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Paper Citation Record · LEDGER

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations

As of 8 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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.133732Z digest=sha256:b5a1302d4e7fc86a1a58f549c585cdf7c87192f3aa4c2bf408e9da994fdfed31

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.195506Z digest=sha256:c57e5c59f46a189de6b2426519e9867c814ab995083bf2ccebde6f7aafb818bc

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:a8b1beec41cc5780ca45517b1b83dddc4c982f527542734687e8e02a4fbffed8

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=sha256:24eeaf36f2217e85bb2af1a3921c9d25bf89f2fc239da91d9315c5048e07449d

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.469009Z digest=sha256:cde7ae6d5832a7314bdf98a4ab824561d8c6c6df98493d65df67488584e8c1d8

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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raw_fallback, observed 2026-08-07T11:20:47.155830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.586588Z digest=sha256:ac3c7905a37b84c51d78c6683d56eb5191aa58c44f36363044143a849bad42b8

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

Resolution
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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:afcf6ccf920d07caed20919ccd67b7cedf741f4c4ac8d7ebe636338695c2edc3

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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raw_fallback, observed 2026-08-07T11:20:47.033122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.766478Z digest=sha256:4754280cb18385e4a0a302c83996dc6f21680f7fa389fb1d50431c09c063d7bf

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:41.845681Z digest=sha256:7283695f95cdf785ee19665a4efc94fe0ca1df95e3b8ee4187d0a666787a4878

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:20:41.923424Z digest=sha256:a5ceec6ae3c1be09ce02578e78daf5fb2e4cab8b4dd8b9b9c108b6302e20a1c6

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.001101Z digest=sha256:b4ecffca536ac598cbc3fe3534dc83993732756997ac4d7f18ea69eb55469196

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.079268Z digest=sha256:755abc71a963eb8f551f5611516af117d032c55d49203a7d340356d67b4290de

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.156559Z digest=sha256:d274fd36d57bcffa9af5e402fe91b2213da00183faaddac7d454c0bb23a3401a

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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raw_fallback, observed 2026-08-07T11:20:46.388364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.235711Z digest=sha256:8bef36fb244421cae67a6ee45d7d82ba693a749f870d2c908d195c5d441c2ae1

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:a9bdfe686a9a3594e306f9a33ea0265acdd7e2f58b5b14b8b99a51f2b3c07f97

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.394773Z digest=sha256:4baf49647c8f1cb4f87f70bb5274401974e4de162edfc4f6f2d92333062d5a36

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.470620Z digest=sha256:5a8d13466e7233b2b3a218039754adc53cb3f3de1a8924d0407d825814d5f44a

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.618513Z digest=sha256:fd69234294007110fed7ea6a1a2c1f197bad4fafe0f484986ec20480db1ee136

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=sha256:1a6c43d8005ac99723dff06227f0a0411d36d3e3eebc976ba0fe55705b4929c3

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.840465Z digest=sha256:b4e282a35498202730694cf7899ef23052a90a36952d8d0c53f83f901474937b

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
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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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:42.932377Z digest=sha256:0380e8b0b54830ea6d3ec305b70f707e664ecae9c86944cdc0963693ddd25290

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:d6d01f9d26be179a30e4e2cac8605af525342963afdf4ab6be1fe5f5220abc97

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

Resolution
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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=sha256:64a63101ae76e74b73e6de6b59858931727621d1843151907d5f72aa0d15376f

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=sha256:1f9234d4a5c98f47562c801d9526c988b6dff884631738b5c26a44ebe4d7b578

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

Resolution
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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:ed13d22a921bab8271e5e597ee0752296ac60ec8e5b3eb19aeaee3d0176e5d00

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=sha256:85d85209d86ffa58c3835d8293e1bbae99db561234cac29c8b1c86e65e0aea74

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=sha256:6909ee04561ce736413bc83a43f74909988baa4b910c0bfc609fe17a6e5644fe

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:43.701447Z digest=sha256:5d77c585250d1167177c3c0f51e2892d63dec6e44531cc6861787ce1f9448560

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:ecf3a3302b04184a9d083908c1f992c4b69f5c3668224725b1087271614bb2f0

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

Resolution
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=sha256:77645fae56238ebc44f3eac56ceaded573be2914141c8c39e554903bd23b066f

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

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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=sha256:87867475db1cc480617e54b5f0ac5d1d76002515e8d3add974ce994f469f3ef1

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

Resolution
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=sha256:9b56b9ff230ad18807b667e31aacbac67ece7493c0038f8478abdc98b969c315

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T11:20:44.324050Z digest=sha256:bf79ee7e1231e8d745033e03469d3a308637320fae699b7991e77bbe11550ec3

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

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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:b424a592f2217885982a4615c28c2ac24b684c8adc086d3ba7a8b619590f9be0

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=sha256:9fa9073571987c405ce5f1007f87d491d94ef578a3f63479a3b85986352d93aa

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

Resolution
unresolved
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=sha256:7b0cdfca4c39c8eaaa166c60afa02c7eb1bfd85ba6828640e869934e7527e946

Pith citing papers

No inbound Pith citation observations are available.