Paper Citation Record · LEDGER

Profiling Apple Silicon Performance for ML Training

As of 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 2 inbound Pith citation observations for arXiv:2501.14925.

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

pith.paper-citation-record.v1
2501.14925 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:49:55.605979Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T07:14:52.640891Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:08:21.714092Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy10
unresolved9
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20b5bd49-f4bb-4a06-b7ff-2bdc0774f6de · outbound

This paper cites https://pytorch .org/get- started/previous-versions/, 2024.

Profiling Apple Silicon Performance for ML Training https://pytorch .org/get- started/previous-versions/, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.886525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f1e797e-2630-495b-bd1c-7c0a7a4102db · outbound

This paper cites Tyers, and Gregor Weber.

Profiling Apple Silicon Performance for ML Training Tyers, and Gregor Weber

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.527417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac3d2a42-7999-4310-97c0-7263eeaac80d · outbound

This paper cites Nvidia Marketplace.

Profiling Apple Silicon Performance for ML Training Nvidia Marketplace

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.862788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d1ff587b-1f34-4bb2-b5cb-c954727956c1 · outbound

This paper cites MLX: Efficient and flexible machine learning on apple silicon, 2023.

Profiling Apple Silicon Performance for ML Training MLX: Efficient and flexible machine learning on apple silicon, 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.834560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a145c97b-68c2-46e3-9707-e55dda07b9fa · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Profiling Apple Silicon Performance for ML Training Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.820416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 690bce1f-c8a5-4c94-bfb6-318a2abbcccd · outbound

This paper cites Apple unleashes m1.

Profiling Apple Silicon Performance for ML Training Apple unleashes m1

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.805850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a5ce8a1-d360-47c1-ac18-17d590a3446b · outbound

This paper cites Apple introduces m2 ultra.

Profiling Apple Silicon Performance for ML Training Apple introduces m2 ultra

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.791380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3e0ea113-080b-40bf-9edf-f6f01f592af4 · outbound

This paper cites Apple unveils m2 pro and m2 max: next-generation chips for next-level workflows.

Profiling Apple Silicon Performance for ML Training Apple unveils m2 pro and m2 max: next-generation chips for next-level workflows

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.775869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6f202eb9-4ea4-4cb6-a81f-eafa3d57b4b8 · outbound

This paper cites Accelerated pytorch training on mac.

Profiling Apple Silicon Performance for ML Training Accelerated pytorch training on mac

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.761499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b8b6335-00d0-42a7-b8b3-784391b025de · outbound

This paper cites Enabling massive deep neural networks with the graphblas.

Profiling Apple Silicon Performance for ML Training Enabling massive deep neural networks with the graphblas

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.747019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 161f7211-98d8-4bb3-bd25-ab75ffe42c31 · outbound

This paper cites an unresolved cited work.

Profiling Apple Silicon Performance for ML Training Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:49:55.731131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bd5de3a6-e8a8-4c9e-98fe-bf33650a659c · outbound

This paper cites Maas, Raymond E.

Profiling Apple Silicon Performance for ML Training Maas, Raymond E

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:49:55.716336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 724e12cd-8c7a-48a2-88ce-c40112115b8d · outbound

This paper cites Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz.

Profiling Apple Silicon Performance for ML Training Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.580403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 201d0557-e95d-4827-aeef-b59e1b6054e5 · outbound

This paper cites Mixed precision training, 2018.

Profiling Apple Silicon Performance for ML Training Mixed precision training, 2018

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.584606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32a94e51-607c-42ff-8a59-9bd688dd324c · outbound

This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

Profiling Apple Silicon Performance for ML Training Robust Speech Recognition via Large-Scale Weak Supervision

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.588901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c7d98ac-fefa-4660-a398-15bddb16dc31 · outbound

This paper cites Language models are unsupervised multitask learners.

Profiling Apple Silicon Performance for ML Training Language models are unsupervised multitask learners

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.593482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 317bf093-bca7-44b4-8430-65e3454dccdb · outbound

This paper cites {Zero-offload}: Democratizing {billion-scale} model training.

Profiling Apple Silicon Performance for ML Training {Zero-offload}: Democratizing {billion-scale} model training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.597524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec88e5de-404c-46e8-8d4c-22a176391ebc · outbound

This paper cites Attention is all you need.

Profiling Apple Silicon Performance for ML Training Attention is all you need

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:55.601672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b618728-6b18-44f6-99f5-3216978d6891 · outbound

This paper cites an unresolved cited work.

Profiling Apple Silicon Performance for ML Training Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:49:55.656340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 57f259d5-eae1-4ba4-9641-a682ba148061 · outbound

This paper cites an unresolved cited work.

Profiling Apple Silicon Performance for ML Training Unresolved cited work

Reference 2024

Resolution: parse uncertain; raw_fallback, observed 2026-08-10T14:49:55.848821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation a0c04688-99d0-4e94-82bb-f3f92669a043 · inbound

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results cites this paper.

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results Profiling Apple Silicon Performance for ML Training

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:22:28.119257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0657c250-0c67-4d75-b932-44fa0653cb11 · inbound

Rigel: Reverse-Engineering the Metal 4.1 Tensor Compute Path on the Apple M4 Max GPU cites this paper.

Rigel: Reverse-Engineering the Metal 4.1 Tensor Compute Path on the Apple M4 Max GPU Profiling Apple Silicon Performance for ML Training

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:08:21.715943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.