Paper Citation Record · LEDGER

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2506.13472.

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

pith.paper-citation-record.v1
2506.13472 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:06:29.141726Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

42 of 42 outbound references displayed

verified exact1
verified fuzzy2
unresolved39
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1aefb22d-e352-4719-8269-9c9721c3a9ce · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.843431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 86d3d280-82af-4df6-bfc7-3e045eace103 · outbound

This paper cites Croci, Marcelo Gennari Do Nascimento, Torsten Hoefler, and James Hensman.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Croci, Marcelo Gennari Do Nascimento, Torsten Hoefler, and James Hensman

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:29.828763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4664bd57-c8f4-4002-8414-8e0bab00d04e · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.936550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da6a59af-0976-477e-a26d-7a811f433228 · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.943205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ab01711-4034-4302-bf23-d58ca9af81fe · outbound

This paper cites QuIP: 2-Bit Quantization of Large Language Models With Guarantees.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.948745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ec8aa7d-0cae-482b-8525-a271eac967ae · outbound

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

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.954480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1be710e-edf7-4229-942f-6a6c7dce7b39 · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.962091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78dfc645-f597-4cdc-86e3-ea815d0d1d5b · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.968255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5612d465-c5a6-4f3d-a725-32862b7ebccb · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.793002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5cc36fce-03fe-4682-a6fc-b507edf3fb66 · outbound

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

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.978603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bed45743-a03c-44ae-991d-3e23e5b2968f · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.985678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87caf8c7-1907-426f-a2b8-11d9402ac3e1 · outbound

This paper cites APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:06:29.213641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49d250bd-d458-4c3d-bb04-03c88971ca6b · outbound

This paper cites Measuring Massive Multitask Language Understanding.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Measuring Massive Multitask Language Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:28.995873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29c62467-4573-445a-af7e-d346e707a5ca · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.778589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b2f9a187-c4ca-458e-ae8e-380759541173 · outbound

This paper cites Mahoney, and Kurt Keutzer.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Mahoney, and Kurt Keutzer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:29.764152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00134eea-9540-4f49-a2d3-036fa3d8b341 · outbound

This paper cites QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.013133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d363606-2a3e-4e8f-b7aa-b3b34880b9b7 · outbound

This paper cites u ttler, Mike Lewis, Wen - tau Yih, Tim Rockt \.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models u ttler, Mike Lewis, Wen - tau Yih, Tim Rockt \

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.017460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 094132f1-a9dc-40a3-8377-c830932d7364 · outbound

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

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.022004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ec1095b-ddec-4343-9166-a40ee26f7a25 · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.737434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 35a334e3-efcd-4a1d-8e07-9ac2fa247b04 · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.720905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d9c60c10-be85-4594-8b36-9a6d9cc082a0 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.036055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8b4158b-e1fa-49dd-8480-406767a9601b · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models SpinQuant: LLM quantization with learned rotations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.040883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41c9165f-28d4-4644-a3cd-127acb7dcaa7 · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.046160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bfb9fa2-957f-44e0-940b-c75a1154ddb8 · outbound

This paper cites Pointer Sentinel Mixture Models.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Pointer Sentinel Mixture Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.051490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c568fa-de08-4104-b046-8c1b728b76de · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.056321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d6486af-d977-4acb-ae5f-0f1adec75a23 · outbound

This paper cites SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.061027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fa02b53-a7dd-418c-9bb6-79c93f873e20 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.066547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f42dfd85-ec02-443f-af45-5d3483bbc15d · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.692648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5697b573-3d89-44d4-b334-dae46b698c98 · outbound

This paper cites Squat: Quant Small Language Models on the Edge.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Squat: Quant Small Language Models on the Edge

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.077223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cb20a2d-e342-4686-8fe0-3374ef11e34f · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.082442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 736268f8-c4dd-41e6-8619-a63550dcfc6c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.087836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b5d301c-373d-43c0-8dfd-21e5b04cc6bd · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.676110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 480b2708-c084-44ac-944c-a4e908dfebbd · outbound

This paper cites ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.097371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa820c6a-a411-4935-8fc6-a89de1b0e1a7 · outbound

This paper cites ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.102698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ba1c022-4f1d-4ad1-95e0-e865b3da50e7 · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:06:29.659189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b77a6369-78ac-49f1-b916-83176836c546 · outbound

This paper cites Qwen2 Technical Report.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Qwen2 Technical Report

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.113411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8be7de8b-4369-49d6-98ba-720147f5f4b1 · outbound

This paper cites ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.118203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7f95455-3a75-4a94-a33b-840c231f2507 · outbound

This paper cites ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.123982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 098b1892-fb25-44be-bced-c5e787093d76 · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.128711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0678551f-be8a-4dc2-9283-0d2758992b57 · outbound

This paper cites an unresolved cited work.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models Unresolved cited work

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.133028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 150034a4-bb39-496e-ad82-c9564e4154dd · outbound

This paper cites online" 'onlinestring :=.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models online" 'onlinestring :=

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.137118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b8ab5ea-84e8-413d-8af2-39f704c86fd9 · outbound

This paper cites write newline.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models write newline

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.141726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.