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Paper Citation Record · LEDGER

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling

As of 21 August 2026, this Paper Citation Record lists 100 of 297 outbound references and 0 inbound Pith citation observations for arXiv:2607.16252.

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

pith.paper-citation-record.v1
2607.16252 v1

Coverage vector

measured 100 of 297 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:51:03.512451Z

measured 100 of 100 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 297 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved99
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9ef8802-a4a4-44af-a78d-f44ac1900646 · outbound

This paper cites 2019 , eprint=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling 2019 , eprint=

Reference 1

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source=arxiv_source observed=2026-08-02T09:51:02.560233Z digest=sha256:c38ef2d03c05303023dfaf499239908ea76e1938bf3f687589403c5e7801bc80

Observation 0afc4433-30f0-416c-8003-5a03276d2ddf · outbound

This paper cites 2026 , url=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling 2026 , url=

Reference 2

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source=arxiv_source observed=2026-08-02T09:51:02.717261Z digest=sha256:7bd7fe27be2321e5f7afbddea4fd8456f2aff3a9134c68af6e051da1f45f0bc7

Observation a5969135-e022-4913-b4d0-e2fc5223de4b · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

Reference 3

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source=arxiv_source observed=2026-08-02T09:51:02.827061Z digest=sha256:cad438ec5c8f6b0871b0fbdf462811b2db9ddff14379da6643215c20c80745ad

Observation 6369c329-c20a-40f0-9f34-432b0307474a · outbound

This paper cites Advances in Neural Information Processing Systems , year=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in Neural Information Processing Systems , year=

Reference 4

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source=arxiv_source observed=2026-08-02T09:51:02.943304Z digest=sha256:ec5377f5ce23f8767e00c0364e16e9bcb3fbaa610c2ccb5714dc88ec8c51b961

Observation 616baf56-9ecd-40ff-ba69-93b5190571b7 · outbound

This paper cites 2026 , eprint=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling 2026 , eprint=

Reference 5

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source=arxiv_source observed=2026-08-02T09:51:03.095751Z digest=sha256:28e02c3f4677cc22f9090e758a2d939377006a42a2206f0850fc88699c81b9d5

Observation 72b03147-a13d-4613-9157-5549f4ea0688 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in Neural Information Processing Systems , volume=

Reference 6

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source=arxiv_source observed=2026-08-02T09:51:03.201991Z digest=sha256:41d1f8e0364c27d7aab54d7510100c925f26ce7495c11b0df116baae06cab8a0

Observation c17149ee-5b6b-4ec3-ab29-7cea4b5909da · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 7

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source=arxiv_source observed=2026-08-02T09:51:03.291898Z digest=sha256:8c71a457e4a448ce9ce42d44f62c68563665387568205fdbfac641e242f1af1d

Observation cb3af98a-dfae-4b11-87e4-355f820cf599 · outbound

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

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Reference 9

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source=arxiv_source observed=2026-08-02T09:51:03.298416Z digest=sha256:aa6dfa9ac9527f0e7552b7cafb88fd7fc8549d21e6cc09da57469b37c37ee654

Observation f0f4eeea-383a-4b3e-a6de-3434661713e6 · outbound

This paper cites 2025 , eprint=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling 2025 , eprint=

Reference 10

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source=arxiv_source observed=2026-08-02T09:51:03.301253Z digest=sha256:b6f3c768eb66c46d82fd331a9c825fd3421e1b4ad9d2fb513d839442cf44cb1c

Observation 546db04d-de14-46ae-aee7-37c5dca399a3 · outbound

This paper cites , author=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling , author=

Reference 11

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source=arxiv_source observed=2026-08-02T09:51:03.303515Z digest=sha256:7ffcfb0da284492331b2f634a07307bac5ac1c6f5ff2bd736a9a68724271b703

Observation 991d1dd0-3791-41b2-9824-4807c38e7d96 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 12

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source=arxiv_source observed=2026-08-02T09:51:03.306056Z digest=sha256:aef2605a7267130669672b1d833b0f1f7ff84a6466b6eae5d28ab8fa6a7bb74b

Observation 10c0d7ab-c482-4555-8c71-830b105e4ac8 · outbound

This paper cites Advances in neural information processing systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in neural information processing systems , volume=

Reference 13

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source=arxiv_source observed=2026-08-02T09:51:03.308589Z digest=sha256:d44be13e21097433877d7877c8c865b326be4b5fe38ee36b2f82b727cf492f1c

