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Paper Citation Record · LEDGER

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 4 inbound Pith citation observations for arXiv:2501.10062.

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

pith.paper-citation-record.v1
2501.10062 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:29:51.454608Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T07:48:12.576212Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:49:44.416043Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved40
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0be3239-d0bd-47f1-a3da-07eae6cfe71f · outbound

This paper cites GPT-4 Technical Report.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning GPT-4 Technical Report

Reference 1

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no resolver link, observed 2026-08-10T19:29:51.268869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.268869Z digest=sha256:5d32a62b2f755fff03e0e563e7bf12b74f32d553e51de57884a31c949c305089

Observation 5bbdab1f-bd1b-4ef6-bcf1-3f9a064c35d6 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.273563Z digest=sha256:6f8ce103b5c52d1ac97af1c5111cf3b703b99159796e1fcbcd0742ad8acaf607

Observation 7821749f-8e0f-4e9a-9975-cbefab5905fd · outbound

This paper cites contextual.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning contextual

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.278582Z digest=sha256:f76dab609fe77851aadde5f3c732c861fdab0da6c6ed3451273d953fb90a8c37

Observation dd887de8-de48-40b5-a587-06747627b7ab · outbound

This paper cites LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.282697Z digest=sha256:a9d2a563a106c1b5b4486c8db26b7e723946f9af0364138f3bf251514c66a247

Observation 57914ead-46f1-4e2a-9bea-def8164da019 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 5

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raw_fallback, observed 2026-08-10T19:29:51.972102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.287839Z digest=sha256:1491bd905b363d1c3d40a79821462bd651c394739fa109ae13a60578dc6218cd

Observation e47fb951-1343-45b4-96f1-b616eadd9ba5 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.292170Z digest=sha256:910e8f85bea2c181e50cf5498a8161b341a32a039d5c98117ce4792af7fb54f2

Observation 43eaffee-2cbf-4d33-b56e-6afb7a72619c · outbound

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

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

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no resolver link, observed 2026-08-10T19:29:51.297553Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.297553Z digest=sha256:45b5ed31527a8f24c5a710f9dddd6f623e6274a664be58ccded606ce06e48e75

Observation 01870c25-96dd-43bb-8f46-3df6d9280450 · outbound

This paper cites LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Reference 8

Resolution
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no resolver link, observed 2026-08-10T19:29:51.301538Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.301538Z digest=sha256:7444b388ba95d825e4f31ed3af52facc860c0d67ea7f07ffbf805602badcfef5

Observation e9d8ac23-c4d5-4ce7-bf3e-efa86a05fccb · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 9

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no resolver link, observed 2026-08-10T19:29:51.305727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.305727Z digest=sha256:12f2a173cb96b3a65fd032c4f6312abec8c2c0ffc7e7f3b1f7e3ba204c683361

Observation 92bb84c7-7240-4e22-b985-dca6e718f94a · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.310928Z digest=sha256:618565b02ef1e1817b23245daf6d4234801bcdef5bdc71a74ee709277e8179c6

Observation 22137179-4a14-46aa-8d11-24f12376940f · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 11

Resolution
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raw_fallback, observed 2026-08-10T19:29:51.960724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.315039Z digest=sha256:f56e75775602186926a84dfa42bc5a529f1301ff6c97332e0da8f793c8a5e25b

Observation 3b3aecd5-822b-468f-9cbd-88eff00ee65b · outbound

This paper cites Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 12

Resolution
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no resolver link, observed 2026-08-10T19:29:51.318798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.318798Z digest=sha256:0642bf658585200cf823753e11cacd0e0904b878587b00d8737ae32f6600c86c

Observation 5a254a17-ed0e-4620-9c9c-0b0ab33be252 · outbound

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

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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no resolver link, observed 2026-08-10T19:29:51.322827Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.322827Z digest=sha256:8e46123fcf06d039254dbb04d198728ba7143bb0e23ef43d9939ab363aa1f8bc

Observation f679a7f7-164e-4c9c-83a8-bb33fd2a72c2 · outbound

This paper cites Sparse Structure Search for Parameter-Efficient Tuning.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Sparse Structure Search for Parameter-Efficient Tuning

Reference 14

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no resolver link, observed 2026-08-10T19:29:51.326533Z

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source=pdf_text observed=2026-08-10T19:29:51.326533Z digest=sha256:327c8ed877860d21a888286d898d53fff247d84d827c01316370653dc17b5795

Observation c405754f-fdaf-43ae-9b79-60b4d8fa0bc1 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.330366Z digest=sha256:271b2d64232be5cfeff16432e5c62343807af5b55342c13abeae5d5ae13d5b1d

Observation d986ccea-952c-4b22-a88d-5aaa0aa4a568 · outbound

This paper cites LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 16

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no resolver link, observed 2026-08-10T19:29:51.334002Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.334002Z digest=sha256:5484271cdccb614550da0d6d06411f4e77e84e4b413777320c5981fddf3fad5e

Observation 19776991-ed42-4261-9df4-eaf11a6233a9 · outbound

This paper cites Huang, Y.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Huang, Y

