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Paper Citation Record · LEDGER

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.15689.

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

pith.paper-citation-record.v1
2506.15689 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:06:26.892775Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

35 of 35 outbound references displayed

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  • verified fuzzy2
  • unresolved31
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation c26b6598-1de8-4ada-8468-c7e4cb088ac7 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 1

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Observation 05a417ef-fd3b-4b5f-87a9-a6aea89baf01 · outbound

This paper cites Qwen Technical Report.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Qwen Technical Report

Reference 2

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source=pdf_text observed=2026-08-07T14:06:23.340470Z digest=sha256:6251ef3852097827117fd898b6024c8692ef59a7dcc23c4b51dc96a21159166f

Observation 14e4d958-dd5c-4e81-9604-b5df1725345f · outbound

This paper cites The Llama 3 Herd of Models.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models The Llama 3 Herd of Models

Reference 3

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Observation 255e687c-dcf6-44f7-8eee-ae557b2dd2f9 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 4

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source=pdf_text observed=2026-08-07T14:06:23.594746Z digest=sha256:54ca091b1cfc3f8f99fd109a3191bea013b689f7c67f1152f6a3477a4fdd7412

Observation c77f5654-34e3-4405-b46a-3c48140c66c3 · outbound

This paper cites DeepSeek-V3 Technical Report.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models DeepSeek-V3 Technical Report

Reference 5

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source=pdf_text observed=2026-08-07T14:06:23.658205Z digest=sha256:2d44e0789626bc0623ccef06cb15dc800c57a7e228510c3763509751c25cde9a

Observation ee549646-d103-4bea-8b68-47d41e8179a3 · outbound

This paper cites Up or Down? Adaptive Rounding for Post-Training Quantization.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Up or Down? Adaptive Rounding for Post-Training Quantization

Reference 6

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source=pdf_text observed=2026-08-07T14:06:23.724826Z digest=sha256:404587c8eccaa334681da156a6dca1198df517d22509aeeaccbd9df26c4b0edf

Observation 877e906a-cbc8-48e9-bb07-2fa16961d5d4 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 7

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source=pdf_text observed=2026-08-07T14:06:23.802464Z digest=sha256:accdc1d62cc4323247879f850145a25a7e6c17aea18c470ac37db3ecc2c78102

Observation 6d94a65f-318a-44c2-bcc7-43a679a065f5 · outbound

This paper cites Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers

Reference 8

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:06:23.861983Z digest=sha256:7f2f7ea880c8e337cff1e909f7d021baace26e05e59ee432237104df95e2d319

Observation c5994444-902e-43be-8f25-944b878c1048 · outbound

This paper cites FBQuant: FeedBack Quantization for Large Language Models.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models FBQuant: FeedBack Quantization for Large Language Models

Reference 9

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source=pdf_text observed=2026-08-07T14:06:23.949517Z digest=sha256:3c690d3b07bfb5a82b40583792f08788106f322a4f09c93f2fee10535532a364

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

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

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

Reference 10

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

Observation 322b8f75-4cb1-4daa-aa9a-b2684b503bac · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 11

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source=pdf_text observed=2026-08-07T14:06:24.154265Z digest=sha256:8ea538fe0e04ed6e8f433a6f944571216ff2f9c6b6d91c3e0c81f3f0da556c12

Observation bd38d449-b875-4550-8691-70ccca586eb4 · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 12

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source=pdf_text observed=2026-08-07T14:06:24.279700Z digest=sha256:6d517f3b778cfda9df13f792a9a306b42f98d7792a1f2092e6835028d4c050cd

Observation 47f24cf5-f99d-477e-bda5-0f8bcad15e1c · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 13

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source=pdf_text observed=2026-08-07T14:06:24.394539Z digest=sha256:6aa69dc61262c4cb164bb77ea9162cec25b154825d9c4610293c83ba7eac58db

Observation 63ce508e-bbf5-45fe-867e-4b47695ddeca · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models SpinQuant: LLM quantization with learned rotations