Observation a242b305-c188-4546-af96-d5df9e061233 · outbound

This paper cites Advances in neural information processing systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in neural information processing systems , volume=

Reference 14

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source=arxiv_source observed=2026-08-02T09:51:03.311146Z digest=sha256:7388109b8b8689e4854b3cee3af2cde34e5437c6f541c4736e59f70bccdefca8

Observation 7e876d0f-560c-491c-88ef-cf99c50b70c8 · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-02T09:51:03.313475Z digest=sha256:9e405368d2cb50faffffc3c95720c1773cb64bf8c70b913ae00505aeef455bf6

Observation 3faced1d-f47f-402e-aba8-224bac0f280b · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 16

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source=arxiv_source observed=2026-08-02T09:51:03.315612Z digest=sha256:cc9b43889f16b7f2c7952cf142e8bf9eedc9a1624593a5dd61a6153bfabd759f

Observation 29334eb7-2d8f-4db4-8028-7ac2e4138a4e · outbound

This paper cites 2016 , eprint=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling 2016 , eprint=

Reference 17

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source=arxiv_source observed=2026-08-02T09:51:03.318031Z digest=sha256:65da281120812c169bbffbf6c70458e8f4a5cb06269b38a10f43dc77a68056b5

Observation 890be5ae-b3d1-49c7-88a6-1a256b4fd5d6 · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 18

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source=arxiv_source observed=2026-08-02T09:51:03.320552Z digest=sha256:0815ca5acdeaab594833d446fb28668eb2e9a6799f70ccdc3f6722ce2d1f50bb

Observation 8035ba1a-0b62-46c3-b16a-24c0efd96319 · outbound

This paper cites OpenAI blog , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling OpenAI blog , volume=

Reference 19

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source=arxiv_source observed=2026-08-02T09:51:03.322660Z digest=sha256:aefd961506467cc53f40b9f26ebf0caba6ee5e4ce581d73b198dda9386e47e6a

Observation 00783083-1192-4192-bd60-1c859aec2b00 · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 20

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source=arxiv_source observed=2026-08-02T09:51:03.324972Z digest=sha256:72ee6975710d8fe7c78e72e8ab6aa489d9f981b5908d604d5ad768743657423c

Observation 9e58044d-cfeb-49a5-b2c0-79d2cdae3d82 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 21

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source=arxiv_source observed=2026-08-02T09:51:03.327206Z digest=sha256:144c3897fe1837963c2fbd5b961daa4cd06e94f20c57ad8385533f1cf2ebe3f3

Observation ee5a565a-6ed5-4274-8286-a1485c55bbb5 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Reproducible scaling laws for contrastive language-image learning

Reference 22

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source=arxiv_source observed=2026-08-02T09:51:03.329640Z digest=sha256:6eff2fb22470a0a3f8b5bfd9dfc184e084956ae17092f09e3def7c354948a94a

Observation b0cff2ff-0f7d-4625-93ac-b3ae1d232be1 · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 23

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source=arxiv_source observed=2026-08-02T09:51:03.332370Z digest=sha256:ad2b82e5ddfb4ac7606468e81bed09d07b275fce4246c907e82e5100cb2d5fa7

Observation a3ad1415-fe6d-4ee0-93d4-53d5239bc3d6 · outbound

This paper cites Advances in neural information processing systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in neural information processing systems , volume=

Reference 24

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source=arxiv_source observed=2026-08-02T09:51:03.334527Z digest=sha256:0643991c84a1d416039034650b37206ab9f6cde93fba1008d2ca426fb3c688ea

Observation 66da2159-0cde-40d9-a51e-03b56728985a · outbound

This paper cites Advances in neural information processing systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in neural information processing systems , volume=

Reference 25

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source=arxiv_source observed=2026-08-02T09:51:03.336520Z digest=sha256:2291ecac7c0dcc66dd91c45473c06486e344d29880169a094efa677535c3c3ea

Observation 2d2f3485-a7b0-4bc9-bdb3-d0a059dbdee0 · outbound

This paper cites Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models

Reference 26

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source=arxiv_source observed=2026-08-02T09:51:03.338653Z digest=sha256:794817516ddaa316989b44640e32a40a2a3d18deb97501b85d2640e4bad0e930

Observation 72683e7f-bb28-4eb5-b906-475de5930321 · outbound

This paper cites You Only Cache Once: Decoder-Decoder Architectures for Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling You Only Cache Once: Decoder-Decoder Architectures for Language Models