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:29:51.937809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.337961Z digest=sha256:ac1f86374eaaa93592a4611f6a1e17d2872bf5013982dbc9ffb06649cc6383c3

Observation 89a7b185-6184-4726-831e-3aac8629a3e2 · outbound

This paper cites A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Reference 18

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.341612Z digest=sha256:bd636144eb40ccef380c66194f1250a283001561c59b917b35eca98a67f167e9

Observation a2ee915c-35d1-48f6-820c-370111a42973 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 19

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raw_fallback, observed 2026-08-10T19:29:51.926397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.345621Z digest=sha256:ed750c09959022bfe4ef4a0caa78d3d04c8ae3b4e6767ee481f7aedb5c1fed4d

Observation 4456a1bc-a255-448f-98d5-c4f5bde3ecd3 · outbound

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

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning VeRA: Vector-based Random Matrix Adaptation

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.349294Z digest=sha256:a941e5c04420deaee25b118f076dc7ab5ceb1b891ef232222fc0c259834554b2

Observation 00035fce-40e4-40ee-ac07-477a83ea8e43 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 21

Resolution
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raw_fallback, observed 2026-08-10T19:29:51.914088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.353017Z digest=sha256:ec19a6782b6452f3e2359f59a0d76e44987bfd8896535fdf6993ef304b43910e

Observation 26d5cf78-b40d-4ffe-a6ad-fd05f9de7724 · outbound

This paper cites MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.356717Z digest=sha256:314876784311f4092086e1b52e662d23bf9531513679124c5f13d9f8f51351d2

Observation 694325e6-2fce-428e-9d63-cc84f1749ca1 · outbound

This paper cites Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform

Reference 23

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.360797Z digest=sha256:7e99965da0990d0bd94bdac8a1ff5d2544f052a4f2be58114c04331142445e61

Observation 812bef60-0e72-4f04-86cf-ae4612453b44 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 24

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.364569Z digest=sha256:04354265413609a6b4bc9d05d27354ac8ff6d2bc80f09646fb1758bcc797701c

Observation 009f012f-4e94-43c1-a306-2c3a937133ce · outbound

This paper cites TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition

Reference 25

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.368304Z digest=sha256:5c3bc931a936431e6f6f4aece1f616c8e2bb8dc0086f7d0a6502b68159dfc057

Observation 8a9c5239-6101-435a-928c-f6c70616f5d9 · outbound

This paper cites Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.372140Z digest=sha256:e11f1121623fc9abd42c6d8e30ec7c5d49891351a89c6e76c7459337f0ee7ee6

Observation 3b0c22b4-f2a2-45a1-9b77-3a4c29beeea3 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 27

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no resolver link, observed 2026-08-10T19:29:51.376180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.376180Z digest=sha256:6e2f23d8e726e7ebad1e5ec0830c20efadd06a5f1ab96163437aeea9e1c43007

Observation 3f88a649-9e9f-4572-85b3-4b0363f3dddb · outbound

This paper cites When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications

Reference 28

Resolution
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no resolver link, observed 2026-08-10T19:29:51.379501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.379501Z digest=sha256:12bcff03da2914e960cd20010b3aa4f44fec347acfb8602f98a1ac356c4d4bab

Observation 2b3e2c96-0264-4ca0-ab49-a92d13e3bb5c · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 29

Resolution
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no resolver link, observed 2026-08-10T19:29:51.383190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.383190Z digest=sha256:b44889c72188f29abe5d638191454e73dfbcdea2966b3f30c6ce79447554f970

Observation 650efc14-e2ab-44dc-b0f0-b6a3ac978b46 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 30

Resolution
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no resolver link, observed 2026-08-10T19:29:51.387161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.387161Z digest=sha256:71b1906b334f192e5da1627e7edd28c6c9cd33ccd45f856af87591e485cb56e5

Observation 6489bc81-beb7-42f6-bca3-9aeb184d7ce0 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 31

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no resolver link, observed 2026-08-10T19:29:51.391185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.391185Z digest=sha256:9c98e192b2b6ec1fd3ab7a273cc803b29bafdf9a8394a5fdbf801e60de7dd199

Observation a606392e-8662-412f-9fc0-aabadfad57a9 · outbound

This paper cites Ouyang, J.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Ouyang, J

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:29:51.896570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.394976Z digest=sha256:aab1acb3c4dc9b40923f1bea192a547900fdecd2dcc45136af54210048817936

Observation 64b9bffb-2e44-4a29-9ca4-685fe772b8e2 · outbound

This paper cites Scaling Large Language Model-based Multi-Agent Collaboration.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Scaling Large Language Model-based Multi-Agent Collaboration

Reference 33

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no resolver link, observed 2026-08-10T19:29:51.398532Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:29:51.398532Z digest=sha256:300a1066f8c76e37d3328be8f1a6a071c08c6087aefd76563bb1a5f34872500d

Observation 13558cc7-089d-47a8-8308-ce0d9631d737 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 34

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unresolved
raw_fallback, observed 2026-08-10T19:29:51.885580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.403265Z digest=sha256:8eb5f21a1b30c82b45e468a324b0b94e1c049904b6e78243f47902b17a7a4684

