Paper Citation Record · LEDGER

MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2205.00119.

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

pith.paper-citation-record.v1
2205.00119 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:40:13.736781Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T21:03:58.748181Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4cc059b8-8ee4-44a6-8ce8-f7be90e52046 · inbound

BloombergGPT: A Large Language Model for Finance cites this paper.

BloombergGPT: A Large Language Model for Finance MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 141

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T23:19:46.598666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ace95df2-273a-4228-8001-907658cae18c · inbound

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel cites this paper.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:15:20.102935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ae1087a2-d68d-443f-ae1d-f5168a54f64e · inbound

The Falcon Series of Open Language Models cites this paper.

The Falcon Series of Open Language Models MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:46:09.892891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb6af78d-5f49-40b9-a22b-54d9e8126298 · inbound

DiLoCoX: A Low-Communication Large-Scale Training Framework for Decentralized Cluster cites this paper.

DiLoCoX: A Low-Communication Large-Scale Training Framework for Decentralized Cluster MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:13.736781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21400e00-7801-467b-b69a-c3240e8fa5b8 · inbound

CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training cites this paper.

CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:41.189739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ec76a35-bdb2-4da5-908e-b281d8097fd2 · inbound

Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training cites this paper.

Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T21:03:58.749953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.