Reference 27

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source=arxiv_source observed=2026-08-02T09:51:03.340861Z digest=sha256:9bc46917ff02708369e903c1151bc136bb786499089fe7101da9a4a32defdc92

Observation 347be885-1d25-4ac8-91f9-9c10bb0815f5 · outbound

This paper cites Reducing Transformer Key-Value Cache Size with Cross-Layer Attention.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Reference 28

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source=arxiv_source observed=2026-08-02T09:51:03.342951Z digest=sha256:a5a7a8a6edb8f75fc916b255f781613496279db85f197e8b8d7e83122a70011e

Observation cb39eb81-8397-4bdb-985c-5218d34daf3d · outbound

This paper cites MiniCache: KV Cache Compression in Depth Dimension for Large Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling MiniCache: KV Cache Compression in Depth Dimension for Large Language Models

Reference 29

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source=arxiv_source observed=2026-08-02T09:51:03.345462Z digest=sha256:b6826bb51f82b91cf408092b70efb3fc6be9ed2a21f83f9fb7f3be0f8708cebc

Observation b9024bee-7640-4399-b436-2a17ca8b25cd · outbound

This paper cites MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding

Reference 30

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source=arxiv_source observed=2026-08-02T09:51:03.347596Z digest=sha256:cefd438cf4e7f74034c9f4fa28e42496f406a13d9166285ba3ddd28d538861ac

Observation 9f3d7527-e1e0-4084-95ca-aaea21a1ea5f · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 31

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source=arxiv_source observed=2026-08-02T09:51:03.350732Z digest=sha256:2f2cf9de4da8dc8ec4e1d755055977036d4be2fa962dcde1669660afb5486aa6

Observation 1d3639a3-b06f-4002-8591-a382a0826c91 · outbound

This paper cites International Conference on Machine Learning , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling International Conference on Machine Learning , pages=

Reference 32

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source=arxiv_source observed=2026-08-02T09:51:03.353112Z digest=sha256:78c1a734208e88e15ddb3268fa915115755613312c9ab2b5b73e178a87c082d2

Observation 3fc216d1-5817-4ab9-855e-5037784662ce · outbound

This paper cites int8 (): 8-bit matrix multiplication for transformers at scale , author=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling int8 (): 8-bit matrix multiplication for transformers at scale , author=

Reference 33

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source=arxiv_source observed=2026-08-02T09:51:03.355111Z digest=sha256:4c21de46ca1b23f7749cc0cb0495bb815e1da93afe454960ae62a14b229972bf

Observation d3ec6394-ca44-4a4f-af4e-4d957e94e0c4 · outbound

This paper cites KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

Reference 34

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source=arxiv_source observed=2026-08-02T09:51:03.357469Z digest=sha256:48ad77c630c9bcbb70bddaf7474d92fecd1bf2ad790cadc6a4942cd4ecc18a03

Observation 27aab2f4-0d4c-4593-9b3e-55914237ada8 · outbound

This paper cites KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 35

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source=arxiv_source observed=2026-08-02T09:51:03.359796Z digest=sha256:2ea9923798696ddbae2bd3e0f14055cd820f797445fc192db47ed52c9a640d18

Observation 4c81d019-a7b1-47be-81d2-0ddf2e63ddfd · outbound

This paper cites International conference on machine learning , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling International conference on machine learning , pages=

Reference 36

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source=arxiv_source observed=2026-08-02T09:51:03.362189Z digest=sha256:3cd6f944efb6756166d3650f7884cecb011c8ffe652fb9c478737e5d2276d43b

Observation 1038e29d-fcaa-4eee-861b-4f8dea734056 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Linformer: Self-Attention with Linear Complexity

Reference 37

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source=arxiv_source observed=2026-08-02T09:51:03.364603Z digest=sha256:0371625c4fa85db4b62cef21a47af4ac686b84d0db5b87b98a34a84450d49df2

Observation 1d048c8e-0b2b-49c7-ae7c-0b878c64acb7 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling RWKV: Reinventing RNNs for the Transformer Era

Reference 38

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source=arxiv_source observed=2026-08-02T09:51:03.367004Z digest=sha256:fe33fa0030ef2c2638022c3a8f1fac5a7a320dd41a38cbc7dd1419b931b52b44

Observation ca2f9ecb-7c29-4278-800a-118742e0c4e9 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 39

