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Paper Citation Record · LEDGER

SqueezeLLM: Dense-and-Sparse Quantization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 54 inbound Pith citation observations for arXiv:2306.07629.

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

pith.paper-citation-record.v1
2306.07629 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 54 of 54 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:28:42.183775Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:18:37.340082Z

Reference resolution

0 of 0 outbound references displayed

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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 fe5067cf-dab7-4166-aa91-a11026099e93 · inbound

ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models cites this paper.

ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 13

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arxiv_id, observed 2026-05-20T13:49:33.850130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T13:49:33.747672Z digest=sha256:71c514be925b5b1fda1614804318957bed7ae69ecf1ebd7825ec1f4ae28795e7

Observation 652a7ae9-34ed-4e07-bc1c-d8e964f9b301 · inbound

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads cites this paper.

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads SqueezeLLM: Dense-and-Sparse Quantization

Reference 81

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arxiv_id, observed 2026-05-13T10:36:18.341205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T10:36:17.764761Z digest=sha256:0f1c37d87d415edc72641402b0947bf32ab4647ebc50f6516e4b5b05acaced0b

Observation 37c8d920-1703-41cf-9a2c-069a3b3898ab · inbound

KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache cites this paper.

KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache SqueezeLLM: Dense-and-Sparse Quantization

Reference 10

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arxiv_id, observed 2026-05-12T08:53:12.337562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T08:53:12.253243Z digest=sha256:29f1c6e7d54882e004e0b1cb2dea8004576c49ec85de6321a0597467b625a76e

Observation 8c46ab81-0c82-4bf6-82d4-4f4f738c1f12 · inbound

RouterBench: A Benchmark for Multi-LLM Routing System cites this paper.

RouterBench: A Benchmark for Multi-LLM Routing System SqueezeLLM: Dense-and-Sparse Quantization

Reference 89

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arxiv_id, observed 2026-05-16T10:47:31.094875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-16T10:47:31.006944Z digest=sha256:66ec19c932bef4cf593216337b15749dff9da2b6b2c5e67379c51bf26d435c37

Observation dc0a389b-ad0f-4564-b0d7-fec01552fe30 · inbound

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

A Survey on Efficient Inference for Large Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 197

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arxiv_id, observed 2026-05-15T02:39:33.214075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T02:39:33.007894Z digest=sha256:7674e60d856c7f2c017272523e18576abf14f30a5a780e288ff39971c8e7bad8

Observation c2b6fba9-5454-4565-b948-54fe9cad1fec · inbound

SpinQuant: LLM quantization with learned rotations cites this paper.

SpinQuant: LLM quantization with learned rotations SqueezeLLM: Dense-and-Sparse Quantization

Reference 8

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arxiv_id, observed 2026-05-15T15:52:34.700791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T15:52:34.606853Z digest=sha256:cdf053a981dd15dc039688a482845e3a668ed80d2ce696e9c0661e16d4afbc18

Observation 8b308851-e505-4df8-ad27-7ad194f53a52 · inbound

Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives cites this paper.

Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives SqueezeLLM: Dense-and-Sparse Quantization

Reference 28

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no resolver link, observed 2026-08-09T11:28:42.183775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T11:28:42.183775Z digest=sha256:f8c79ad1ecff8e91940f49af34449aa8104f61df9b1d872f10aad58dbf3adb38

Observation 82638e70-4319-4cad-85ea-af7a65b3dd55 · inbound

Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding cites this paper.

Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding SqueezeLLM: Dense-and-Sparse Quantization

Reference 16

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source=arxiv_source observed=2026-08-08T18:42:15.010420Z digest=sha256:87d9ce452767dfc2a23dcddbabeef63db6011bec50891213a171bcabc6f19153

Observation 82fa24fe-d6f5-4e80-a3b1-bf4e25e3196a · inbound

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters cites this paper.

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters SqueezeLLM: Dense-and-Sparse Quantization

Reference 46

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no resolver link, observed 2026-08-08T13:44:00.544467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T13:44:00.544467Z digest=sha256:e3a4b66ada89b80c81677b46c565bf15b45fad882d5a26b1a5fda300070ac737

Observation 4eee505b-e77f-4694-be24-ab55ae4e2279 · inbound

On multi-token prediction for efficient LLM inference cites this paper.

