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Paper Citation Record · LEDGER

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.02818.

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

pith.paper-citation-record.v1
2506.02818 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:20:44.577644Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

36 of 36 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved33
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ee253a3-543f-4a22-8782-cc6ae3a9b247 · outbound

This paper cites TQCompressor: improving tensor decomposition methods in neural networks via permutations.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations TQCompressor: improving tensor decomposition methods in neural networks via permutations

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:20:45.159807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.133732Z digest=sha256:5325c8e945399d3b9ba9cd4227ad36f9e92fdc6a50baf5d0d83fd908140e5226

Observation ccb64201-f760-4376-80d0-fa9cfbd6d4f4 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations 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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.195506Z digest=sha256:f88c1c3e567c906eed37fd80abe6d096b44e292d37453b8a1ee92d6af3a46a32

Observation 66540966-449e-41b2-ae36-e7872854f0b8 · outbound

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

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 3

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no resolver link, observed 2026-08-07T11:20:41.273580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:41.273580Z digest=sha256:9f97e301ddb2bf1fc5ae4f7d053d6b1ef2a036bc2bf22feb00704a4389d9773e

Observation 8020dfa0-c08b-4282-8689-b300b22075e0 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 4

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no resolver link, observed 2026-08-07T11:20:41.394250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:41.394250Z digest=sha256:b497c153f83c55e4d41b5c9190fc49ecee31d4c3e6dc6b958399064d4162fdc8

Observation 6cec8bb6-3424-4d33-a123-043608bedcd7 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.469009Z digest=sha256:7da2e10ef3f6c51c2d92ecde231e1a1c7bb6957f61dfd54df009c7df562ce8d6

Observation fb78f19f-beb8-4480-9126-ea2a51a0d0f4 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.586588Z digest=sha256:3acf123a18eff55e1ac2ba548f3da9e3047433ab79d3ad43015a1ba13e5e5973

Observation 9ce221e0-41a7-4f86-80a0-1abeb1fc27ea · outbound

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

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

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no resolver link, observed 2026-08-07T11:20:41.684653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:41.684653Z digest=sha256:f67696a5b2470274f764698752d57d432bd7f76b59a85131da97e48b7f863a9e

Observation 7a50be1b-8303-41aa-9531-05c7b37acb1b · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.766478Z digest=sha256:cae5ee16a5d4aa028291c49400035ba01aca9baf01469dcfa980f2127dd4e189

Observation fbeed5a8-002c-4182-94f6-e16f9b29532a · outbound

This paper cites Kronecker Decomposition for GPT Compression.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Kronecker Decomposition for GPT Compression

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:20:45.018226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.845681Z digest=sha256:7bddc88dde3939fbc8c2210349c71dd7fe056d0fe82b5e5a01d8299eb73f6d70

Observation 6316f252-2685-43ab-a7cf-5bdbaae6b7c5 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 10

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no resolver link, observed 2026-08-07T11:20:41.923424Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:41.923424Z digest=sha256:3e055bfe5908f72a6a2681bf38b75242932e9885cc8b56336ef4979026d2a677

Observation ba189480-b5e1-4656-8466-ab43db630d1c · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.001101Z digest=sha256:3a7d6da6e7e3835633eec107ad458656647c3c123dc7e678aba7d58d29332e68

Observation 0b069764-20ad-4ddd-86c7-6fbef50efde3 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 12

Resolution
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raw_fallback, observed 2026-08-07T11:20:46.726873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.079268Z digest=sha256:de4c96d48418e5d492945949c284c0ebfa4d9ffb4e3694edf45daff7bdfe648e

Observation b1f12065-a921-4d24-9cdf-db1722e7a61a · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 13

Resolution
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raw_fallback, observed 2026-08-07T11:20:46.562931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.156559Z digest=sha256:21a4ebf19d021ec72daf3244a7d6b14967921f08103be35daba476bde4fef70d

Observation 076d47b7-d47c-445a-90ed-62c4e31850a2 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.235711Z digest=sha256:00c745034a33754d9e8544c89e6f51718017744d29b9f0b6a4f6d5190b9b0d10

Observation a6441d57-d696-4e5a-8b34-34510d9a74b6 · outbound

This paper cites Language model compression with weighted low-rank factorization.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Language model compression with weighted low-rank factorization

Reference 15

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no resolver link, observed 2026-08-07T11:20:42.319067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:42.319067Z digest=sha256:4f1e05b93540e48b18fcd8b848e2e056ef543de2c06377a2ba0f3b20f1304486

Observation c9839aa4-34e6-4438-b4b6-6061e228e37d · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 16

Resolution
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raw_fallback, observed 2026-08-07T11:20:46.198118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.394773Z digest=sha256:8b4c20cd9370fada702f5735adb2b9a93c1e1f9d4dc125aedcf14536781d3c54

Observation 7f615d19-26a8-4a9f-ab1c-a2ce128186f8 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.470620Z digest=sha256:b4adef096935ed7b28d1dd3fb39f91319d4064429b303e6bee0527fa742515b7

Observation 18f3713b-9235-4c80-ab25-5aaf710dec53 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 18

Resolution
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raw_fallback, observed 2026-08-07T11:20:45.857049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.618513Z digest=sha256:47d331622f3d7bf18f948048b1d35c303406c80c58ac82019ba0baa92a7e90cf

Observation 976b242a-f50e-4901-ae49-be2e6c277da8 · outbound

This paper cites MoDeGPT: Modular Decomposition for Large Language Model Compression.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations MoDeGPT: Modular Decomposition for Large Language Model Compression