Observation 5f1f4010-6cea-4ce1-b57b-e9c82dcddbf7 · outbound

This paper cites Sakaguchi, R.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Sakaguchi, R

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:29:51.873708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.407546Z digest=sha256:98fc96d0a2bcfbcdb9ff718409da784a4c0459b59bcaee40e3c12c4bd8d09e88

Observation 2e7a488f-9fe1-47b0-8c3c-67d9968dc485 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning SocialIQA: Commonsense Reasoning about Social Interactions

Reference 36

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no resolver link, observed 2026-08-10T19:29:51.411148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a0b2f5d7-b668-4c96-aa8e-0972247d5d36 · outbound

This paper cites Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast

Reference 37

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source=pdf_text observed=2026-08-10T19:29:51.415215Z digest=sha256:a93581a02f040336a4dd6437558de8c30e7d4131ca0f7bc76fcd0fb3c3636658

Observation a19bd146-fe6d-482f-b695-9ecf2fb44e3f · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 38

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raw_fallback, observed 2026-08-10T19:29:51.863089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation acdf167b-8616-4b55-bb6b-f38850803066 · outbound

This paper cites HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning

Reference 39

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no resolver link, observed 2026-08-10T19:29:51.422645Z

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source=pdf_text observed=2026-08-10T19:29:51.422645Z digest=sha256:f1cb3191c7e1ba6279366919a2cfdbecd98d4150b365367f43e08c5aefaa5db1

Observation fbc2169d-579a-4ff0-9ddd-642a1b2995ec · outbound

This paper cites TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models

Reference 40

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source=pdf_text observed=2026-08-10T19:29:51.426371Z digest=sha256:7808848bf4768a0b781317659f89e9d77daff30c6692bc8b14494376b19e74b2

Observation 755b6541-9b39-40c6-9c0f-e0e3c197c130 · outbound

This paper cites an unresolved cited work.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Unresolved cited work

Reference 41

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no resolver link, observed 2026-08-10T19:29:51.430133Z

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source=pdf_text observed=2026-08-10T19:29:51.430133Z digest=sha256:a71c642637092c90726630295a61fc0d87d7e17b210b8ac41e5b46ff5017d6c6

Observation c83aa6f7-3181-4020-89b3-a61d57ea621d · outbound

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

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 42

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no resolver link, observed 2026-08-10T19:29:51.433696Z

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source=pdf_text observed=2026-08-10T19:29:51.433696Z digest=sha256:36b41428e26c831faefb03e71d04b075fc5358c9d859ed6b2aa420651128eb1d

Observation 0e7c659d-f987-4427-ade7-389e3272ee64 · outbound

This paper cites Zhang, Y.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Zhang, Y

Reference 43

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.437737Z digest=sha256:1355e863d5e6bb3654fbf7c8ccbb9f4303e56b326aa993dc9844e1e7d5268dfa

Observation c7256a8c-720b-4819-b8cc-a4f25709b226 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 44

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no resolver link, observed 2026-08-10T19:29:51.441366Z

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source=pdf_text observed=2026-08-10T19:29:51.441366Z digest=sha256:10650fffd08485e91fd68ccb2466541653987f98a21e31681c2b1c32d33c7ed5

Observation 080264aa-4351-4fe5-9a50-a6ba5f77d38d · outbound

This paper cites Zhang, P.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Zhang, P

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:29:51.839267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 071dbd86-6f8a-4ea1-9117-db18bc130ad5 · outbound

This paper cites IAPT: Instruction-Aware Prompt Tuning for Large Language Models.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning IAPT: Instruction-Aware Prompt Tuning for Large Language Models

Reference 46

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no resolver link, observed 2026-08-10T19:29:51.450322Z

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source=pdf_text observed=2026-08-10T19:29:51.450322Z digest=sha256:ff16a9e8905e24f9feb2be217f723bd3ef123cc321717cb505dbe43a84506e7f

Observation f1e5c290-e3e2-4202-8907-9688b6ea5969 · outbound

This paper cites Zhuang, Y.

OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning Zhuang, Y

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:29:51.826045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:29:51.454608Z digest=sha256:3483800e230adcf9dedc71fbba6a928af892936d49c62266d3db95c7c427fb98

Pith citing papers

Observation 1ac979d5-c127-4bdd-b467-af21efdd8fa6 · inbound

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning cites this paper.

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-21T08:39:53.466548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 3a5151e2-7f3b-4f51-8158-dddcfe7d0233 · inbound

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE cites this paper.

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning

Reference 16

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verified exact
arxiv_id, observed 2026-07-02T01:36:25.764933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 67a9330c-9044-4599-9d17-bf0e9a0946e2 · inbound

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE cites this paper.

AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T07:48:12.576212Z digest=sha256:a2bd32cb0301207ba468a5aea81f6874d1278f7133b335e136219fd887a41087

Observation 3a450172-d54d-44ae-b1bf-fd7bdce8fd12 · inbound

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning cites this paper.

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:49:44.417986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T09:29:03.234211Z digest=sha256:d39eb8fc6def5bcb2ba4e73d0a044c30cbf7130bed41706b655320804c375da3