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Paper Citation Record · LEDGER

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

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2304.09145.

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

pith.paper-citation-record.v1
2304.09145 v3

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measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 32 of 32 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:09:58.003309Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-06-28T19:42:36.070011Z

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Outbound references

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Pith citing papers

Observation e73f5471-146c-465c-8514-1674f4582101 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 257

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arxiv_id, observed 2026-05-19T20:28:39.159884Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 34588567-aa6b-4b47-ba76-f31aeac5d949 · inbound

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

A Survey on Efficient Inference for Large Language Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 169

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arxiv_id, observed 2026-05-15T02:39:33.108405Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0c85add7-2e8a-4b72-b67c-98d6ba5d6a7f · inbound

SpinQuant: LLM quantization with learned rotations cites this paper.

SpinQuant: LLM quantization with learned rotations Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 22

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arxiv_id, observed 2026-05-15T15:52:34.788483Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation bd7249e2-90cd-4f89-b4ac-be228cefbd4c · inbound

DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation cites this paper.

DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 24

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Observation 288e1c28-d624-48ca-97fe-73cdf11e8452 · inbound

Deploying Foundation Model Powered Agent Services: A Survey cites this paper.

Deploying Foundation Model Powered Agent Services: A Survey Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 200

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Observation 3187b729-b6b5-4683-934a-b59df0dea78e · inbound

A Survey on Large Language Model Acceleration based on KV Cache Management cites this paper.

A Survey on Large Language Model Acceleration based on KV Cache Management Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 82

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Observation 1f82671e-6d1e-403d-aee3-7da93505fcfe · inbound

Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs cites this paper.

Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 33

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source=pdf_text observed=2026-08-11T05:45:50.287457Z digest=sha256:ebaa99517b3ec428c483805764e024f66f5dfe774728f7c87cdc6c882ed305cb

Observation 055707ab-0c07-45ed-b1f4-75872dfe7a32 · inbound

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models cites this paper.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 30

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source=pdf_text observed=2026-08-10T22:04:24.319774Z digest=sha256:81b6ada59181e62d9f4a27b519a90e13a3fd8f23bedb6e32ef85b9dad84e29bb

Observation c208a281-ae88-4a78-8607-fa24e89187bb · inbound

Accelerating Large Language Models through Partially Linear Feed-Forward Network cites this paper.

Accelerating Large Language Models through Partially Linear Feed-Forward Network Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 59

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

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring cites this paper.

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

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source=arxiv_source observed=2026-08-10T16:19:57.610432Z digest=sha256:a6b13ba735e629ec540b18a0e7b28711cf28c8b62ecf78f3b7cd0eb9bd966512

Observation fa6a0ec5-f35c-433f-9f9d-556e71043bd8 · inbound

On Accelerating Edge AI: Optimizing Resource-Constrained Environments cites this paper.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 9

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Observation d9467dd6-8133-4c6c-b586-6606bd970425 · inbound

Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs cites this paper.

Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 27

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source=pdf_text observed=2026-08-16T12:09:58.003309Z digest=sha256:79bedabad5b58a5b962e88f6d83305e7054322a9bffa670cd3ee0a1b5a7d4f77

Observation 4a266efd-cef3-40aa-9bef-13ecc57e9ac1 · inbound

FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs cites this paper.

FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 27

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Observation 31570def-bd1d-4203-a5d6-ef675b01d94d · inbound

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference cites this paper.

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 67

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source=arxiv_source observed=2026-08-15T21:55:07.863414Z digest=sha256:e28766a34a483824362a5dd97c68e88284473d4161dca5376956d9b35e04c416

Observation 9ed1d098-b6a5-42f7-814f-85f2d927af3d · inbound

Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis cites this paper.

Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 58

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no resolver link, observed 2026-08-07T15:43:41.625021Z

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source=arxiv_source observed=2026-08-07T15:43:41.625021Z digest=sha256:62d43b983a79edb064e8b71045e5d24fb901b2a666e8c7c69e37113bf3fba858

Observation 633f0dc7-212f-47b7-864f-b5b08e2e0762 · inbound

NQKV: A KV Cache Quantization Scheme Based on Normal Distribution Characteristics cites this paper.