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T11:20:42.726644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:42.726644Z digest=sha256:970cce67728dd88bb31967714d3f7be0ac699d509c03b07483d338d8cbcc3f89

Observation db47e515-8e39-483c-8ee3-4ca768c4e047 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:20:45.666072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.840465Z digest=sha256:edba2f92958a6b575b5caed901d24380f4ec8e2575eacd12013cc4e45b23202a

Observation 81cb9614-a1ab-469a-aaa9-cd4be46f1bdc · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:20:45.505399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.932377Z digest=sha256:4653d353d4b8a54aee7c039bcb21fc8f3b4a42ef64db7afc78766607b1041681

Observation 3d97fb41-a830-40fe-9602-3951030da87e · outbound

This paper cites Pointer Sentinel Mixture Models.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Pointer Sentinel Mixture Models

Reference 22

Resolution
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no resolver link, observed 2026-08-07T11:20:43.040771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.040771Z digest=sha256:cd70279b6b22868f2f8e286d8d8b32a3672e0692a3be074fd81d0d921e0d5e4d

Observation 7da6e152-8ad4-4cea-ac32-69c7dc5cf007 · outbound

This paper cites Compressing Large Language Models using Low Rank and Low Precision Decomposition.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Compressing Large Language Models using Low Rank and Low Precision Decomposition

Reference 23

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no resolver link, observed 2026-08-07T11:20:43.149426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.149426Z digest=sha256:c01cf02d1b936430dcdfd0d70c5ca1710869f0693cffac13fdcb79f098c09b5b

Observation bdebc6ff-daea-4619-bede-2f4d2a9db0cb · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 24

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no resolver link, observed 2026-08-07T11:20:43.262170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.262170Z digest=sha256:0b5775b8fb82e3eef7c0b9cff198c170d1494f56e4f9a4aaa11a9154ff53fe69

Observation 7c678e98-0a7b-4598-8b4d-b3e31bf7e06f · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 25

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no resolver link, observed 2026-08-07T11:20:43.371280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.371280Z digest=sha256:394c5da147ae2e66678ac2e74a20908df5d63bc274e1ff59874b037992297910

Observation 2ef59a31-7451-4859-a02a-71bf4f207e68 · outbound

This paper cites The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T11:20:43.477837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.477837Z digest=sha256:3ccf88e2d00f065bd26889b0d4f1c5bff2ecdbb94fe51807019de2a4fbfd6c63

Observation b3007589-1249-4ed5-8778-84e7a8302faf · outbound

This paper cites SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T11:20:43.592215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.592215Z digest=sha256:d9cdeac6c6489d7888ce868f7a536967189e0c30b34c5e5d6ff81a39757adffa

Observation dbdd1af4-bc56-4e5c-af7e-9bc8d885d74f · outbound

This paper cites KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:20:44.810605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:43.701447Z digest=sha256:00ae297e608c86d007d0bfc3aef9c9a76bc068fc68772c5b5111dc95ba69817a

Observation a3ebc434-eea3-47d4-9f51-340991749e91 · outbound

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

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations LLaMA: Open and Efficient Foundation Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T11:20:43.812689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.812689Z digest=sha256:e400505b277ad695237aa551fe900499d687ddb4f20ff9fd195946a0cbd60cca

Observation 3c936bf9-8629-4769-9bad-a0bf75ec2f29 · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 30

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unresolved
no resolver link, observed 2026-08-07T11:20:43.959675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:43.959675Z digest=sha256:23d7e607e6d14a1bf04e880d2a772d4b2002d03ddb9110c57060c632399b5464

Observation ed8f8407-19d7-4506-be4f-5406a242d9e8 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T11:20:44.070103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:44.070103Z digest=sha256:86f38eeb6502051bb53b709159eb99c36d90ed7267b0c3f57a54fa717824e030

Observation 83658602-2a10-48c0-9e6c-119af577c460 · outbound

This paper cites TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition

Reference 32

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unresolved
no resolver link, observed 2026-08-07T11:20:44.215235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:44.215235Z digest=sha256:297027cc78f3997b82810b652fc84beec997d221e9b3077d0e0ce37c48348149

Observation bdb45236-326c-49a5-8776-aba08d9c0139 · outbound

This paper cites an unresolved cited work.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:20:45.345375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-07T11:20:44.324050Z digest=sha256:bab2507743dfe6fec61acf9eac6efe2f77dffac4d9cd65d7b2ea0e223acf5f7b

Observation b5838fde-dfa9-4986-a017-430ef527993d · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T11:20:44.403008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:44.403008Z digest=sha256:47bec3c4562c4311ec84f15637b26e0e9a7f06c40ef83219bbbb94a6ded4ef5d

Observation d4e74dad-2dad-425d-b06a-b9e080ddfe6f · outbound

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

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 35

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unresolved
no resolver link, observed 2026-08-07T11:20:44.479099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:44.479099Z digest=sha256:66b443305b69df8f25a46a947c9407c19280b7e75b44df37d195623e39a53e0c

Observation 5e21c1fd-391a-4fde-9a5d-c493c455326f · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations OPT: Open Pre-trained Transformer Language Models

Reference 36

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no resolver link, observed 2026-08-07T11:20:44.577644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:44.577644Z digest=sha256:f237d3b348b7ef239cb4b80d1bc9725e28407135c7ee60891b3d8da903131f78

Pith citing papers

No inbound Pith citation observations are available.