Reference 14

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source=pdf_text observed=2026-08-07T14:06:24.448912Z digest=sha256:bebe8d1b282a603c93a1008347f5045a46156d08a1c825eac50ea9dcd6aae25a

Observation 6d56403d-f536-4b30-b255-95fc7fe68239 · outbound

This paper cites OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting

Reference 15

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source=pdf_text observed=2026-08-07T14:06:24.512952Z digest=sha256:bb1b005f3e8ed801438ac50324d013a23d0563ad895d87a4ffb75413999798e0

Observation db2a2f74-26e0-42fe-9bd5-1a29b77d24f2 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 16

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source=pdf_text observed=2026-08-07T14:06:24.569811Z digest=sha256:54d171d03d85001a17cfeae5b632327b1903ef98edcbaf9d86cd6dda9350bce3

Observation 13303272-2f8c-4228-ab0f-54d8b80b88b8 · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 17

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source=pdf_text observed=2026-08-07T14:06:24.651918Z digest=sha256:dbd5cb2f8ebb97666beb024574fdafce483ddd3fde6a396a0fee8824a3af116c

Observation fa214855-69a4-4aa3-82e5-215be9e068c3 · outbound

This paper cites AffineQuant: Affine Transformation Quantization for Large Language Models.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models AffineQuant: Affine Transformation Quantization for Large Language Models

Reference 18

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source=pdf_text observed=2026-08-07T14:06:24.737750Z digest=sha256:fccc6e27c281aaf253963ac56effbba374db8997901185d396e6919fd73c5f2e

Observation f87cbbb8-73fe-4fe3-a03e-d36621456d68 · outbound

This paper cites Quip: 2-bit quantiza- tion of large language models with guarantees.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Quip: 2-bit quantiza- tion of large language models with guarantees

Reference 19

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:06:24.798384Z digest=sha256:2d6c758cc8a9acba0310dc68ed00670182f5ec0378ed5b313d863a5dea60a52d

Observation 76718352-586f-4a20-830d-36091d474f31 · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 20

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source=pdf_text observed=2026-08-07T14:06:24.880486Z digest=sha256:320d6f060498b44c3089de950adbd4ed2629c609c78d5a0d6d4415064c6d94db

Observation 9cc3d3d4-1efb-4d42-98ee-49973a2542a7 · outbound

This paper cites Duquant: Distributing outliers via dual transformation makes stronger quantized llms.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Duquant: Distributing outliers via dual transformation makes stronger quantized llms

Reference 21

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source=pdf_text observed=2026-08-07T14:06:25.040563Z digest=sha256:2b7fb3dc4206631510f06e3da74943c6731f3a5d074b7d3f7469ae0b7de8ab12

Observation f7019349-af58-4c26-82d7-f4579f20ef7b · outbound

This paper cites FlatQuant: Flatness Matters for LLM Quantization.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models FlatQuant: Flatness Matters for LLM Quantization

Reference 22

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source=pdf_text observed=2026-08-07T14:06:25.044991Z digest=sha256:9bec8f567a617a1fdb3228bc132fca1f005f836c20b5aadb455ec0b0d987a6e3

Observation 6b97d8ae-1689-4b07-a2e4-70ab795811b6 · outbound

This paper cites Qwen2.5 Technical Report.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Qwen2.5 Technical Report

Reference 23

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source=pdf_text observed=2026-08-07T14:06:25.171531Z digest=sha256:5e7633bc27314c934d32aa08349d07ffca5fd23c0e5e3a54f9d7126cb743eb25

Observation 8a89b250-4652-47dd-96e8-1cea268d5ce7 · outbound

This paper cites Pointer Sentinel Mixture Models.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Pointer Sentinel Mixture Models

Reference 24

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source=pdf_text observed=2026-08-07T14:06:25.309380Z digest=sha256:6c69a26504846d6246f4547f6bd0c5e5db42516debb7b5bcea1cf4d1e5cf872a