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source=arxiv_source observed=2026-08-02T09:51:03.369467Z digest=sha256:5ce9351bbcd1a9c28c92bc91e374907225c2bf2355044bfc0d3c53a30c7d42e2

Observation c5acb4ad-c3fa-4129-b437-9835a7216160 · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 40

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source=arxiv_source observed=2026-08-02T09:51:03.371636Z digest=sha256:56a6c8321b4342b5bb2adfb04ddcebe3bd86d593ecbc44e7b02141eba0032c1a

Observation 421cfbb6-0894-4c99-9f39-701ab756934d · outbound

This paper cites LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference

Reference 41

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source=arxiv_source observed=2026-08-02T09:51:03.373817Z digest=sha256:00288d6110bff7ebf04d896108495c1a9ebc1664c57d08695111f8b9e31bad0d

Observation f59c0168-eb65-4900-b7db-bf522100379c · outbound

This paper cites A2SF: Accumulative Attention Scoring with Forgetting Factor for Token Pruning in Transformer Decoder.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling A2SF: Accumulative Attention Scoring with Forgetting Factor for Token Pruning in Transformer Decoder

Reference 42

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source=arxiv_source observed=2026-08-02T09:51:03.376221Z digest=sha256:2dd41d7375bb4b072b4ec9355edaf55b328045db98b6c204b50639339a403019

Observation 11dbdad8-5b6d-4fe9-9c3b-880bcd5fb01c · outbound

This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling SnapKV: LLM Knows What You are Looking for Before Generation

Reference 43

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source=arxiv_source observed=2026-08-02T09:51:03.378531Z digest=sha256:7d83661d7a4d5d0fdaf2ef968d756e6c8be85fc229149b0480d7a1b903b55e93

Observation 1f056050-87c1-4250-87af-e51b66e0326d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in Neural Information Processing Systems , volume=

Reference 44

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source=arxiv_source observed=2026-08-02T09:51:03.381229Z digest=sha256:9e99ab060d20dbe2c946d047dba7360bcdc0626ae09313b3fb5e54b9b4bd2f15

Observation ef730e95-8480-4c56-acde-e42cbae0321e · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Fast Transformer Decoding: One Write-Head is All You Need

Reference 45

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source=arxiv_source observed=2026-08-02T09:51:03.383201Z digest=sha256:bfdcc436574c4f1f63426e2d5bdbaa42a387024573690383d232f2caf96e4ba5

Observation 5d02d924-b0ff-4237-97e7-b2ddffeab72f · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 46

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source=arxiv_source observed=2026-08-02T09:51:03.385980Z digest=sha256:5ecee4273f4507a87aa04a084f3e70be3b5c3deb6e8463f9f4bb548ac4645808

Observation 906ecff6-6869-4d8a-9b1c-114a7c9ddbb5 · outbound

This paper cites Effectively Compress KV Heads for LLM.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Effectively Compress KV Heads for LLM

Reference 47

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source=arxiv_source observed=2026-08-02T09:51:03.388419Z digest=sha256:cd120626a9ad6ffcfb68d5039c5101777f292cc4f52e50e49e69fa20c2413600

Observation fefb9260-6692-4b72-8306-f7ccf2912d03 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 48

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source=arxiv_source observed=2026-08-02T09:51:03.390763Z digest=sha256:9efb3d26602ce454a1929aec4364d801c37235d77a8d9fba48a001e3d41d334e

Observation c6359858-0342-4451-b509-8d88feff6ffb · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 49

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source=arxiv_source observed=2026-08-02T09:51:03.393031Z digest=sha256:fc063ad52555f12aabad3b2e02e235255902cfd7134c897900959d6fb854942c

Observation ffb617f8-12f7-4883-a1d5-cd965a4429b7 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling A Simple and Effective Pruning Approach for Large Language Models

Reference 50

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source=arxiv_source observed=2026-08-02T09:51:03.395317Z digest=sha256:ab2e0f7c6e63838ca9e6d5320991b0890511df80c9c825d896d1ee5bfb00f62e

Observation 410772a9-49a7-4a89-9f02-ef95340a6da3 · outbound

This paper cites arXiv preprint arXiv:2106.10199 , year=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling arXiv preprint arXiv:2106.10199 , year=

Reference 51

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source=arxiv_source observed=2026-08-02T09:51:03.397876Z digest=sha256:2111c7fb13299f4998f377be8ae2a2007d16fefcd716594b9e8976c2fb5ab9a1

