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Paper Citation Record · LEDGER

mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.02515.

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

pith.paper-citation-record.v1
2312.02515 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:56:44.642060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T22:05:06.235727Z

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 4d0020c2-97dd-473c-8d48-98b920280f02 · inbound

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data cites this paper.

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-05T12:56:44.642060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:56:44.642060Z digest=sha256:3bd2b62f4859e65eec0a0386fe7f95887d1009e22d25f13871167a64ff9177e0

Observation 96a50d0b-9980-49b6-bd14-a1d4055fce3b · inbound

Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation cites this paper.

Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-03T23:27:28.175841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T23:27:28.175841Z digest=sha256:477c647be4979cfef80d79c67031f534a96f4a02d6365b86e4293972a0d287df

Observation 4537bcdf-7d36-4531-a93a-67bf7aa87db3 · inbound

Epistemic Uncertainty for Test-Time Discovery cites this paper.

Epistemic Uncertainty for Test-Time Discovery mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T01:57:06.010184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T01:52:41.192353Z digest=sha256:7d6bd085112943cc1fc131d0c0b4589384f743d4fe52bea7d408769b3b1ce97e

Observation e18fe9fc-0f1b-4051-a049-c28552023d64 · inbound

MinT: Managed Infrastructure for Training and Serving Millions of LLMs cites this paper.

MinT: Managed Infrastructure for Training and Serving Millions of LLMs mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:27:51.856248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T19:25:12.407148Z digest=sha256:aecf269b24636f72bfc1ca51b8f1e329f399d0cfe2c1ebad2962c41219489327

Observation 203ac591-74c3-4105-9bf2-2a83e0b4db5f · inbound

MinT: Managed Infrastructure for Training and Serving Millions of LLMs cites this paper.

MinT: Managed Infrastructure for Training and Serving Millions of LLMs mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:05:06.237423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T21:47:00.295144Z digest=sha256:d3f70587ddbaca34794524fac2fb50836ceb8abe44c0c2112cce265d3d82871d