Observation a5e21f50-4d94-4b74-918a-920a38331dd1 · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 25

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source=pdf_text observed=2026-08-07T14:06:25.480561Z digest=sha256:fddfc5d9b5df1385b05f70cfd74f730c69a11dfc3b4645112387e11a6e5a8b31

Observation 001e0c7b-6999-4c98-bdfb-5ef3be6e97f2 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 26

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source=pdf_text observed=2026-08-07T14:06:25.696290Z digest=sha256:dedfb3551911331c9fbc289389cad496858c03cc04a583e855134786f8dd2c46

Observation 7cd97b0b-3595-4b10-8ba1-b470dfd6eb62 · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 27

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source=pdf_text observed=2026-08-07T14:06:25.812447Z digest=sha256:73f1111ef35e0d8780589b5fa65b55314942c127f0ebbdec695a2289320cdc13

Observation d0c8a70a-e737-49a4-8cc8-b9e5dfcbdc42 · outbound

This paper cites Language models are unsupervised multitask learners.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Language models are unsupervised multitask learners

Reference 28

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source=pdf_text observed=2026-08-07T14:06:25.954271Z digest=sha256:6fd8ad56a964d9e0c11b8083916ac3eaec7d5bedf3a21cae9e85f6560c75454e

Observation 49a511ed-2fbd-45cd-aad5-98edf09d33cc · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 29

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source=pdf_text observed=2026-08-07T14:06:26.090941Z digest=sha256:c360ca9978603668cc667a00ff8e62b1244c6d16529346d610bed0b0deb1e1a5

Observation b28cd0b4-4ea0-41df-a379-6ed83b610544 · outbound

This paper cites Piqa: Reasoning about phys- ical commonsense in natural language.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Piqa: Reasoning about phys- ical commonsense in natural language

Reference 30

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source=pdf_text observed=2026-08-07T14:06:26.218349Z digest=sha256:7f2c2ff0fcd7c09ab3e5036916d875490a38f2b93572dd5afc5f6c3d8f51c6b5

Observation 25259a9f-ab99-41c5-b64c-56f87f578427 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models SocialIQA: Commonsense Reasoning about Social Interactions

Reference 31

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source=pdf_text observed=2026-08-07T14:06:26.330099Z digest=sha256:b3f58854be546bff216f474aa7e0c68300b24eee1bc091de98da7c67b832dfd9

Observation 6619db19-55fd-4d76-a959-9b6d2575756c · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models Winogrande: An adversarial winograd schema challenge at scale

Reference 32

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source=pdf_text observed=2026-08-07T14:06:26.454824Z digest=sha256:571706eda9f68f8c227e9b47d5c0333aaaab1d228d7accc8ff77b526efb8e9a7

Observation 16bbe977-c947-4ea2-88f1-a446af282dcd · outbound

This paper cites The language model evaluation harness, 07 2024.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models The language model evaluation harness, 07 2024

Reference 33

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source=pdf_text observed=2026-08-07T14:06:26.592387Z digest=sha256:5d2da1b426f14ca111c3f4eca0b3f414fa3dfa2f19885a227dbc74e65303a7f6

Observation 5efa2cdb-d0dc-438b-9bd0-b1468289d2fb · outbound

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

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 34

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source=pdf_text observed=2026-08-07T14:06:26.709166Z digest=sha256:017f7c9951388d365bbe864a641d0a772ac6d0cc5f05cc0aaacf8507ae5e3fb9

Observation 2dc92a52-3d22-4aba-ab42-925513558bac · outbound

This paper cites fast-hadamard-transform, 2023.

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models fast-hadamard-transform, 2023

Reference 35

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:06:26.892775Z digest=sha256:ba79ecb209821cb83c3019fb91508254cfb4d4b5d8f2f756210edae13833e25d

Pith citing papers

No inbound Pith citation observations are available.