On multi-token prediction for efficient LLM inference SqueezeLLM: Dense-and-Sparse Quantization

Reference 8

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no resolver link, observed 2026-08-07T21:38:34.760608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:38:34.760608Z digest=sha256:51965f0a8101cde4dc081b87f00f4a04495e649f94b405b26073c0a9333d98af

Observation 9949b154-5c0b-4936-8e4c-06b655bb5173 · inbound

QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache cites this paper.

QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache SqueezeLLM: Dense-and-Sparse Quantization

Reference 12

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no resolver link, observed 2026-08-09T04:28:03.938479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T04:28:03.938479Z digest=sha256:6b399a12ff65f5182622fa96c03e58b4ab1f3b3a09429aa68a68404bd52ac889

Observation a609dd30-4a14-47d9-b2e7-a04c77294da5 · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices SqueezeLLM: Dense-and-Sparse Quantization

Reference 174

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arxiv_id, observed 2026-05-23T01:05:16.310998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T01:03:26.037233Z digest=sha256:3a3dfda2a79ef75203296b0c0bb4bd39d41e9c09a48ded52c8e7a148b6b74bc0

Observation e28853ba-9226-4caf-bf9b-35df3a4991a8 · inbound

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate cites this paper.

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate SqueezeLLM: Dense-and-Sparse Quantization

Reference 37

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verified exact
arxiv_id, observed 2026-05-20T08:09:22.277661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T08:09:22.226608Z digest=sha256:93e2c718c634044a46fb11fe3ba51e05f112ff0a47469ee95573df2e2443d43e

Observation 929dcb5e-aab5-4020-aca7-84de10b3c0bc · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 10

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arxiv_id, observed 2026-05-22T16:34:59.261977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T16:34:37.083239Z digest=sha256:13fadd5272b7468f6144d6be2091c34eb70f034febd8a74bc8fe9061d6b39b05

Observation 776bbb39-0683-4ba8-a428-617b2fe31cc3 · inbound

Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference cites this paper.

Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference SqueezeLLM: Dense-and-Sparse Quantization

Reference 27

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no resolver link, observed 2026-08-07T15:36:05.656359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:36:05.656359Z digest=sha256:4ec7657a63b22247dc9a464b8338cf43ed335bc9789bd59188df517a8284f1c9

Observation 61b27dfa-542c-4af4-b9dc-1325f222f8d7 · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 25

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no resolver link, observed 2026-08-07T15:09:28.498086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:09:28.498086Z digest=sha256:effc97f83d2cc7858c79254bc5d8c1090ab575c39fc475a288da871f55fb45bd

Observation c33b91b4-7076-4fed-839a-2d4ea3d92494 · inbound

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache cites this paper.

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache SqueezeLLM: Dense-and-Sparse Quantization

Reference 22

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no resolver link, observed 2026-08-07T14:42:21.069575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:42:21.069575Z digest=sha256:5fbcf80f0fc372ece7204a886c27f3313dd9e74b4c10ac6d5ad970b04962aeff

Observation 522070b8-6cf0-4e0d-a01a-26bae17ff451 · inbound

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression cites this paper.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression SqueezeLLM: Dense-and-Sparse Quantization

Reference 26

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no resolver link, observed 2026-08-07T14:17:56.372336Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:56.372336Z digest=sha256:fcdeb48fd043531f476502e9992588bedf1d6e3d069121aa3718ed625f135b26

Observation d2d1cc3b-7958-4a42-afa3-9fa22aa93fd5 · inbound

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression cites this paper.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression SqueezeLLM: Dense-and-Sparse Quantization

Reference 27

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no resolver link, observed 2026-08-07T14:17:56.464514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:56.464514Z digest=sha256:774937403e432703519e3f2d1d4c5a937851112a3efcefd01eff6f5736d6de1f

Observation 8667e534-2518-4bf2-954d-47bdcf62d9c7 · inbound

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

FPTQuant: Function-Preserving Transforms for LLM Quantization SqueezeLLM: Dense-and-Sparse Quantization

Reference 36

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no resolver link, observed 2026-08-07T10:43:46.395454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:43:46.395454Z digest=sha256:e2ae484a1fd1d248324954a7a6b362e079159a2743c0c62be3df68c0e13708a5

Observation 35aa94be-e2a4-4b81-aa83-0b824d01744a · inbound

BAQ: Efficient Bit Allocation Quantization for Large Language Models cites this paper.