Observation 48728fb9-b584-4d8f-9f85-12ee20ccf587 · outbound

This paper cites Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models

Reference 52

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source=arxiv_source observed=2026-08-02T09:51:03.399956Z digest=sha256:0ecdfaf8e7ae961d925f10ad423da006a8e2dc2ee4cb3fd5730ecef7014ca810

Observation 5e2bfdf3-9b8f-47a9-a323-e5c9f6eb0b44 · outbound

This paper cites Masking as an Efficient Alternative to Finetuning for Pretrained Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

Reference 53

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source=arxiv_source observed=2026-08-02T09:51:03.402370Z digest=sha256:cfd076c8b407b55c4b9d1f425a72445a3884589dc824b90f220a027e433b369c

Observation 68004874-17e8-4969-8273-d22c5d5492f3 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in Neural Information Processing Systems , volume=

Reference 54

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source=arxiv_source observed=2026-08-02T09:51:03.404627Z digest=sha256:0fa19b467e2c87a8928714f08bb323cfeb917bd605a12b791d089e26f0378439

Observation feec5860-dd8a-47b8-a602-5060ff3262d1 · outbound

This paper cites Composable Sparse Fine-Tuning for Cross-Lingual Transfer.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Composable Sparse Fine-Tuning for Cross-Lingual Transfer

Reference 55

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source=arxiv_source observed=2026-08-02T09:51:03.406704Z digest=sha256:2984a2b334bbecf231b85a1be7cd15ab74cb6dd351fb780d6b698b6baecffbf7

Observation 2177b909-a4b4-4057-8ff7-9cd25399783e · outbound

This paper cites Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Reference 56

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source=arxiv_source observed=2026-08-02T09:51:03.409186Z digest=sha256:2452615f98649e8affe0f6c7db2b13e9e497dde14b275ae75b4f580d9832d31e

Observation 67e534fd-5bfc-4317-b939-9a68970cc497 · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Parameter-Efficient Transfer Learning with Diff Pruning

Reference 57

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source=arxiv_source observed=2026-08-02T09:51:03.411612Z digest=sha256:8b0f572802f8a28452b2e5c912500f9b8ff1700de29df4fba6415fee52eb5f6c

Observation 20ed5d6b-4696-4bdc-8a75-73a0595c9006 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 58

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source=arxiv_source observed=2026-08-02T09:51:03.414064Z digest=sha256:9dd6886016ea4abaabbbae12f13226c92dd098ba154aed669d3f3345b6f9c8aa

Observation 9c098878-74d2-4fcc-9c87-0cb5d478f839 · outbound

This paper cites WARP: Word-level Adversarial ReProgramming.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling WARP: Word-level Adversarial ReProgramming

Reference 59

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source=arxiv_source observed=2026-08-02T09:51:03.416138Z digest=sha256:5fdfca19f403b616a28652c4911741199e3e761a585a9fc378fbcbde6af55065

Observation 5d90199a-bfd5-4e0a-96be-5c9cfee76f11 · outbound

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

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 60

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source=arxiv_source observed=2026-08-02T09:51:03.418653Z digest=sha256:db843f7cbbbb900e6fb4d3c6f6f6753614f822d726a0599dc014b439287a503c

Observation 315fd48a-15a8-4f6a-896b-d06d6415075c · outbound

This paper cites AI Open , year=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling AI Open , year=

Reference 61

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source=arxiv_source observed=2026-08-02T09:51:03.421240Z digest=sha256:220ffa0e9794473babd207df021a6a23eddeb0284902ed6b87209905ec9c2b14

Observation 93b5082e-d193-4aa9-90f9-b404adc04ab0 · outbound

This paper cites SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer

Reference 62

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source=arxiv_source observed=2026-08-02T09:51:03.423549Z digest=sha256:543e1a6a2c355e6bf7d88363411b3eaccf9e33fc3f4754153d11bdcb7e35645f

Observation f4b12e27-e998-41c4-82d2-bbfd5a2f11f5 · outbound

This paper cites ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

Reference 63

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source=arxiv_source observed=2026-08-02T09:51:03.426216Z digest=sha256:4b6b07b6ff7c57e57f7f67fb99a94ae5d77dee917ef9023370e8c04b42edac01

Observation 2ffbfd06-bb48-48ce-8b67-07008e7f015a · outbound

This paper cites Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

Reference 64

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source=arxiv_source observed=2026-08-02T09:51:03.428540Z digest=sha256:febba52fc27800c0919773201ad326c6c229fc1e7eeb0085d01f20a8abe878ac

