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Paper Citation Record · LEDGER

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights

As of 16 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2608.06763.

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

pith.paper-citation-record.v1
2608.06763 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:37:51.969859Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

31 of 31 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 35ddb784-c191-4b63-b602-bd0ac0517b42 · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 1

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.866445Z digest=sha256:9c32dcac9d2ab745bf249f7150c23d95f10a2e241e60929c2b102cf1ea22f805

Observation 8952b72f-b4e2-4d03-87eb-8275e0c51054 · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 2

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.870534Z digest=sha256:903ed8a78fa6e6f4ff6e9f5a7dd1d78c5e14bf1dc95582931189a85bb32bd46a

Observation fbf75246-eb19-4634-b929-6e3dea3213a5 · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 3

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source=pdf_text observed=2026-08-15T14:37:51.874367Z digest=sha256:838104718aab2529abbc534673ccbeaeeedaae108fc73f408c42077876fe19ce

Observation 02858430-7466-4380-8513-dad4b4f0eaf9 · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 4

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source=pdf_text observed=2026-08-15T14:37:51.878299Z digest=sha256:12931c9e4ba230ec86503a1584977bd2d16395749b722accc149af2aaef26be0

Observation 8b3fc2d1-bb4e-4938-b953-6bbaa027385c · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 5

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source=pdf_text observed=2026-08-15T14:37:51.882079Z digest=sha256:e6f147f7c8b5bf15504d993edd843c1d623d6c9724d7ed4b6517c1084542d2ff

Observation 6d5977e7-6bb8-4793-9324-883c3b0a37db · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:37:51.885673Z digest=sha256:8367cd484e5e81a47c85bc10ae133d5f4bdbaec1dabe6540e34b95f063e709eb

Observation ab3dcfb9-137c-4320-8d73-aaeff59185d1 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights QLoRA: Efficient Finetuning of Quantized LLMs

Reference 7

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source=pdf_text observed=2026-08-15T14:37:51.889813Z digest=sha256:fd393643eea4e4a78084fe06b0f1a1f01d4005dd4a1cf09ff37fee563ddb1e31

Observation 7798a314-f5f4-4792-b3e9-94eb94066060 · outbound

This paper cites NF4 Isn't Information Theoretically Optimal (and that's Good).

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights NF4 Isn't Information Theoretically Optimal (and that's Good)

Reference 8

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source=pdf_text observed=2026-08-15T14:37:51.893158Z digest=sha256:3ce2bafb031ea090610c88754041e7376a474df67a2a8f7dcd06725d1a805725

Observation 1416a9ed-452b-45ac-9d02-d784546e38cb · outbound

This paper cites Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations

Reference 9

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source=pdf_text observed=2026-08-15T14:37:51.896735Z digest=sha256:4aa3c66e8849e022ebdb20b471ea085c346c9d378037878c6d7d07b9c0339035

Observation d3c3b17e-05a4-4c1b-8dba-5d365b6c18f9 · outbound

This paper cites Cook et al.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Cook et al

Reference 10

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source=pdf_text observed=2026-08-15T14:37:51.899676Z digest=sha256:bc63d8803833b52d1f96cd3c682f050891ab7aafe02d3058f80a8c87bc5d4917

Observation cdb6bb5d-f966-4433-9ba1-0d323b787b6f · outbound

This paper cites Elangovan, C.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Elangovan, C

Reference 11

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source=pdf_text observed=2026-08-15T14:37:51.902525Z digest=sha256:638e9f8d2122a6cbe4490567ecf9ed123215c5cded73221e921a884b2690089d

Observation 590b14fd-1e57-47f3-837e-22577d86488c · outbound

This paper cites AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

Reference 12

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local_arxiv, observed 2026-08-15T14:37:52.153508Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.905348Z digest=sha256:f830502fb0568156a6bd608e25be079f819f513a8450cae80343ec5045fb1c04

Observation 3c247724-cdf6-4299-851d-c9a240268d3d · outbound

This paper cites FP8 Formats for Deep Learning.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights FP8 Formats for Deep Learning

Reference 13

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source=pdf_text observed=2026-08-15T14:37:51.908150Z digest=sha256:49d84c8b5d757e378808d6e711cef3f9bc3f70405cf61440d1987747e2e80193

Observation a7416f9b-af4f-4111-8886-f14a698dd9e2 · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights SqueezeLLM: Dense-and-Sparse Quantization

Reference 14

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source=pdf_text observed=2026-08-15T14:37:51.911058Z digest=sha256:dc64bf89a377300cbacb05a1e5093e1ee45d6011cdaa194154673f5307f5825e

Observation 94a4d206-ecce-4dd5-9da6-7fc639909889 · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 15

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source=pdf_text observed=2026-08-15T14:37:51.914357Z digest=sha256:7bce74c7097a49038e62ed227a015b119208da826f31909e78d0faaeaa883a55

