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Paper Citation Record · LEDGER

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 2 inbound Pith citation observations for arXiv:2505.00347.

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

pith.paper-citation-record.v1
2505.00347 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:52:46.814686Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T14:24:48.140126Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T02:12:07.088792Z

Reference resolution

28 of 28 outbound references displayed

  • verified exact2
  • verified fuzzy3
  • unresolved21
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cae948a6-4b5f-40e3-a968-5b5c06ff15a2 · outbound

This paper cites 1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics 1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

Reference 6

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Observation 532ed55f-f5ff-479e-a655-d659d9420755 · outbound

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

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 7

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source=pdf_text observed=2026-08-16T04:52:46.702601Z digest=sha256:1bb55f0ff4ef799b83e9ffc03bf96524d3dbd942cadd8a0a2e8942f508de076d

Observation 853e43bd-b03f-4691-972e-18cc043c1c6b · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 8

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source=pdf_text observed=2026-08-16T04:52:46.708987Z digest=sha256:60d89b75e3cf602fea2b32efd806892eb89f71891a1dd6645b0d29087da61775

Observation 170f94c5-ff27-44ef-b3a6-687922f0dd34 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.715473Z digest=sha256:2058a70644a708c277211e74386b3ae590f2509b3539831d46ebf69917f5d0a8

Observation 5092d89c-8f1d-48e4-b418-f2fb9a3a27ac · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Measuring Massive Multitask Language Understanding

Reference 10

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-16T04:52:46.721492Z digest=sha256:feea1e11a2fa7cc72acfb81ba3e8284d8e6f2d241dc8daced9eb89fdcf7e8a5d

Observation 5bb5b762-1517-4a0f-a631-5c504e8fca34 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 11

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source=pdf_text observed=2026-08-16T04:52:46.727359Z digest=sha256:85fa3263fe430dfa869efa967ffad394739e1f7e02d0ac7f339281f18d23a4f5

Observation 0fbed652-8462-45e0-831d-27925edf7fe9 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-16T04:52:46.732784Z digest=sha256:ec7037cf90c2f12a622b8b3e4053359818290e562f76e9cc690f7f3b0dff0bff

Observation db0ae041-3b10-456a-8bbc-528170c62de2 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics SocialIQA: Commonsense Reasoning about Social Interactions

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.748663Z digest=sha256:1ecc0bacd54b2ba5f944e09d449e4051ebcc52abd778329a46a04693cc93335e

Observation b4c79ae8-759c-4401-86ad-c12e769cb8e8 · outbound

This paper cites 1-bit stochas- tic gradient descent and its application to data-parallel distributed training of speech dnns.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics 1-bit stochas- tic gradient descent and its application to data-parallel distributed training of speech dnns

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:52:47.446851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T04:52:46.754114Z digest=sha256:cc523066fa73534a9d2370e8d7fcf28d904d5d5f6daabe91aa39e0cdc616c09c

Observation 1370d299-e68b-485b-af0b-0909f1d6e74f · outbound

This paper cites MKQ-BERT: Quantized BERT with 4-bits Weights and Activations.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics MKQ-BERT: Quantized BERT with 4-bits Weights and Activations

Reference 17

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verified exact
local_arxiv, observed 2026-08-16T04:52:47.082749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ae54ceba-aec4-41f7-a0d8-93b4d01c9502 · outbound

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

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics LLaMA: Open and Efficient Foundation Language Models

Reference 18

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source=pdf_text observed=2026-08-16T04:52:46.763984Z digest=sha256:8bcf2c1ba765ab40f316bc01607adcc8b0bb4805e5ae68c335220c0931bd64b2

Observation 0a86100d-a918-454c-a256-531a47e0f342 · outbound

This paper cites On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond

Reference 19

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no resolver link, observed 2026-08-16T04:52:46.769204Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.769204Z digest=sha256:d27f117810768f442745e3b3c716d3ad0c5e951dd7d07e22d06061d7070b447b

Observation e2b65753-fd2d-475c-891b-3d23ebe14856 · outbound

This paper cites Improved stochastic rounding.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Improved stochastic rounding

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:52:47.020740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4fc2736c-6d20-46f9-af9e-c8d132fe8383 · outbound

This paper cites Qwen2.5 Technical Report.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Qwen2.5 Technical Report

Reference 21

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no resolver link, observed 2026-08-16T04:52:46.780098Z

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source=pdf_text observed=2026-08-16T04:52:46.780098Z digest=sha256:8a2d45b492477ad631cae242c5d3a892a69c7343c54d04613d85fe82f9ce9b61