Observation c770d1b2-70e8-40d4-90cd-685091a947ea · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in Neural Information Processing Systems , volume=

Reference 65

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source=arxiv_source observed=2026-08-02T09:51:03.431485Z digest=sha256:8c9b594c256d41540f8ffb2f3895c317b3df06a6b1871c8107daaaa0347707c6

Observation b8e5371a-9ce3-4f82-ae38-1b7a14c5e7b6 · outbound

This paper cites Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

Reference 66

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source=arxiv_source observed=2026-08-02T09:51:03.433601Z digest=sha256:e7ae6cf7e3e0e359e0392672bd5c8740f827a4cc82df60d0f549811a0aa8fe35

Observation e45ec2d2-c888-485d-bc78-1829020a2c26 · outbound

This paper cites Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation

Reference 67

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source=arxiv_source observed=2026-08-02T09:51:03.435895Z digest=sha256:9a926a3e1afd5e8fb05da5342d206796cf08dc515fcb68c1c2949f6d898a0a73

Observation 2d986e66-4542-45a3-83f7-742ace5a3f3d · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 68

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source=arxiv_source observed=2026-08-02T09:51:03.438377Z digest=sha256:eb743221a032b4fdd629d79f518fc6c5e80b542dd89bf0d96ecb8c375a7c5669

Observation bc9c96e0-93d4-4930-90dc-1c127fbf8c83 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 69

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source=arxiv_source observed=2026-08-02T09:51:03.440728Z digest=sha256:f71532b2df9c5be1b5bdb3d51826d8204c6aec0b716824d2e441c88005b05adc

Observation 37085926-47c5-4c40-a0bb-e73c4243ccfa · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 70

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source=arxiv_source observed=2026-08-02T09:51:03.442781Z digest=sha256:56706881cd0509d0450d69e033bd022bd65c8a8c3541ef805f6d514a4b26724e

Observation 393a159e-4874-4446-ac73-dcff704a1637 · outbound

This paper cites Findings of ACL 2022 , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Findings of ACL 2022 , pages=

Reference 71

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source=arxiv_source observed=2026-08-02T09:51:03.445058Z digest=sha256:e50dd85d3d81957aa6c9a7bbd82ab68b96d071a8d24db67693a590f74afe2f83

Observation 206ecc20-c551-482a-88d3-e2b995244b00 · outbound

This paper cites InRank: Incremental Low-Rank Learning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling InRank: Incremental Low-Rank Learning

Reference 72

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source=arxiv_source observed=2026-08-02T09:51:03.447212Z digest=sha256:faeae10ecffdd670af856e974e8ce5028f0e6825b3ef120bab7652fbb1dfc84b

Observation df99ceb9-121c-471b-b298-6a8b90dd54be · outbound

This paper cites NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models , year=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models , year=

Reference 73

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source=arxiv_source observed=2026-08-02T09:51:03.449631Z digest=sha256:f277cecbecc63a32ef65618736794f233aa69bea000ce860b77645d0c527a457

Observation ee24cbef-c9ea-4adc-bd0d-1363318a25dc · outbound

This paper cites InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning

Reference 74

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source=arxiv_source observed=2026-08-02T09:51:03.451799Z digest=sha256:ecdb2c39395360a4fe1ea26174cbbc4d3080b52646e9e3f383c21948331caaf1

Observation e689aed8-3166-4d44-bffe-c2b2249fa9fc · outbound

This paper cites MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning

Reference 75

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:51:03.454276Z digest=sha256:6fdbdd50390675185d612fff47ca7199e7650d0affb9e6d3592800e974d1bf36

Observation 03776e9e-3188-4539-98cc-2ce1b3a58af0 · outbound

This paper cites Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting

Reference 76

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source=arxiv_source observed=2026-08-02T09:51:03.456655Z digest=sha256:0219c903fb8c2b155d1c73b42ddd02a8d01f63504f90d74db54f6044d052fce3

Observation b7e63196-a61d-4886-ae1d-9b7ae8fbfe40 · outbound

This paper cites Proceedings of the national academy of sciences , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of the national academy of sciences , volume=

Reference 77

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no resolver link, observed 2026-08-02T09:51:03.458984Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.458984Z digest=sha256:cb0250556d64ff365d0c99964806990a26778e8a4663fd6985026f3dc61f60b3