BAQ: Efficient Bit Allocation Quantization for Large Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 11

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no resolver link, observed 2026-08-07T10:18:52.624716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:18:52.624716Z digest=sha256:a7935635aa85071f7a5dc9c1559a38453d75a183bfc21fab7f54549c62104cd1

Observation 0c4f7916-3238-492b-bb86-7a95c6562580 · inbound

Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs cites this paper.

Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs SqueezeLLM: Dense-and-Sparse Quantization

Reference 16

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verified exact
arxiv_id, observed 2026-05-19T08:53:04.144810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T08:52:45.818050Z digest=sha256:184daa2f1a910470e23ac1cebb11f2dbea0bfc3f0da7b57d90672721ecc532ab

Observation 53dfb406-e291-4a4b-badf-f4e1a106241a · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 24

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no resolver link, observed 2026-08-06T23:00:19.558832Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:00:19.558832Z digest=sha256:835c91a96ac88a930213e3a941d462d88cf44bbf919f95a86e3a13b6797e43a3

Observation deb9f2da-2284-4ec8-bf66-743303f6bb47 · inbound

Information-Bottleneck Driven Binary Neural Network for Change Detection cites this paper.

Information-Bottleneck Driven Binary Neural Network for Change Detection SqueezeLLM: Dense-and-Sparse Quantization

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:15:24.158945Z digest=sha256:664e90f057bb95685c01ee09ba18558f3804fe80a91620abce95a677213fcb4a

Observation 14813428-cb77-415d-9713-4f4d6f3f670b · inbound

CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs cites this paper.

CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs SqueezeLLM: Dense-and-Sparse Quantization

Reference 15

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no resolver link, observed 2026-08-06T19:09:14.550295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:09:14.550295Z digest=sha256:15e0064a78a2e83c250f3a50f3d57b7751c18e045d8514b708419ab45647c855

Observation e84da4c0-1e3d-4d94-82c6-e8450d5c288a · inbound

OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration cites this paper.

OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration SqueezeLLM: Dense-and-Sparse Quantization

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:15:33.956446Z digest=sha256:2abc8b7860a1e74e4c10bcd9266576400181e1dd4d4e45f4ce1719f9dd69f897

Observation 7aa8ef5c-c2e7-459f-b440-804064a16c33 · inbound

Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs cites this paper.

Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs SqueezeLLM: Dense-and-Sparse Quantization

Reference 23

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:52:44.950132Z digest=sha256:c51c7e38d2fdbc431e5cd3e67c8795d76f3d722170947b6d6c08b8f53c51e1a9

Observation 5345532e-07ac-4710-acc3-c0145d49c1b6 · inbound

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference cites this paper.

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference SqueezeLLM: Dense-and-Sparse Quantization

Reference 15

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no resolver link, observed 2026-08-03T23:41:52.877669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T23:41:52.877669Z digest=sha256:bf8e84c1fef1a6635e8e01e329b365ef140c908862208f2be23c9bc6f128e1b4

Observation 0985625e-76f9-437f-ad97-54c08b11d733 · inbound

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation cites this paper.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation SqueezeLLM: Dense-and-Sparse Quantization

Reference 23

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no resolver link, observed 2026-08-03T06:38:22.663076Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:38:22.663076Z digest=sha256:6c231561fc268eaf4ccd6ef7117ca37b890fd2f1a0d1f768151df42bf41bee9a

Observation bd74cafc-1369-4735-b1e1-8dc004cf81fc · inbound

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization cites this paper.