Observation d1d3c721-2f17-4c0b-a6ac-4401bff74d62 · outbound

This paper cites Extreme Compression of Large Language Models via Additive Quantization.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Extreme Compression of Large Language Models via Additive Quantization

Reference 16

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source=pdf_text observed=2026-08-15T14:37:51.917762Z digest=sha256:d52120fc43b31a3e05a2cb15f348dd1fc599398391ce8169f0a119371db78887

Observation cfb16197-e846-4ae6-9451-ab02bc3c76a6 · outbound

This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 17

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source=pdf_text observed=2026-08-15T14:37:51.921431Z digest=sha256:d94f01b1ae8284569a996cb336adfad1b205907daa8813d284d2184d9a2dbb03

Observation 1b8385ab-6c08-48de-bb60-068aa42be394 · outbound

This paper cites LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models

Reference 18

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source=pdf_text observed=2026-08-15T14:37:51.925114Z digest=sha256:e1d9d735e1f99e6b4a46629fa9d9778d9895e6ef4249ee1e8d1a3a782b8ead03

Observation 89c1a04a-2a02-425b-887d-10716361de31 · outbound

This paper cites Fast Matrix Multiplications for Lookup Table-Quantized LLMs.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Fast Matrix Multiplications for Lookup Table-Quantized LLMs

Reference 19

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source=pdf_text observed=2026-08-15T14:37:51.929038Z digest=sha256:b02729ca42b7e89f5cb501db49dbbc806ff375a727f14e1dfcf1e671267c7f84

Observation 018de6fc-3bc7-4061-a741-0e320dd5ff01 · outbound

This paper cites MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models

Reference 20

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source=pdf_text observed=2026-08-15T14:37:51.932311Z digest=sha256:a524dedb4f51cb2523693a8b1a9996dd92d7edb19a6368ff239321b1d67d26fa

Observation d434098f-a569-4f9d-9f0b-aef64c8d30c3 · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 21

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source=pdf_text observed=2026-08-15T14:37:51.936116Z digest=sha256:cfd4dd26507556f5b89e6a72d9cd00ce540b655aca29a0a4a5c68ac3c37bef57

Observation 6276d88e-1bee-4694-b241-3aaf305918bf · outbound

This paper cites ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 22

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source=pdf_text observed=2026-08-15T14:37:51.939462Z digest=sha256:7236d4e054e3b5acc7e342f3144cdad7ed892d76d47c959ad4ae7e86bd60c597

Observation c0adacbf-fd65-45f8-a7b3-5a8d6e1922d4 · outbound

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

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 23

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source=pdf_text observed=2026-08-15T14:37:51.942650Z digest=sha256:9b28561ea835cef727a941e090db123a379057ac9c93d789e926806e1174bfd6

Observation 3647b336-4397-49b9-b324-839da58f1fa1 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 24

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source=pdf_text observed=2026-08-15T14:37:51.945821Z digest=sha256:189ba3409f7a5264ef976ea4390928b4dceb72a156658dcca4402b69ff6e16b7

Observation 140d3f71-4c16-4e20-9abd-ac194f11a5a2 · outbound

This paper cites CUDA Binary Utilities.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights CUDA Binary Utilities

Reference 25

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raw_fallback, observed 2026-08-15T14:37:52.518911Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.948863Z digest=sha256:57047a4c22806baf64fb21654df61547069220b2e1d9617a0a293d56673621c5

Observation 2ec69a78-a05c-4d32-94ac-0b6cba3fcf93 · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.952237Z digest=sha256:b5292de9e7a590e05329447dac0a8950fd827d2bd184fd8c181e0fd83f2f9e9a

Observation 30ec63d6-fbe9-4de1-8147-89ed7525649c · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.956378Z digest=sha256:56fd794d2f9af6f63c824a822608d3c3a63f9ea0c6c7627656e9af0dfddc324d

Observation 161300db-3004-4dd2-9996-a68f93f55c7f · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 28

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raw_fallback, observed 2026-08-15T14:37:52.485020Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.959751Z digest=sha256:d0dcdc5c74d92e92da5a06efbda169f5936101b50b8c5c1489f4bf834b234826

Observation 96264e0d-bf53-4bf2-8007-c01b467df09f · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 29

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.963159Z digest=sha256:aa64ad5e85fcde952846f81f25119ec9798634a0f826dc74adfd7807b518d817

Observation 3f31e6b3-42c2-4fc1-a64c-c815e6c727c5 · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 30

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.966477Z digest=sha256:e8b68e68da0bf57db60922dab5ef5906ae510d5993d85022af364aff722f342e

Observation 23fa6840-28b7-467d-8366-51fd919063d3 · outbound

This paper cites an unresolved cited work.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T14:37:51.969859Z digest=sha256:c25a66c3a4e3b0780aefda191aaa1e691f78d78e582561b2977baece69e33c28

Pith citing papers

No inbound Pith citation observations are available.