NQKV: A KV Cache Quantization Scheme Based on Normal Distribution Characteristics Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 2023

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Observation b7a28310-e77c-47cf-a0e2-19d3232e669b · inbound

KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning cites this paper.

KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 34

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Observation 4ae87194-27b8-4ff2-b320-de5ba10837d1 · inbound

DECA: A Near-Core LLM Decompression Accelerator Grounded on a 3D Roofline Model cites this paper.

DECA: A Near-Core LLM Decompression Accelerator Grounded on a 3D Roofline Model Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 75

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Observation 0af6810a-da2d-419c-840e-3bac0083962f · inbound

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study cites this paper.

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 37

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Observation a2cac9bb-05ea-4ff6-a91f-d0c1ecd524df · inbound

FPTQuant: Function-Preserving Transforms for LLM Quantization cites this paper.

FPTQuant: Function-Preserving Transforms for LLM Quantization Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 43

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Observation a05345fa-7475-41b2-a502-2699e1cf9580 · inbound

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models cites this paper.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 10

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source=pdf_text observed=2026-08-07T14:06:24.060629Z digest=sha256:a7a9a9f3bfd009dd9219aba7a1d91481f8589055969764a200f2d15ebd667cce

Observation 32bf6349-d0e6-4e50-bce4-50b815969fc6 · inbound

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models cites this paper.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 39

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Observation f247830d-ff20-4990-83eb-6e8bebe5da40 · inbound

PoTPTQ: A Two-step Power-of-Two Post-training for LLMs cites this paper.

PoTPTQ: A Two-step Power-of-Two Post-training for LLMs Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 24

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Observation 6b86ac59-2285-4dfb-8993-1e5bd2975a13 · inbound

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization cites this paper.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 63

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Observation 057bd4f2-a8c0-4035-9284-998b1ad53b9a · inbound

LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving cites this paper.

LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 28

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Observation 93c423a1-59f0-4a34-9066-41f91a54c11b · inbound

Efficient Reasoning on the Edge cites this paper.

Efficient Reasoning on the Edge Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 135

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source=pdf_text observed=2026-07-13T23:28:12.790404Z digest=sha256:7bca8219e6f32480d9ca9beb3171a34e4c60ba3eea603f5aecedae8d58a5a47b

Observation cc8739a6-9c27-4149-af0d-a97be86a3177 · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 29

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arxiv_id, observed 2026-05-11T12:46:04.675725Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T03:04:14.900791Z digest=sha256:ff38f4f2d6d85b1de14468c1850545bbf8e4e2bd92bed786a0abb895163706ee

Observation 72e8ee89-4cd9-434b-9197-301b6eeb3b86 · inbound

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities cites this paper.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 163

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arxiv_id, observed 2026-05-11T20:16:10.297825Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation fdf57dfa-0585-4c9d-ad2a-33e054ae9f25 · inbound

Quant.npu: Enabling Efficient Mobile NPU Inference for on-device LLMs via Fully Static Quantization cites this paper.

Quant.npu: Enabling Efficient Mobile NPU Inference for on-device LLMs via Fully Static Quantization Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 38

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arxiv_id, observed 2026-05-21T07:54:02.625596Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation cd16a273-dd71-4d18-9bd5-87328511b638 · inbound

GNMR: Runtime Stability Control for Low-Precision Large Language Model Training cites this paper.

GNMR: Runtime Stability Control for Low-Precision Large Language Model Training Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 57

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arxiv_id, observed 2026-06-28T19:42:36.071574Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e210193d-bab0-49e1-b61c-e1d60621ce0e · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 2

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source=arxiv_source observed=2026-08-01T17:12:27.226905Z digest=sha256:146b1790e411062039432afb59c4d38b7ea552d1cf350e224b28d5985e30ba0b

Observation 56f04a3c-c28b-420f-8654-1eea4d2fb4de · inbound

When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation cites this paper.

When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 46

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source=arxiv_source observed=2026-08-14T12:54:26.620007Z digest=sha256:13703d9fdf17d456c597d997dcd0c74cef30bbf4d461511b59599792f1e11244