Paper Citation Record · LEDGER

SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 42 inbound Pith citation observations for arXiv:2306.03078.

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

pith.paper-citation-record.v1
2306.03078 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 42 of 42 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 42 of 42 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:24:19.403461Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

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verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bb0773e2-c299-4c5c-b311-4e488060f525 · inbound

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

A Survey on Efficient Inference for Large Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 196

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.210769Z

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 abbca5a6-faa9-4c66-9b1e-0a241ffeff3a · inbound

EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices cites this paper.

EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-22T16:34:59.286285Z

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 c47f2b99-b8d2-47f0-a23a-a326b70c7a08 · inbound

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction cites this paper.

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:47:07.687089Z

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 dc117cc0-8f7c-4e3e-94a1-dbcfbac4d388 · inbound

Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM cites this paper.

Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T11:24:19.403461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c40f42f-b3c2-42b6-ac17-82841a3ccebe · inbound

Collaborative Lossless LLM Inference Serving with Offloading-based Pipeline Parallelism on Edge Devices cites this paper.

Collaborative Lossless LLM Inference Serving with Offloading-based Pipeline Parallelism on Edge Devices SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T14:04:34.644179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5199e9da-5a26-450e-8ec1-9e215102956d · inbound

Harmonia: Algorithm-Hardware Co-Design for Memory- and Compute-Efficient BFP-based LLM Inference cites this paper.

Harmonia: Algorithm-Hardware Co-Design for Memory- and Compute-Efficient BFP-based LLM Inference SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T04:37:26.060146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56105472-385e-4698-963b-1c5ff359324d · inbound

Fast Entropy Decoding for Sparse MVM on GPUs cites this paper.

Fast Entropy Decoding for Sparse MVM on GPUs SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T19:38:56.284753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 912358a8-8cba-4513-9622-c2cc17e05a8c · inbound

Efficient Reasoning on the Edge cites this paper.

Efficient Reasoning on the Edge SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 124

Resolution: unresolved; no resolver link, observed 2026-07-13T23:28:12.790404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2d37f02-7cb0-4f3a-aed4-d8319b61500f · inbound

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling cites this paper.

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:25:56.668692Z

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 23fd9b77-2b6d-4bb9-8ccc-76ff4ee2878d · inbound

TStore: Rethinking AI Model Hub with Tensor-Centric Compression cites this paper.

TStore: Rethinking AI Model Hub with Tensor-Centric Compression SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:21:27.081572Z

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 c9f6c509-6706-40b0-bc7c-e2b5e87c82f6 · inbound

TStore: Rethinking AI Model Hub with Tensor-Centric Compression cites this paper.

TStore: Rethinking AI Model Hub with Tensor-Centric Compression SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:08:04.731475Z

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 c7d73a95-d2fe-4d80-b28c-16bb1f9a002a · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:36:02.324016Z

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 137de9a0-079b-43b2-b6ed-fab5d6b3da09 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:02:42.165210Z

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 6e088adb-7b8d-44b9-b8f5-fa52471cfced · 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 SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:46:04.928422Z

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 dd2d1e82-23ad-468d-8bf9-35cbc370a7c9 · inbound

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization cites this paper.

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T02:38:17.222652Z

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 402acaad-10c2-44e7-aeb3-3b5d57c70dba · inbound

Variance Is Not Importance: Structural Analysis of Transformer Compressibility Across Model Scales cites this paper.

Variance Is Not Importance: Structural Analysis of Transformer Compressibility Across Model Scales SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:26:04.489349Z

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 6c94e8d7-3868-43ef-a1ff-2d9524769418 · 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 SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.423526Z

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 04fc6925-320a-4bf0-9578-6fb8c4805269 · inbound

Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels cites this paper.

Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:41:16.276376Z

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 508720db-69cb-483d-9c1a-ca2eb750e94e · inbound

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment cites this paper.

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:42.449581Z

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 a31d2bd8-75ae-4446-84fa-f321c3bdd100 · inbound

WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization cites this paper.

WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:08.092490Z

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 7c4972d2-18c2-42e6-84af-5789017e334a · inbound

Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven cites this paper.

Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:46:08.487611Z

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 1bb94b70-b3d9-4bcb-9d5b-38cb1b382a7c · inbound

XtraMAC: An Efficient MAC Architecture for Mixed-Precision LLM Inference on FPGA cites this paper.

XtraMAC: An Efficient MAC Architecture for Mixed-Precision LLM Inference on FPGA SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:11.301572Z

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 69fc575c-709f-4493-856f-b0fbb3ae6b16 · inbound

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models cites this paper.

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:37:07.849512Z

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 25a9cd37-c230-48c1-898e-23fd8e314672 · inbound

XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference cites this paper.

XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:35:04.693173Z

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 525299f0-dde1-4505-bead-c2e73308cb40 · inbound

StatQAT: Statistical Quantizer Optimization for Deep Networks cites this paper.

StatQAT: Statistical Quantizer Optimization for Deep Networks SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:22:55.766489Z

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 7f9f3ad1-5da1-465e-8b40-aa5a0d1a5d26 · inbound

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models cites this paper.

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:23:03.685044Z

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 d74e7e15-7d25-4983-b79c-95b16e64e007 · inbound

Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification cites this paper.

Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-21T09:39:57.649753Z

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 6785d84f-8a65-4c56-840b-839f7360b804 · inbound

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs cites this paper.

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:39.947415Z

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 1024fff2-658c-4bfd-af25-aaae7e16e62a · inbound

ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models cites this paper.

ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:05:47.964211Z

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 502cb8de-5919-4714-b595-88ad5a0b764c · inbound

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation cites this paper.

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:06:14.443665Z

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 717bb3ab-73a8-48f7-8b9e-9643b389c5e3 · inbound

Averaged Evaluation Masks Capability Trade-Offs: Multi-Source Calibration for High-Sparsity LLM Pruning cites this paper.

Averaged Evaluation Masks Capability Trade-Offs: Multi-Source Calibration for High-Sparsity LLM Pruning SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:56:27.563117Z

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 3ee4bc38-d30c-49f2-ad35-d4906aae8c83 · inbound

Do Transformers Need Three Projections? Systematic Study of QKV Variants cites this paper.

Do Transformers Need Three Projections? Systematic Study of QKV Variants SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:36:17.239860Z

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 1e05ef26-205f-4fbf-bf74-2ac4256e16f8 · inbound

MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models cites this paper.

MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:56:44.617007Z

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 f96606f8-32f2-469f-be75-f20943798333 · inbound

ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE cites this paper.

ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:26:56.437227Z

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 4c7493ab-d888-45ef-b06e-f4f834f5e3da · inbound

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation cites this paper.

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:16:15.998711Z

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 fc55f228-6f35-4346-95aa-4801b5f1abe8 · inbound

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization cites this paper.

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:48:20.625511Z

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 e9202480-a0cc-4eb9-b907-2e86d001c1fb · inbound

HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models cites this paper.

HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:45.520230Z

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 0be784fa-4d15-4337-ba3b-dc65e0879361 · inbound

OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers cites this paper.

OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:58:32.414192Z

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 1608e6ca-b4d6-4e66-970f-76d12497b53a · inbound

From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving cites this paper.

From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T09:50:23.266920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 985908c5-d4d4-4e43-a1c3-239b203a1eea · inbound

Reliability Scaling Laws for Quantized Large Language Models cites this paper.

Reliability Scaling Laws for Quantized Large Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 88

Resolution: unresolved; no resolver link, observed 2026-07-14T08:45:52.855783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c93a4183-b56e-4ef5-ab2f-0cba141bdf49 · inbound

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems cites this paper.

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T03:55:40.314783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c55b6ea-52d1-422e-b93f-4f07719a5c3a · inbound

PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization cites this paper.

PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T21:09:06.957974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.