Paper Citation Record · LEDGER

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring

As of 11 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.13331.

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

pith.paper-citation-record.v1
2501.13331 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:19:57.666122Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

40 of 40 outbound references displayed

verified exact2
verified fuzzy2
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a0da431-a571-4bcd-aa68-ef05db174c31 · outbound

This paper cites write newline.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.341524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9b3aaf8-f63b-4e7e-af60-9c394c870d5f · outbound

This paper cites QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.351802Z

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Observation bbe66914-2085-4210-84c3-93654493d010 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.357499Z

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Observation 73a1e5c7-80b4-41e2-a752-52af15fef528 · outbound

This paper cites Post-training 4-bit quantization of convolution networks for rapid-deployment.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Post-training 4-bit quantization of convolution networks for rapid-deployment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.363052Z

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Observation 3be87f72-e4e0-4525-9676-670a62fce13b · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring QuantEase: Optimization-based Quantization for Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.368395Z

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Observation 217bbb1e-7fb9-4b11-b07f-21b6a6780d93 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.373615Z

Source-reported events for the cited work

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Observation 1c748489-ba7c-4797-a677-e32498911cd4 · outbound

This paper cites Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.378916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c7c046d-0833-4a90-9719-dba81a639f63 · outbound

This paper cites VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.384515Z

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Observation 85482f43-8a08-4b14-aa65-47de8cd4a9ec · outbound

This paper cites B., Cavalcanti, G.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring B., Cavalcanti, G

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.389378Z

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Observation 6f56452b-9b8b-41c8-bfdc-83fd73947b48 · outbound

This paper cites Gpt3.int8(): 8-bit matrix multiplication for transformers at scale.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Gpt3.int8(): 8-bit matrix multiplication for transformers at scale

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:19:59.634976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbe635a7-a84f-48e8-84be-6b34799c41b7 · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.400172Z

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Observation 37c1cd01-7281-4402-a958-9d9a882bdb21 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.406827Z

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Observation 3941d96d-5140-4ff2-9006-1c7e17db5b34 · outbound

This paper cites A framework for few-shot language model evaluation, 12 2023.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring A framework for few-shot language model evaluation, 12 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.412279Z

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Observation addc7dc0-a3c9-4b37-959d-48b721fe14e5 · outbound

This paper cites The Llama 3 Herd of Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring The Llama 3 Herd of Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.419383Z

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Observation fc9b218d-df91-4c00-a951-970479a27e28 · outbound

This paper cites Olive: Accelerating large language models via hardware-friendly outlier-victim pair quantization.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Olive: Accelerating large language models via hardware-friendly outlier-victim pair quantization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.425059Z

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Observation c31d1aee-0fb5-42e4-8863-ca596ed73df0 · outbound

This paper cites O-2a: Low overhead dnn compression with outlier-aware approximation.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring O-2a: Low overhead dnn compression with outlier-aware approximation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.433226Z

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Unavailable: canonical work link unavailable.

Observation bc330e45-6dd7-4f4d-b307-5b56cc54e84d · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.440237Z

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Observation e4589b22-bab1-4780-bff3-fd0e67da1f41 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring SqueezeLLM: Dense-and-Sparse Quantization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.446387Z

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Observation 444e9ef1-dca1-434f-8968-9a4fc989c94c · outbound

This paper cites Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:19:58.580568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d97e2d60-ed5e-44c0-bff7-bd37a3ea2441 · outbound

This paper cites OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.466052Z

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Observation 91dfbf79-da4b-491f-a9e8-db66a71a3b3d · outbound

This paper cites Norm Tweaking: High-performance Low-bit Quantization of Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.478372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bddd51e-f1fd-41ce-8db9-77f7de62512a · outbound

This paper cites FPTQ: Fine-grained Post-Training Quantization for Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring FPTQ: Fine-grained Post-Training Quantization for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.485389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cd62d78-affc-48ff-9238-15c4f0aa04e0 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.495259Z

Source-reported events for the cited work

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Observation 3dbfebed-0c40-4281-a48d-0ce9816587a7 · outbound

This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.517227Z

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Observation d6de235c-32ef-411a-90e9-4841949c427e · outbound

This paper cites QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.526967Z

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Observation 91f0dd9f-91b1-4b16-9f83-74bc34240102 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.535780Z

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Observation e0b1d81b-c1f2-4989-8b31-7d8de2dc073f · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring SpinQuant: LLM quantization with learned rotations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.542793Z

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Observation 75c6eddf-c62e-4786-96bb-5f8f6271328d · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.553255Z

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Observation ca2b1707-834d-4858-a0b4-6e2ba8cc4725 · outbound

This paper cites Normalization: A Preprocessing Stage.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Normalization: A Preprocessing Stage

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.561765Z

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Unavailable: canonical work link unavailable.

Observation 7ecf95b8-9fdb-40ac-85b9-26af37e26a42 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.569008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ec15a6a-896d-463f-84ea-f11f8285a99e · outbound

This paper cites FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.579518Z

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Observation 15665b4e-b0a9-4c05-a20d-bd42a9650351 · outbound

This paper cites A Note on Approximate Hadamard Matrices.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring A Note on Approximate Hadamard Matrices

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:19:58.049991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc062707-1c09-4d20-adfc-d3b084f31f66 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.596732Z

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Observation f399bc1f-34b9-4f94-ac58-5018e2b690f2 · outbound

This paper cites OutlierTune: Efficient Channel-Wise Quantization for Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring OutlierTune: Efficient Channel-Wise Quantization for Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.604260Z

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Observation 88b2d013-29a9-4bea-b67a-e56b73f4af4a · outbound

This paper cites Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.610432Z

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Observation 432d6a19-8924-4b5c-9d51-7d36ab5c2700 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.616343Z

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Observation 32fa36e5-38e3-421d-982d-b10ac952ade6 · outbound

This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:19:59.602069Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6a3d74eb-d8f7-49c0-910b-a9e9f61f0d36 · outbound

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring RPTQ: Reorder-based Post-training Quantization for Large Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.635110Z

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Unavailable: canonical work link unavailable.

Observation 05e522e5-e560-4887-8ca6-bf09e899d755 · outbound

This paper cites Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.646521Z

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Unavailable: canonical work link unavailable.

Observation 701fc2dd-82e0-4125-8523-63f72ed3fca3 · outbound

This paper cites Atom: Low-bit Quantization for Efficient and Accurate LLM Serving.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.666122Z

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Pith citing papers

No inbound Pith citation observations are available.