Observation 96a8ce4f-39fb-451e-a1ed-9275c09f4546 · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Adam-mini: Use Fewer Learning Rates To Gain More

Reference 22

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source=pdf_text observed=2026-08-16T04:52:46.784783Z digest=sha256:cbbee9dd77e4fb4701ca1439c8073266ed09c81da321f6d74181441bd12031fa

Observation 5bc35c0b-a35d-4bc1-9d1d-f7340af0eab9 · outbound

This paper cites an unresolved cited work.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T04:52:46.789900Z digest=sha256:d3d8790e60d139e311168c27eb2eedf76e0e183ff4bce097f3cb2b87d1484be1

Observation a67a1d42-0936-4b3b-945f-e443d8f613f2 · outbound

This paper cites c−1 c , (βc)k+k′+1, w.p.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics c−1 c , (βc)k+k′+1, w.p

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-16T04:52:47.429397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 8999db14-f84d-4783-8537-7d400863604d · outbound

This paper cites LLaMA-7B/13B/33B ( ) on Alpaca.LLM fine-tuning is one of the most compelling applications, but still faces challenges due to its high memory demands.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics LLaMA-7B/13B/33B ( ) on Alpaca.LLM fine-tuning is one of the most compelling applications, but still faces challenges due to its high memory demands

Reference 25

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 6d87c815-19a0-4389-b25f-6e7b6d89fd0b · outbound

This paper cites an unresolved cited work.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T04:52:46.804691Z digest=sha256:9d6ba72b254d6616be0f71c33ec5bf8b9f5ef6b80eca1fd939f1a9a382ca26f0

Observation 651f1c08-1e6b-4327-b5e1-43e7ed16f6c6 · outbound

This paper cites an unresolved cited work.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation cf508b40-16fb-48b1-b37e-1f5b03f79713 · outbound

This paper cites an unresolved cited work.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 77d10cd9-47a9-4332-a10a-a5463a6020e4 · outbound

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

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 2018

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Observation 540142ae-5eee-4d19-976c-b544eb46653b · outbound

This paper cites PaLM 2 Technical Report.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics PaLM 2 Technical Report

Reference 2019

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Observation 6e1f25e4-bf06-450c-80a7-921a2b1a09d2 · outbound

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

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2020

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source=pdf_text observed=2026-08-16T04:52:46.685017Z digest=sha256:68cc0f7b983a88062a9591673e31115329e29bb9cf3dc46db813685bdc6a6b99

Observation cf843508-30ea-4acc-ac65-4b55ba6edc17 · outbound

This paper cites Scaling Neural Machine Translation.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Scaling Neural Machine Translation

Reference 2021

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.743488Z digest=sha256:e1f6f775532eb97a72038661ff401caae9a51beebe82518b26ffb098978f4553

Observation b4982b9b-40c6-4bed-8fae-9b366328381b · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 2022

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source=pdf_text observed=2026-08-16T04:52:46.690536Z digest=sha256:8bc96a6fedcb94d125e17eb5c3c094eb29e98bbb8a966cf701de62ee3ced0195

Observation c1faef5e-c554-4e4f-a0c0-4b739b8b7590 · outbound

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

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2023

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no resolver link, observed 2026-08-16T04:52:46.679047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.679047Z digest=sha256:c8eb7237ee09934cebd7433216b1228ee948b6ee41b78a9cc4a30130d1147576

Observation cdbd06d6-0bee-481f-8e41-3c7d6800a7fa · outbound

This paper cites Qwen Technical Report.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics Qwen Technical Report

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.673459Z digest=sha256:4fcb32c74f5b166cdd5e48e4cf19fdb7176b708cf51bb0804ded9a993440a3c5

Pith citing papers

Observation dc10dd01-4b5a-404b-94d5-bbeb9fbcc5ad · inbound

MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization cites this paper.

MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics

Reference 13

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verified exact
arxiv_id, observed 2026-05-13T02:12:07.090702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-13T02:11:43.022143Z digest=sha256:35d70647f1ea43af648dbe2baaba3a6bee57e1882d2f34d40daba18ef4b0907c

Observation 7d450edd-aace-4582-8d5f-92db86c86ee3 · inbound

MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization cites this paper.

MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics

Reference 2026

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no resolver link, observed 2026-08-02T14:24:48.140126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:24:48.140126Z digest=sha256:23ad55067ef60ceeb65179e3a8c0552de087a724a7612ba83769f9bca5b09c40