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization SqueezeLLM: Dense-and-Sparse Quantization

Reference 10

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verified exact
arxiv_id, observed 2026-05-16T06:52:28.393534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T06:51:18.629467Z digest=sha256:431e3c65033d3e6ca9ac69f73c317b03416d7aae6242f42a262e5dc8ca5fc6be

Observation 4f1281b2-6e40-4ab6-8b75-236e1bbd952f · inbound

On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks cites this paper.

On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks SqueezeLLM: Dense-and-Sparse Quantization

Reference 15

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metadata mismatch
arxiv_id, observed 2026-05-10T00:24:47.003271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T00:21:30.101748Z digest=sha256:1d7088ea4dc50aa017e8682abc1a4900c5d62ecaeec112ff9511e152f2d7045a

Observation 4de3b8e2-c80f-463d-a5d6-a86773e97d0d · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 13

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verified exact
arxiv_id, observed 2026-05-11T21:41:16.200257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T04:35:02.120009Z digest=sha256:64d64f49aa39ec53f4da023cd4ef3259fe0885340fced90f5eb853d25e87bd70

Observation e07df099-eae8-4bee-9a7e-d68669a9ec3a · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 2

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metadata mismatch
arxiv_id, observed 2026-05-11T21:46:42.443324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T04:23:26.079298Z digest=sha256:e55834df69d0b235db20849027d38cf2be5c4cb47dbdbcaa9e04becb6c00689d

Observation 8f50f0c4-d94e-433f-ab19-6021234e0e44 · inbound

DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning cites this paper.

DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning SqueezeLLM: Dense-and-Sparse Quantization

Reference 24

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arxiv_id, observed 2026-05-09T06:10:42.160072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T18:49:53.432959Z digest=sha256:d110ad4ce4794cdfe452a22ff7a8ae353fc95194f48fca9bdd1eacee75311fbe

Observation 39e3d22d-1c77-4575-9115-5a4f1c462bc9 · inbound

DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning cites this paper.

DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning SqueezeLLM: Dense-and-Sparse Quantization

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T01:50:51.165324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T01:49:51.021741Z digest=sha256:32b46bc3de0141e6f9063e298315b406bf865a8ce8ba68e44ba60bd9684157ee

Observation 99e832e7-3ec7-4588-b1ac-415de378cc29 · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:36:08.029916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T16:06:26.450483Z digest=sha256:0a595d793ba5c8958dddb083283993d57ccc89c5c43990899bb3cb80b5aea45a

Observation 0d0da963-e2f9-4554-b344-1f115737f3f5 · inbound

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning cites this paper.

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning SqueezeLLM: Dense-and-Sparse Quantization

Reference 121

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T07:01:10.321373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T17:19:59.247074Z digest=sha256:fac99bbd0346e7862ecf0627ad55b50f0ec6b40e086fac3358465721e62ca9b7

Observation ee2ac829-f9e2-4fd6-ad98-c96f3e2bc34f · inbound

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization cites this paper.

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

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:30:44.142303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T18:23:14.935801Z digest=sha256:b5f3edd75fb98ad37199ccb01e8f45e1076f9338a3932895df5b585e38fbd86f

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

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization cites this paper.

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T02:59:00.997742Z digest=sha256:7ad2b54596c0c9887272c18cd3086f59626795541f68bb22c792e10612c72d43

Observation d16da3e7-de78-4192-8548-66862fc2ff1b · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-30T21:35:04.690279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T21:28:36.358474Z digest=sha256:10b7f653e0583519c90b0d497aa1af83b17e695eaf098c822455b8bea5887ac7

Observation 7ec28d36-0207-4a96-98b0-9217129dccff · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T05:23:03.625863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T05:20:45.264341Z digest=sha256:62b0b673058378bbf656db30491fbbf7b6428a875b772b3efafa6008e77ae9ab

Observation 9bd3ab0c-6349-4222-a3c0-7f1dfd3eba6d · inbound

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

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs SqueezeLLM: Dense-and-Sparse Quantization

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:36:39.872360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-25T05:33:06.719954Z digest=sha256:54f1ad3f7b4cbe3399a42740e53305a88786609794bb0fdb91391dc7dd1ac7a9

Observation bf0cc52c-1c31-4da3-9a8e-9cba87bf348f · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T18:04:58.491515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T17:54:56.386488Z digest=sha256:0ec6f1978cacc9de87f4860fd67589370075764d959582ac1dbb8a30eab2e54f

Observation 9f262736-e8cc-4a2d-ada2-88c261a3f7b2 · inbound

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation cites this paper.

