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Paper Citation Record · LEDGER

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2608.04048.

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

pith.paper-citation-record.v1
2608.04048 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:50:44.621739Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

33 of 33 outbound references displayed

  • verified exact2
  • verified fuzzy3
  • unresolved27
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 7504a75c-5692-44ae-8552-3705cef79ed2 · outbound

This paper cites GPT-4 Technical Report.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs GPT-4 Technical Report

Reference 1

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Observation b53ea7b1-e9e7-4f4c-9c8d-4d19c6cbc46d · outbound

This paper cites DeepSeek-V3 Technical Report.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs DeepSeek-V3 Technical Report

Reference 2

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Observation 8ad853d0-c0b0-4c6b-8aca-8e037437a905 · outbound

This paper cites Qwen3 Technical Report.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Qwen3 Technical Report

Reference 3

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source=pdf_text observed=2026-08-08T00:50:44.475131Z digest=sha256:0b9e0da1bbdc1e4c629db584acc98c02e0cd62fe6b2daa558e2b1e2db47c5107

Observation ad94c565-a6b3-4e6a-b588-7d8e37869a9d · outbound

This paper cites The Llama 3 Herd of Models.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs The Llama 3 Herd of Models

Reference 4

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source=pdf_text observed=2026-08-08T00:50:44.480239Z digest=sha256:0f1a827747b16a92a9158c40a8c5c6d9ab5e9ba6fe859d9f9f0435e9a690e74e

Observation 96cf0907-de23-44e5-b765-90344cc776a6 · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 5

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source=pdf_text observed=2026-08-08T00:50:44.485168Z digest=sha256:3853d57b9334852725215fd817c3322ba856577d1a2b037bbf35873060366e80

Observation 3f3fa94e-efb8-44e7-86ab-fcfea5f417e4 · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 6

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Observation 44addd7a-2979-4bdb-bcad-3bb2feb6bee7 · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Reference 7

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Observation cc13639b-2bbb-4c2c-a67b-772753eecef0 · outbound

This paper cites an unresolved cited work.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Unresolved cited work

Reference 8

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Observation 51480185-9103-4bb8-8cd9-e04c5e4ce676 · outbound

This paper cites Autoround: Advanced quantization algorithm for llms.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Autoround: Advanced quantization algorithm for llms

Reference 9

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source=pdf_text observed=2026-08-08T00:50:44.505422Z digest=sha256:e7bc22ac1ba24e43f71f596e0fb2223c93e1b3fb933364b16b93266655baf7b0

Observation 7fa38eb5-5e38-47c4-b1e9-9267f7fad42d · outbound

This paper cites DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

Reference 10

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source=pdf_text observed=2026-08-08T00:50:44.510061Z digest=sha256:eb0f549882396e414694e7f8b6dbb57d8470a4e486c4119afc59a6c22d4631ab

Observation f44ce703-8e6b-41ec-90d2-7de3488c9407 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Gonzalez, Hao Zhang, and Ion Stoica

Reference 11

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Observation f4cb2e1d-6332-494e-ade7-40491c318a59 · outbound

This paper cites Matryoshka Quantization.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Matryoshka Quantization

Reference 12

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source=pdf_text observed=2026-08-08T00:50:44.520201Z digest=sha256:070168d1ae20da755e0e634435ffca666aaf9064f5517ff33a425d489d89eed8

Observation 611c6721-e368-4b38-9274-cbdd26e74f18 · outbound

This paper cites Theoretical Grid-Forming Extreme of Inverters.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Theoretical Grid-Forming Extreme of Inverters

Reference 13

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source=pdf_text observed=2026-08-08T00:50:44.525345Z digest=sha256:4304af5836c96e2e7a3d3f8c9284cdd28b81ae1567edf0ff2caf644b2a4d0d87

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

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

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

Reference 14

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Observation 9b657036-7739-4565-99a4-a304fc1d5b48 · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 15

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Observation 17ae9737-fa3b-4950-bc93-c6cb68bbf6c9 · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 16

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Observation 4aa58001-ee2b-486f-ab5f-c93f5be466fe · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 17

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source=pdf_text observed=2026-08-08T00:50:44.544992Z digest=sha256:c4a85f68efc35ae7bfce8c9b3d692baa35ec253d4da35affa2ae655d9031b029

Observation f45dc4b4-e35c-4d53-9dbc-029e2a64a2fc · outbound

This paper cites HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision

Reference 18

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Observation 18be5264-191e-4268-91fe-a3cea5259322 · outbound

This paper cites HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks

Reference 19

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Observation e38827b4-81b1-46d4-992f-b2762537cbb5 · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Reference 20

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source=pdf_text observed=2026-08-08T00:50:44.559064Z digest=sha256:540ba4feecb894f7399d81d569389b20e11b24d69e81949b8a94690c6fd0d7f7

Observation 399431a4-cbb8-4d61-9e7b-a8ea533ea0a5 · outbound

This paper cites Juang and A.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Juang and A

Reference 21

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Observation a712fa5a-f6be-4e8a-9495-14d28005747c · outbound

This paper cites A convolution type model for the intensity of spatial point processes applied to eye-movement data.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs A convolution type model for the intensity of spatial point processes applied to eye-movement data

Reference 22

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Observation 51d759a7-d75f-4b65-8c01-3289a55b9c47 · outbound

This paper cites Post-starburst galaxies in the centers of intermediate redshift clusters.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Post-starburst galaxies in the centers of intermediate redshift clusters

Reference 23

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Observation 809efcc9-c5ff-49d1-b094-a56de27aaf89 · outbound

This paper cites Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization

Reference 24

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Observation f8ff366e-1770-45e8-ac9e-fe0d045b1de9 · outbound

This paper cites Massive Activations in Large Language Models.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Massive Activations in Large Language Models

Reference 25

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Observation fecd4785-3405-4cba-93aa-b69654045e7f · outbound

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

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 26

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Observation b5814b4d-5d41-40c8-8d22-a33f923bd6ae · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 27

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source=pdf_text observed=2026-08-08T00:50:44.591825Z digest=sha256:6cb6bb8d405a90034b810b05a2f9b6fa503d98ebb76a635ac9642b1e80562a6c

Observation beb74699-9790-45ae-b3d4-56ab7b2b0955 · outbound

This paper cites an unresolved cited work.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Unresolved cited work

Reference 28

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Observation b42d4592-8746-42b1-980f-312eecce96e2 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 29

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Observation 0ddc2cba-1726-4155-b14f-61355b4487ec · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

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Observation 0cf4641b-7ed5-4f40-9cc6-47cb3fdad7aa · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 31

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source=pdf_text observed=2026-08-08T00:50:44.610606Z digest=sha256:1f46bdd95fc78b491c57e3c00e8d926130f75b5636fe36d92ed1ab8a1973dbb6

Observation 64884a03-5a2f-426b-999f-51235b4e49d7 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 32

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source=pdf_text observed=2026-08-08T00:50:44.615501Z digest=sha256:63eddac3d4868f70ea450ee77eb837c42b2f34dd5d7ecc68b985a6077e6a1489

Observation 5eb8d857-a77b-43d0-86b0-7f6a84a40084 · outbound

This paper cites Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects.

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

Reference 33

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Pith citing papers

No inbound Pith citation observations are available.