_text observed=2026-08-01T15:58:13.129774Z digest=sha256:0b7f06ab0cc8f20a924685ab81bb07bb312bee305f6759fa60b326484a4624e8

Observation fb5957a7-1995-4571-9377-1c6f62b965f2 · outbound

This paper cites TBP-mHC: full expressivity for manifold-constrained hyper connections through transportation polytopes.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning TBP-mHC: full expressivity for manifold-constrained hyper connections through transportation polytopes

Reference 19

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source=pdf_text observed=2026-08-01T15:58:13.133012Z digest=sha256:49d1a4a8bfccb673f27659441f56e1f67cf85910a077e9ce40650f8db502b683

Observation c11b213d-4a95-4850-aa71-4990028b1cc5 · outbound

This paper cites Nowtricity.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Nowtricity

Reference 20

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source=pdf_text observed=2026-08-01T15:58:13.136387Z digest=sha256:e7d6482bc6a17cea30e071dbebcee57509fa9108f0e6252062ac8e7d350cc680

Observation 6b6b37d6-3b27-4c2c-af6c-dd861b4a91f1 · outbound

This paper cites Forecasting smog clouds with deep learning: A proof-of-concept.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Forecasting smog clouds with deep learning: A proof-of-concept

Reference 21

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source=pdf_text observed=2026-08-01T15:58:13.139519Z digest=sha256:0cbbdf0a88c974852ccf3b426e068b2770c1b66b011d279ec3f37c6c63bbdb77

Observation 185a4a6f-7dd7-436a-8eef-72e05fa1e19a · outbound

This paper cites 2 OLMo 2 Furious.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning 2 OLMo 2 Furious

Reference 22

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source=pdf_text observed=2026-08-01T15:58:13.142230Z digest=sha256:345d3afbfe400bdadf4a7d83ca9ebc9248b4ed88e5c95bebef4ebe9a2ec06514

Observation faee3d33-8caa-4969-90f5-fd7615808f99 · outbound

This paper cites Sengupta, B., Wang, J., and Brunswic, L.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Sengupta, B., Wang, J., and Brunswic, L

Reference 23

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source=pdf_text observed=2026-08-01T15:58:13.145190Z digest=sha256:9353b380714f1c2a1ace43712529f9341e09467e1c8f141dc6293d88791c1c91

Observation cca71c81-3550-456a-8ee6-b72657e6aaa2 · outbound

This paper cites Sinkhorn, R.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Sinkhorn, R

Reference 24

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source=pdf_text observed=2026-08-01T15:58:13.148284Z digest=sha256:6372914e4e4bf8ecb03b92c2862c115d90a6277db58c9161b3662e17cc197dee

Observation 4fef9bdb-f939-41de-a638-c82fcbf718dd · outbound

This paper cites Overtrained Language Models Are Harder to Fine-Tune.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Overtrained Language Models Are Harder to Fine-Tune

Reference 25

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source=pdf_text observed=2026-08-01T15:58:13.151051Z digest=sha256:cb818064b9e99375a0b24c1920c68c4434a68c7f6ace1868e265c06db1a39820

Observation 35dde10a-211e-4aea-9640-f587bc17b56d · outbound

This paper cites Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models

Reference 26

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source=pdf_text observed=2026-08-01T15:58:13.153990Z digest=sha256:69110815adb30dcfdefb8867e7525e9a9cc1a8493ddf73560e033cbf8873f195

Observation 95688e9a-8465-4dbd-a5c2-d7776d467071 · outbound

This paper cites Attention Is All You Need.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Attention Is All You Need

Reference 28

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source=pdf_text observed=2026-08-01T15:58:13.161909Z digest=sha256:afbfc7598d0631e080090809eccc09ba7832c605f162ac177a536118b0b475aa

Observation 5b836546-3783-4a6a-84b5-951e7f5ffb77 · outbound

This paper cites Accelerating Birkhoff Projection for Manifold-Constrained Hyper-Connections.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Accelerating Birkhoff Projection for Manifold-Constrained Hyper-Connections

Reference 30

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source=pdf_text observed=2026-08-01T15:58:13.168428Z digest=sha256:a04b405d742ec202dfc96da36c37fb458286fa024da2bed14a6cc26150954e8c

Observation 74a1add0-7c06-4ea3-add0-8eff061bd9e7 · outbound

This paper cites SpanNorm: Reconciling Training Stability and Performance in Deep Transformers.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning SpanNorm: Reconciling Training Stability and Performance in Deep Transformers

Reference 31

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source=pdf_text observed=2026-08-01T15:58:13.171476Z digest=sha256:4b46cdee5ea9f586be3755baf105c0c825e6a20ab6f3cba93805c242a9ff618a

Observation 2b4336d1-8ae6-4442-a6a9-37bdbd69aa5f · outbound

This paper cites mHC: Manifold-Constrained Hyper-Connections.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning mHC: Manifold-Constrained Hyper-Connections

Reference 32

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Observation 0cfc6edc-3a0c-4b02-90a9-d1a015636485 · outbound

This paper cites Zellers, R., Holtzman, A., Bisk, Y ., Farhadi, A., and Choi, Y.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Zellers, R., Holtzman, A., Bisk, Y ., Farhadi, A., and Choi, Y

Reference 33

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Observation d438d388-8723-424d-be0b-2c528c41d6ec · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 34

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source=pdf_text observed=2026-08-01T15:58:13.180704Z digest=sha256:547ed7f1128dec0e3606ec26b00a1d583e8e307f7f06806dd1c11ed34c85846c

Observation 5a0d45a6-7503-4380-a2bb-456655c4eb75 · outbound

This paper cites Hyper-Connections.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Hyper-Connections