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation SqueezeLLM: Dense-and-Sparse Quantization

Reference 73

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T19:02:34.617720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T18:55:51.474956Z digest=sha256:4bf18bf01ea690426f1f66f80ccf13c34b57f8e91639bd252ed4fd4fa1578c9d

Observation d4b4ba6f-6db8-4589-807f-f054a40c3feb · 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 SqueezeLLM: Dense-and-Sparse Quantization

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-07-01T21:06:14.475000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T17:28:14.160341Z digest=sha256:0976ffd5d86766a971c89299e14e3bdbf76bf48c34dca4cc4d3bdf33d5a551d3

Observation cb5672a2-5a67-43de-ac03-95457cefd64a · inbound

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models cites this paper.

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T08:16:48.293704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T06:09:42.838355Z digest=sha256:4d49e4522d1682431688ead10038697957902fc1a19754811dbe861861db17c1

Observation cc0d0cdb-359f-4fbf-a642-c00e4281e965 · inbound

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs cites this paper.

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs SqueezeLLM: Dense-and-Sparse Quantization

Reference 141

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T15:25:48.408417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T01:29:42.919461Z digest=sha256:6046a25f6d41df0bcabddc00712c61e9bcc5e1f89787df181c04a9bb4506b6aa

Observation 1bac6c64-6ee8-4ee4-a0bd-310a5e10279c · inbound

GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache cites this paper.

GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache SqueezeLLM: Dense-and-Sparse Quantization

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T15:57:06.403971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-02T15:55:40.177742Z digest=sha256:1d42bd1274881d0d8eaa56af32c289f1273cc6cac644dee042e300454b54592a

Observation 807090fd-1b0e-484d-b714-c291eb299548 · inbound

SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models cites this paper.

SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T16:18:37.341488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-03T16:14:03.717787Z digest=sha256:0cda4087b4ac864b91f3b8650181240b0580b42d4d84a3649970ea852b6b88c3

Observation 0e56bdc7-8bc2-4799-9fed-944eb3b39dbb · inbound

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

OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers SqueezeLLM: Dense-and-Sparse Quantization

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:58:32.417750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-03T14:56:10.553212Z digest=sha256:07f7b0baf2ba8f080fc6984f5881d889ee07cb855a345bda9e733ef3821d040d

Observation 91ab3514-5ab9-417b-bd6b-a58cc4aea220 · inbound

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration cites this paper.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration SqueezeLLM: Dense-and-Sparse Quantization

Reference 40

Resolution
unresolved
no resolver link, observed 2026-07-13T01:10:03.032181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:b1808e1e35b2fe26e21f7eec65d0cd8a7fb0830fc81aff8f9a88e9d19e94c7f4

Observation 25b40a43-1701-4822-b146-c3fd2fdc511c · inbound

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

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference SqueezeLLM: Dense-and-Sparse Quantization

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-01T17:12:27.941291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:12:27.941291Z digest=sha256:3f5bafa14c439e131ba05fdcd50348009a4894e84d9aab7d60f64674c4727c9c

Observation a32228b5-c235-4152-960b-a3ebecdb9bf7 · inbound

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications cites this paper.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications SqueezeLLM: Dense-and-Sparse Quantization

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T12:04:14.348367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:04:14.348367Z digest=sha256:dcd7e67a0a62dbb327ed0c01161e9386aa33b4ac63d9bf3bd34c62d22ed3ce49

Observation c46894f7-5422-494f-8ba4-abcadcbcafc3 · inbound

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs cites this paper.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs SqueezeLLM: Dense-and-Sparse Quantization

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-08T00:50:44.530504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:50:44.530504Z digest=sha256:d4b30365184c8a9fa48f4ffce477e6c1a61a36c5ba204f2d2047f0150334de71