Paper Citation Record · LEDGER

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2506.17615.

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

pith.paper-citation-record.v1
2506.17615 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:09:04.281671Z

measured 35 of 35 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-14T01:54:00.951348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T01:58:36.890401Z

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy7
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b53eda37-c546-4203-bd08-e987f8cdfcf4 · outbound

This paper cites Program Synthesis with Large Language Models.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Program Synthesis with Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.154517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22a4a6e9-cb6d-4913-a106-4d21311985a7 · outbound

This paper cites Language models are few-shot learners,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Language models are few-shot learners,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.160214Z

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Observation 749bfed9-3319-43e2-8136-4d461fab22e3 · outbound

This paper cites Collective Communication: Theory, Practice, and Experience: Research Articles,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Collective Communication: Theory, Practice, and Experience: Research Articles,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.873578Z

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 31f80bde-f650-4e2e-8681-b6bf5dec3479 · outbound

This paper cites Evaluating large language models trained on code,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Evaluating large language models trained on code,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.169281Z

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Unavailable: canonical work link unavailable.

Observation 41abfb4e-bf6b-4050-b0da-2944db66f400 · outbound

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

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.173061Z

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Observation ae32f4a8-bc2e-4d51-969b-521b7a835975 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.177197Z

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Observation 4eea3a0c-52f2-4c25-a724-d898a4ebfb01 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.181092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d71965f4-92cc-4487-beed-afde324320ac · outbound

This paper cites Measuring Massive Multitask Language Understanding.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Measuring Massive Multitask Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.184843Z

Source-reported events for the cited work

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Observation b82e863d-e02d-4f37-b63d-ae5c96e12798 · outbound

This paper cites gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.188465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30cd6e37-c4ff-43dc-bc9b-c9808ca19484 · outbound

This paper cites Huang, Y.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Huang, Y

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.858864Z

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 c2db6612-1075-4a93-99d3-de30f675b2fc · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.849164Z

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 e66e0e0c-ef95-4af8-9292-d7ce9213bf0d · outbound

This paper cites Optimal Bucket Algorithms for Large MPI Collectives on Torus Interconnects,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Optimal Bucket Algorithms for Large MPI Collectives on Torus Interconnects,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.203609Z

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Observation 34a39546-06f4-4b14-86c0-434248fb569c · outbound

This paper cites SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.207388Z

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Observation 82fc37ba-94cb-474a-987e-4a247f9a8bc0 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration What disease does this patient have? a large-scale open domain question answering dataset from medical exams,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.211375Z

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Observation ae2a760a-1129-4985-9857-803d73c444a1 · outbound

This paper cites In-Datacenter Performance Analysis of a Tensor Processing Unit,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration In-Datacenter Performance Analysis of a Tensor Processing Unit,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.215195Z

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Unavailable: canonical work link unavailable.

Observation 0af57a6b-61ce-4317-88a7-d448e85b134b · outbound

This paper cites Triviaqa: A large scale distantly supervised challenge dataset for reading comprehen- sion,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Triviaqa: A large scale distantly supervised challenge dataset for reading comprehen- sion,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.832123Z

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 bc0882c8-0074-4cb4-865f-cf62c91dffc3 · outbound

This paper cites TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.821379Z

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 136095f1-2e4a-4352-aa2d-0cb9160ca027 · outbound

This paper cites A domain-specific Supercomputer for Training Deep Neural Networks,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration A domain-specific Supercomputer for Training Deep Neural Networks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.230776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f7059c1-249a-403e-9cc2-2d17e4c86d1d · outbound

This paper cites Scaling Laws for Neural Language Models.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Scaling Laws for Neural Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.234328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff91739-f361-4dfb-932f-8dfa26521116 · outbound

This paper cites Evaluating Modern GPU Interconnect: PCIe, NVLink, NV- SLI, NVSwitch and GPUDirect,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Evaluating Modern GPU Interconnect: PCIe, NVLink, NV- SLI, NVSwitch and GPUDirect,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.809224Z

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 b51e2b6b-34a0-43a0-814b-b321b25b0bdd · outbound

This paper cites Quantized Distributed Training of Large Models with Convergence Guarantees,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Quantized Distributed Training of Large Models with Convergence Guarantees,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:09:04.799567Z

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 76d50b81-7fe6-48bd-9553-6c5f5a62125f · outbound

This paper cites PipeDream: Generalized Pipeline Parallelism for DNN Training,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration PipeDream: Generalized Pipeline Parallelism for DNN Training,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.244547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8330db3e-096e-44b8-bfdf-25074bb9cfe1 · outbound

This paper cites Efficiently Scaling Transformer Inference.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Efficiently Scaling Transformer Inference

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.247876Z

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Unavailable: canonical work link unavailable.

Observation aa642454-ec22-48ac-95b4-adc097cdd4d2 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Gpqa: A graduate-level google-proof q&a benchmark,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.251759Z

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Unavailable: canonical work link unavailable.

Observation 728b0238-2525-4f83-aa89-fb672bb0cfad · outbound

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

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Winogrande: An adversarial winograd schema challenge at scale,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.255744Z

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Unavailable: canonical work link unavailable.

Observation d46c7ff7-52fd-481c-9fee-a93296abed57 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.259494Z

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Observation d1f068c2-8706-4225-8f70-3c468b6fe8ec · outbound

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EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:09:04.777757Z

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 6e1679a7-ef32-46d9-8b44-e6ff60859365 · outbound

This paper cites Attention is all you need,.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Attention is all you need,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.266540Z

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Unavailable: canonical work link unavailable.

Observation 18bcd5da-146a-4850-8d13-ca2ae02146f9 · outbound

This paper cites ZeRO++: Extremely Efficient Collective Communication for Giant Model Training.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration ZeRO++: Extremely Efficient Collective Communication for Giant Model Training

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.269873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cae3b080-fca8-4087-896e-e26bcd878abd · outbound

This paper cites Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.273376Z

Source-reported events for the cited work

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Observation cd2146e5-76fe-400c-97df-2cd708186ce3 · outbound

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

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.278075Z

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Unavailable: canonical work link unavailable.

Observation aadeb1d5-8c85-45ca-b4f5-08ad8ebee611 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.281671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fae42f52-15f7-4c2b-9d4e-d255aa586713 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.198844Z

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Unavailable: canonical work link unavailable.

Observation af20717f-dd64-48e5-97e9-6e67a8059ee1 · outbound

This paper cites Available: https://doi.org/10.1145/3579371.3589350.

EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration Available: https://doi.org/10.1145/3579371.3589350

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:04.226984Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation f56c51a1-6677-4445-9a92-2d5cccc8bbd6 · inbound

A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network cites this paper.

A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:58:36.893224Z

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.