Paper Citation Record · LEDGER

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 6 inbound Pith citation observations for arXiv:2502.05003.

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

pith.paper-citation-record.v1
2502.05003 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:44:48.501356Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:30:56.624151Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:47:26.479505Z

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy5
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0dea6fb7-6f1c-4d97-baa4-d79ff3f46d4b · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.413268Z

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Observation 80d6743b-4aef-40bf-a357-bf0981496b21 · outbound

This paper cites 2:4 INT4.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations 2:4 INT4

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:44:48.913395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c548efff-8269-4b41-bc52-41e19c002947 · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 9

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Observation 9523e6ea-2daf-4f1b-9db7-f829fd379ab6 · outbound

This paper cites Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

Reference 11

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Observation f92e385d-eb77-49e4-b2a4-190ea8f8a5c2 · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.429321Z

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Observation a16b1939-9604-4793-952f-8c496c0761c2 · outbound

This paper cites Scaling Laws for Sparsely-Connected Foundation Models.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Scaling Laws for Sparsely-Connected Foundation Models

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T20:44:48.799372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d695d4b7-448e-4b5b-b158-19580f9edf91 · outbound

This paper cites Training Compute-Optimal Large Language Models.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Training Compute-Optimal Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.435471Z

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Observation 08afdb74-a924-4455-b045-0f40949cdcbd · outbound

This paper cites Scaling Laws for Precision.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Scaling Laws for Precision

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.443920Z

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Observation 350f25d5-d1cb-4817-b654-3828407661dd · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations SpinQuant: LLM quantization with learned rotations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.446391Z

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Observation f2d0654e-83b0-4032-b08c-cef9abf3d41d · outbound

This paper cites Decoupled Weight Decay Regularization.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Decoupled Weight Decay Regularization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.449157Z

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Observation a59fb065-89e2-437c-af6e-5e24e6dc7a94 · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.451632Z

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Observation 66eb65f4-4be3-4c6e-bfd7-b0d519b51c49 · outbound

This paper cites Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.453946Z

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Observation 4a70ecdb-8adb-4db4-81be-139541be4bc7 · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.456296Z

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Observation c3954dc0-306c-49df-88c0-7239b2baa6a1 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.459696Z

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Observation dff64a9f-c8c9-43af-8a41-aa2162ef1ab7 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Gemma: Open Models Based on Gemini Research and Technology

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.462636Z

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Observation 5bcd912d-3982-4277-8c66-9eb0926352a6 · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.465717Z

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Observation 839dc7de-d203-4981-99cc-ca6522294d58 · outbound

This paper cites AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.468564Z

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Observation 93990542-faa2-4554-a353-12d707a862ce · outbound

This paper cites DRIVE: One-bit Distributed Mean Estimation.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations DRIVE: One-bit Distributed Mean Estimation

Reference 27

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T20:44:48.579892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a253c42e-ccc5-4d68-b101-ab3c32a42e5d · outbound

This paper cites EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-08T20:44:48.568245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64c202d1-28eb-4ec2-aa4f-93d83272c83c · outbound

This paper cites Attention Is All You Need.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Attention Is All You Need

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.477451Z

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Observation 81049eb2-1117-4bf9-957d-6cc2d03ddbf2 · outbound

This paper cites BitNet a4.8: 4-bit Activations for 1-bit LLMs.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations BitNet a4.8: 4-bit Activations for 1-bit LLMs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.480442Z

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Observation ebb6410c-ed18-40f4-99f7-9ef90f3ac0d7 · outbound

This paper cites Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.483210Z

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Observation 7e3f4934-d3b9-49bb-a126-fac795bbde37 · outbound

This paper cites Atom: Low-bit Quantization for Efficient and Accurate LLM Serving.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.486215Z

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Observation 8915f77f-e821-4bec-9446-077388c602d4 · outbound

This paper cites Additional “Trust” Details A.1.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Additional “Trust” Details A.1

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:44:48.921811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc96839b-ce38-46b7-97ae-88726710461f · outbound

This paper cites For the MLP block, it uses additional gate projection and SiLU (Elfwing et al.,.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations For the MLP block, it uses additional gate projection and SiLU (Elfwing et al.,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:44:48.905023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7d6a791-b9ac-41da-98dc-da57bcba8500 · outbound

This paper cites We kept the MLP intermediate dimension equal to8/3of the hidden size, padding it to 256 for increased kernel compatibility.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations We kept the MLP intermediate dimension equal to8/3of the hidden size, padding it to 256 for increased kernel compatibility

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:44:48.896776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b85d5c2-de78-4037-8979-b6ac8a8dfb81 · outbound

This paper cites As described in Section 4.3, we closely follow the fitting procedure of Hoffmann et al.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations As described in Section 4.3, we closely follow the fitting procedure of Hoffmann et al

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:44:48.887884Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ba3dadc1-6bc3-4d9c-b522-161ffcf6fcf3 · outbound

This paper cites URL https: //doi.org/10.1214/aoms/1177703732.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations URL https: //doi.org/10.1214/aoms/1177703732

Reference 1964

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.438519Z

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Observation a37a2b2e-be67-49ef-a3c2-5c34ce427a48 · outbound

This paper cites Alistarh, D., Grubic, D., Li, J., Tomioka, R., and V ojnovic, M.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Alistarh, D., Grubic, D., Li, J., Tomioka, R., and V ojnovic, M

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.397508Z

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Observation 68e5e098-dcdd-4809-ac59-e273aa97d9c6 · outbound

This paper cites QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.401788Z

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Observation 6bf7a098-5429-4188-9c42-389597c7e57b · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.416614Z

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Observation 9dcb243f-0123-40e6-9a26-a3a7641b6c2f · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 2019

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Observation fd732453-1172-4f8c-bc65-9408b451133c · outbound

This paper cites Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Reference 2021

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Observation 48ce5bd3-1114-4109-9ed5-56510e59bed9 · outbound

This paper cites Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis

Reference 2022

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Observation 9f495784-4a3c-4e22-a818-c54a27849091 · outbound

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

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.404965Z

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Observation 1434bf59-b639-4116-8fde-667080428ca7 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 2024

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Observation be29f8b8-dde7-43ba-8519-5dbca8c31da0 · outbound

This paper cites The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws

Reference 2025

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation dd72ea8d-4238-480b-89eb-25cb911e6a7b · inbound

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models cites this paper.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 2022

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Observation 57511f37-f5ec-4379-ae11-cbf1e0671b09 · inbound

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities cites this paper.

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 159

Resolution: unresolved; no resolver link, observed 2026-08-16T04:30:56.624151Z

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Observation 0d03675b-b4af-43a8-9b9a-58862f3451e3 · inbound

Scaling Law for Quantization-Aware Training cites this paper.

Scaling Law for Quantization-Aware Training QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:08.146988Z

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Observation 8d5d4d8a-3600-43c7-8af9-e07044b03515 · inbound

FP4 All the Way: Fully Quantized Training of LLMs cites this paper.

FP4 All the Way: Fully Quantized Training of LLMs QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:38.408806Z

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Observation 4c0ccf2a-2b84-4fdc-995f-53ff96de54c8 · inbound

Unified Scaling Laws for Compressed Representations cites this paper.

Unified Scaling Laws for Compressed Representations QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:09.909242Z

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Observation cd28a4c3-804b-470b-960f-b73fc20931a0 · inbound

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models cites this paper.

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:47:26.483109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.