Observation 085c1aa1-a536-4dce-bd42-31afa7390194 · outbound

This paper cites Proceedings of the European conference on computer vision , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of the European conference on computer vision , pages=

Reference 78

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source=arxiv_source observed=2026-08-02T09:51:03.461052Z digest=sha256:3cfbe0adacdc749739e613d91ded1897476347fb8ae3de411ac79d240d9eea8d

Observation aea45aab-6477-491e-b739-65c0d75e2251 · outbound

This paper cites Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=

Reference 79

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source=arxiv_source observed=2026-08-02T09:51:03.463414Z digest=sha256:057cb7320129bf289b38d988d2229c6f34cd7600d3bd93f7b92ce095cfa58b0e

Observation f959ba4d-d80a-4cca-bafc-941993a7e9fe · outbound

This paper cites Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=

Reference 80

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no resolver link, observed 2026-08-02T09:51:03.465541Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.465541Z digest=sha256:5cf7e0afb5e7fc41d815138378dd5e53b43f86ac9ec5162eb2fc8ceed7bb8451

Observation 755d33ea-fba2-44c0-9243-eca0878a85ed · outbound

This paper cites Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=

Reference 81

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no resolver link, observed 2026-08-02T09:51:03.467915Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.467915Z digest=sha256:57944af5374dbee92cfacc9d6ed4cc8ae77903f3fb7019ceb4482c45e86c0a4c

Observation c258db95-4464-493d-ac06-934a175ada4f · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Advances in Neural Information Processing Systems , volume=

Reference 82

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no resolver link, observed 2026-08-02T09:51:03.470015Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.470015Z digest=sha256:1abc1f12d50e5c8f415f804e7b11039fe5612c2ecb09b17c8e4d62da91077f8e

Observation 9bb83b94-7095-4ff2-b978-50c4c910d3f1 · outbound

This paper cites Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=

Reference 83

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no resolver link, observed 2026-08-02T09:51:03.472282Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.472282Z digest=sha256:90d596c3544b3bae426f3d5cff22ed920971fc06734580ea2d79255297aa00da

Observation 058b9474-aa3c-4fe7-ad01-60d23c238744 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2022 , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Findings of the Association for Computational Linguistics: ACL 2022 , pages=

Reference 84

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no resolver link, observed 2026-08-02T09:51:03.474552Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.474552Z digest=sha256:7a0da259ba418124e0be1f456f30e41b00c920333be999317fed449dcafff719

Observation f6c57eaf-90c2-466a-bb66-0cceab6af3c4 · outbound

This paper cites Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=

Reference 85

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no resolver link, observed 2026-08-02T09:51:03.476602Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.476602Z digest=sha256:0c9acd632fa6f32f5d9b2babf74de20df98c559383aeb3e3046d2daf7b55d3d1

Observation 793f8bcb-5721-4d62-bbb0-543914ad0d5e · outbound

This paper cites CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks

Reference 86

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source=arxiv_source observed=2026-08-02T09:51:03.478546Z digest=sha256:b52127fe765ec74bad7c568aefe4ab1d97670baa25b103c14efe4ab46f9046ed

Observation d78a4d29-87aa-49c5-bf7b-ccd9027716ff · outbound

This paper cites International Conference on Learning Representations , year=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling International Conference on Learning Representations , year=

Reference 87

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no resolver link, observed 2026-08-02T09:51:03.480924Z

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source=arxiv_source observed=2026-08-02T09:51:03.480924Z digest=sha256:e2d1911a8940a473f2cd1075eace363203c98aa31f3d86dcaa777d388550cc7b

Observation d5155497-f5ec-4fcc-b983-f82587205943 · outbound

This paper cites International Conference on Learning Representations , year=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling International Conference on Learning Representations , year=

Reference 88

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no resolver link, observed 2026-08-02T09:51:03.482945Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T09:51:03.482945Z digest=sha256:3d7d116c95e8b7e8b0ed4b63646c4cde60636fdf776e0b99b85e976f0e549489

Observation 28c03cfa-42b3-4889-a3dd-92615a8c5cf2 · outbound

This paper cites Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Proceedings of Conference on Empirical Methods in Natural Language Processing , pages=

Reference 89

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no resolver link, observed 2026-08-02T09:51:03.484945Z

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source=arxiv_source observed=2026-08-02T09:51:03.484945Z digest=sha256:d291426f9d6539e7b41e7176eddf5ffbcf40ad96b80793b01b4c971419ea4683

