Paper Citation Record · LEDGER

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization

As of 4 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2605.04738.

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

pith.paper-citation-record.v1
2605.04738 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T02:59:00.997742Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T15:49:31.741930Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T15:57:06.547972Z

Reference resolution

23 of 23 outbound references displayed

verified exact20
verified fuzzy1
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f028cbb6-8e60-4fb9-9a6e-ce851316d0af · outbound

This paper cites QuantEase: Optimization-based Quantization for Language Models.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization QuantEase: Optimization-based Quantization for Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.223978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0c0c512f-960d-4bbe-a863-342e884611d5 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:01:18.230604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7f24c574-8f3d-4bcc-9097-2fc2e8332080 · outbound

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

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.206903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8c0cf26a-12a4-4ac0-9b64-f1e31fb77cb2 · outbound

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

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T13:35:36.365299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation df43dc13-4ab7-4605-a063-e0f1984e1f4c · outbound

This paper cites Learned Step Size Quantization.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization Learned Step Size Quantization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.227436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 36aa2bb1-fe19-48ee-bba5-cd6a05181a23 · outbound

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

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.196238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4e08ccc9-d7c8-4722-ab48-7424bc59e2ca · outbound

This paper cites Measuring Massive Multitask Language Understanding.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization Measuring Massive Multitask Language Understanding

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.217237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2740b635-abd5-496a-a944-9bdd0dd5b5ea · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization SqueezeLLM: Dense-and-Sparse Quantization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.210120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c45c827c-e4fd-48c8-8a45-5b2b9216305d · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.193087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d870a1da-1342-44aa-9e19-d5442e01b147 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.213607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation be6bc577-ecba-456a-abb3-a3259a5f984c · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.200028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9a2ef353-10e6-45e6-8ea2-f6021b557efa · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization SpinQuant: LLM quantization with learned rotations

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T15:52:34.845430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0c04aaec-0f85-4dc0-82d7-9706ecfafd5d · outbound

This paper cites Pointer Sentinel Mixture Models.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization Pointer Sentinel Mixture Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.185060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 950ae4ec-e484-48b7-9270-b6887ec6cd96 · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.174160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 48ec1dae-9879-4d1a-89ac-27a22b9df223 · outbound

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

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization LLaMA: Open and Efficient Foundation Language Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.159685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e4086864-4fc8-4b4f-9fe9-fa9cefeb88a2 · outbound

This paper cites Efficient Large Language Models: A Survey.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization Efficient Large Language Models: A Survey

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.166123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3a500291-32c2-4680-9473-776ffd37d6dc · outbound

This paper cites RPTQ: Reorder-based Post-training Quantization for Large Language Models.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization RPTQ: Reorder-based Post-training Quantization for Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.170290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b24b8616-dbc5-43d5-a0c1-d1b4ed63a515 · outbound

This paper cites LLM Inference Unveiled: Survey and Roofline Model Insights.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization LLM Inference Unveiled: Survey and Roofline Model Insights

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.177694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2c33c427-3e9b-42fb-ba8d-d75426045cb6 · outbound

This paper cites WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.181638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9a9da23-22f6-449e-b4dc-7d99bc15a645 · outbound

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

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization OPT: Open Pre-trained Transformer Language Models

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:01:18.162498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7563c64d-8d0e-4f92-a961-cd241c957d29 · outbound

This paper cites A Survey of Large Language Models.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization A Survey of Large Language Models

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T03:01:18.188496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1c51a173-e49f-4bda-816c-f6a0d004c3f9 · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization A Survey on Efficient Inference for Large Language Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.856027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a83a2ec0-0d11-4966-a18e-cc8b72fa97d3 · outbound

This paper cites More Visualization Results of Weight Distributions Here, we provide additional visualization results to further illustrate the effect of the proposed method.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization More Visualization Results of Weight Distributions Here, we provide additional visualization results to further illustrate the effect of the proposed method

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T21:06:53.025605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation dcc2c9bd-19d0-4099-b443-30c6fa6d11f5 · inbound

Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization cites this paper.

Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-02T15:57:06.549373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.