Reference 35

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source=pdf_text observed=2026-08-01T15:58:13.183764Z digest=sha256:621813bba419aad5e90c456a4e79fa73a5d5cf6456af741c21e1ea3498a4fb10

Observation d5b46032-9646-4bfb-beca-43b98371c695 · outbound

This paper cites This threshold ensures that we cover more than 99% of examples in allenai/tulu-3-sft-olmo-2-mixture.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning This threshold ensures that we cover more than 99% of examples in allenai/tulu-3-sft-olmo-2-mixture

Reference 36

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Observation 8a3714c9-d880-43d9-8c60-158c4ddb30e5 · outbound

This paper cites Lengths are computed after applying the OLMo chat formatting and tokenizing with the allenai/OLMo-2-1B tokenizer.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Lengths are computed after applying the OLMo chat formatting and tokenizing with the allenai/OLMo-2-1B tokenizer

Reference 37

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Observation 71c047c3-76d8-4168-ab21-7aed25f3fb7b · outbound

This paper cites with padding to 2048 tokens) setting.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning with padding to 2048 tokens) setting

Reference 38

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source=pdf_text observed=2026-08-01T15:58:13.192050Z digest=sha256:1692de14c2e95002fc3572ac9dcfa498e05d1ff7d0236c3c0daa6c63f86009f7

Observation 09be1c33-8230-493d-bb2e-5427f75e04f1 · outbound

This paper cites Random entries are set to −∞ before applying the Sinkhorn normalisation.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Random entries are set to −∞ before applying the Sinkhorn normalisation

Reference 39

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Observation 1e7bcd78-44b3-4e9e-aed9-31f66d8f79f2 · outbound

This paper cites This diverse layout ensures a robust assessment of each finetuning method.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning This diverse layout ensures a robust assessment of each finetuning method

Reference 40

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source=pdf_text observed=2026-08-01T15:58:13.197859Z digest=sha256:899f9333dd91d23f5766f3e29768ab521e61b88a8293fa28609eda0c8bdf141e

Observation 65b50411-2101-4a2d-8fa0-f93e0b493aa1 · outbound

This paper cites For high-confidence completions, KromHC routing does not induce stream specialisation.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning For high-confidence completions, KromHC routing does not induce stream specialisation

Reference 41

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source=pdf_text observed=2026-08-01T15:58:13.200554Z digest=sha256:34006e960d48c4ee1e05452ff9687ae3d0682b7f8fc311da5f79f199b46ebeb7

Observation d9a8829d-8cc6-405c-85ec-0c050a23bacd · outbound

This paper cites Deep Residual Learning for Image Recognition.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Deep Residual Learning for Image Recognition

Reference 2015

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source=pdf_text observed=2026-08-01T15:58:13.101125Z digest=sha256:2bec694417a16c38eb666d4ceee1be66de0fdfce21b7a4452ff111847eb18f31

Observation 700fbcec-a778-465c-9072-cd26fece285a · outbound

This paper cites Residual Networks Behave Like Ensembles of Relatively Shallow Networks.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Residual Networks Behave Like Ensembles of Relatively Shallow Networks

Reference 2016

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source=pdf_text observed=2026-08-01T15:58:13.164778Z digest=sha256:d229d74eef9cd3d5b67fd14064095a24281f375ed467f61cc8850ad47ad91ad8

Observation 4714b42c-0e4a-43a3-b812-f9d18c0f04df · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 2017

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source=pdf_text observed=2026-08-01T15:58:13.111326Z digest=sha256:52ffd3e1d4c958cb69f38026434b04b4475ea93699f183e5e8bce1706bb29a50

Observation 56c52b8f-3b7f-4a44-a300-86e6e007d704 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 2019

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source=pdf_text observed=2026-08-01T15:58:13.083195Z digest=sha256:b7ff98445a08a39a88f2fd577807bcb2ae2d87d634b075ad309f3cf47e0a0e2c

Observation 685c63a1-030f-40f8-904b-0191c84d4f88 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Training Verifiers to Solve Math Word Problems

Reference 2021

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source=pdf_text observed=2026-08-01T15:58:13.086552Z digest=sha256:04324a1fa87fd4bceef8f16250c0e94d3e376f587a446e31d49c454d0a83ca83

Observation 074db230-c0f9-4896-ac96-290f8e91af09 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 2022

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Observation e7c8265a-f76b-4e79-9903-d69398a63c71 · outbound

This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 2023

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Observation 8ec186a8-37dd-4028-8479-efca0937ae7b · outbound

This paper cites Lessons from the Trenches on Reproducible Evaluation of Language Models.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Lessons from the Trenches on Reproducible Evaluation of Language Models

Reference 2024

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source=pdf_text observed=2026-08-01T15:58:13.079127Z digest=sha256:52b15d95293d90c95102e67821d1f91f3160db51ab7fb8e78be2f9ea84aa7671

Observation f21f90b1-1ef2-4bdc-bec8-a68693d9b0f1 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 2025

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source=pdf_text observed=2026-08-01T15:58:13.121260Z digest=sha256:43bf3f138d20b77e6a2a6b45fa0dd0d09f4c4deac43a13d7bbc033a296297ba6

Observation 2a583cef-b12c-4576-b69f-93a1c9e224f8 · outbound

This paper cites Analyzing Stream Collapse in Hyper-Connections: From Diagnosis to Mitigation.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning Analyzing Stream Collapse in Hyper-Connections: From Diagnosis to Mitigation

Reference 2026

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no resolver link, observed 2026-08-01T15:58:13.071245Z

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Pith citing papers

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