Observation 3b8f4f8d-df66-4b15-b85c-7489c7ae80cc · outbound

This paper cites Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

Reference 90

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no resolver link, observed 2026-08-02T09:51:03.487344Z

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source=arxiv_source observed=2026-08-02T09:51:03.487344Z digest=sha256:e7cba32e6ee8501a6733daae84b3f9295d6470c385c86499f543ceb755df409c

Observation 73b8ebf3-d160-4fa8-860d-b5341ee0a915 · outbound

This paper cites Continual Learning of Large Language Models: A Comprehensive Survey.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Continual Learning of Large Language Models: A Comprehensive Survey

Reference 91

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no resolver link, observed 2026-08-02T09:51:03.489576Z

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source=arxiv_source observed=2026-08-02T09:51:03.489576Z digest=sha256:e2b3f4bb730fb5eeb5355c064819bf7e206b7391890718c8f051a994db64845d

Observation 20eebc25-91b2-4c29-8b70-64d4bb3a7a5b · outbound

This paper cites 2024 , url=.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling 2024 , url=

Reference 92

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no resolver link, observed 2026-08-02T09:51:03.491908Z

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source=arxiv_source observed=2026-08-02T09:51:03.491908Z digest=sha256:400bfb2ce8fd924d8d7ce012a2902a16fcef8d3e7aca00a060b891f3ca3d6f1a

Observation 1a2e43c0-cba5-4a72-964c-9080c8df58b6 · outbound

This paper cites CoRR , volume =.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling CoRR , volume =

Reference 93

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no resolver link, observed 2026-08-02T09:51:03.493998Z

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source=arxiv_source observed=2026-08-02T09:51:03.493998Z digest=sha256:e0080328f0ba8f352f648fe43fdd3dece5679473dd9d6114958e400ae674f07e

Observation d6137203-0ceb-4a26-ad78-3494a762c8e2 · outbound

This paper cites DeepSeek-V3 Technical Report.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling DeepSeek-V3 Technical Report

Reference 94

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source=arxiv_source observed=2026-08-02T09:51:03.496049Z digest=sha256:7846f2e8923aac6e7efc2ab359dc9ae2b702317fd851106dc38a43a6cf189a81

Observation 686a8777-afe9-49ca-a3b0-f9b773ae6508 · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 95

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parse uncertain
no resolver link, observed 2026-08-02T09:51:03.498588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:51:03.498588Z digest=sha256:33714653da02fedf6e66bb36dfef080b73d2371837cd3943c8acfce2ba87373f

Observation fbc89610-fecd-4710-a771-355ac107dad4 · outbound

This paper cites Introducing.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Introducing

Reference 96

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source=arxiv_source observed=2026-08-02T09:51:03.500849Z digest=sha256:c118f2718df8506ee1fc0b7495abd7e196e4ab53096bb6b4f6a750aec16534e3

Observation 4c587fbd-cca9-4a41-9e4a-010fccf079a4 · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 97

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source=arxiv_source observed=2026-08-02T09:51:03.503235Z digest=sha256:deeec69e5e80940cce2d35ac3a46c6f751114edc08752bb9243b59757a59cb4c

Observation c5492e16-77ca-4a10-bb27-fae137b7160a · outbound

This paper cites an unresolved cited work.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Unresolved cited work

Reference 98

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source=arxiv_source observed=2026-08-02T09:51:03.505430Z digest=sha256:538c596681417c02fd3917a4b5c8bf104190090b216bc4cedbfe28cf5f96a513

Observation d23a61ea-9159-4a90-9a98-a93133005b71 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 99

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source=arxiv_source observed=2026-08-02T09:51:03.507908Z digest=sha256:1d507fffe98bdb9c8659c01407a78a13ef28a141a343851b9e06a8ffd399dd7e

Observation 1ec9fc38-7398-4774-a126-e105f352bd40 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling LLaMA: Open and Efficient Foundation Language Models

Reference 100

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:51:03.510157Z digest=sha256:b76ede595c1b7c1e063e3362bb572e1da78fa07181a25cf29bddfa8d18ef0063

Observation abd61e8e-9dd8-4bf8-9d1c-ff612c38facc · outbound

This paper cites Qwen Technical Report.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Qwen Technical Report

Reference 101

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no resolver link, observed 2026-08-02T09:51:03.512451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:51:03.512451Z digest=sha256:e59e0d7297f70c6b8d600194abb06f120b51689c5b8e06fe2dcf2c49a7982a54

Pith citing papers

No inbound Pith citation observations are available.