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Paper Citation Record · LEDGER

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

As of 14 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.01806.

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

pith.paper-citation-record.v1
2507.01806 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:53:30.040384Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e524735-6763-44de-85c8-3fc4b97d08e1 · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:32.071309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.269447Z digest=sha256:1ae7b4ef75194835e96476966b516b3358ca77d205d0bcdc9bba039161dc3bb6

Observation 0bb92516-8d41-4492-83d5-c5268134d127 · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:31.853600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.350974Z digest=sha256:c0dedaeb7af4991a02325b4aff4dc5b9d66f8e82bfd97d82fc6c94ca83556143

Observation 2a76d948-1307-4ca7-b022-c0dc7e660e54 · outbound

This paper cites Table 2 reports the time elapsed at each stage of our LoRA generation pipeline, measured on a Dell XPS15 (Intel i7-13700H, 14 cores, 64 GB RAM).

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Table 2 reports the time elapsed at each stage of our LoRA generation pipeline, measured on a Dell XPS15 (Intel i7-13700H, 14 cores, 64 GB RAM)

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:31.617369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.429079Z digest=sha256:12dc72f430341daebe982ef16e73bef191c05d85b55e26e67fe21f49f156908f

Observation 3ec061e2-af84-449a-8e14-65fd3695a15d · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:30.620387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.873779Z digest=sha256:c1101c0e997f1d8f3130b544a22c833f0a7cdfd026173534045d73640f4c3cfa

Observation 74803aef-1d6b-4b9d-8ed7-cceae4d72263 · outbound

This paper cites Expected Answer:.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Expected Answer:

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:30.366741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:30.040384Z digest=sha256:5f219f189631a1094be22788dbfb0cf2fd49605b07d36215def3e603fe69f358

Observation 5426550e-568e-484e-bcc5-132c9ecf6edb · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:32.661214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.001816Z digest=sha256:da9c20b6b89f4621f4dc8e9a8d5a5cefefc99aba50ccd3865ea67e37d7914456

Observation 3f1efa76-fa9b-43c9-ad2e-8aa4959e660e · outbound

This paper cites URL https: //doi.org/10.1007/978-3-030-56402-5.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs URL https: //doi.org/10.1007/978-3-030-56402-5

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:29.093799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:29.093799Z digest=sha256:9f4337b1b60279fba1d58b558e496ad21760403f094017baef0e1056fdbf0042

Observation 2c4ae8f2-ff1e-4c9e-b206-6e191f34e009 · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:31.334895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.522326Z digest=sha256:174f951932afc39e51ef8454becf0f1ae602b8b5e6957b92eaefeb97e575f72d

Observation 4f97af6a-aced-4721-99b9-8dd869d1cd25 · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:31.069823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.598399Z digest=sha256:5067a410187dd18f4d43c9f1a181b7bebd9ef1e1dff8eef6f959781cc2a7ff7b

Observation eec41203-6178-449d-bdc8-513b22a1982b · outbound

This paper cites an unresolved cited work.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:53:30.851551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.759056Z digest=sha256:1c404b3d5e7adb4bc2c298a2248284a945200ee5dd9884dcf215d1d90d0e7dba

Observation a9e8ba4a-892b-4025-ac4f-ef500716d49d · outbound

This paper cites A hitchhiker’s guide.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs A hitchhiker’s guide

Reference 2006

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:28.894826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:28.894826Z digest=sha256:8de2e6584a27c60d74ac5439e2ad0cc67e97f8302c6e92aa62205a41a95926aa

Observation 67e36b4f-4bfb-4796-94a9-3423ad939ee1 · outbound

This paper cites Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:28.445295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:28.445295Z digest=sha256:5c3b2c440c80a27abf9b1ed2ee6097fa425541be0437c7babfe49cc5dd9fc4ec

Observation d4af1f95-1c90-4b2e-8194-888929e98ca8 · outbound

This paper cites copy” of S in “distance domain.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs copy” of S in “distance domain

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:32.364385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:29.194089Z digest=sha256:05e914a78d0824f767799ecdfa993b434c2c339268082640bac1c8b5a9eefd0b

Observation 5d781889-c3e9-47a4-ad91-8cd4cc408c68 · outbound

This paper cites Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vuli´c, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vuli´c, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:32.928779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:53:28.342804Z digest=sha256:991517050bb8c0b0ec7889f2e92c932c72551d4399a76b6862ff8e7f3828c5fd

Observation 461fffb6-3e5d-4d2f-91ee-61c23dff2ad3 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs High-Resolution Image Synthesis with Latent Diffusion Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:28.617124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:28.617124Z digest=sha256:d9a48cd3fcd0fb4bb2cd92fff5c639b4f62360ac6991f2239383b8b2826208a3

Observation 7e44a120-e246-4620-b9dd-d876daa9179c · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs QLoRA: Efficient Finetuning of Quantized LLMs

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:28.773451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:28.773451Z digest=sha256:23b8159f3337922e2a48553ddfb78bb8d78e3030c3d0412db97183c7ddd296a0

Pith citing papers

No inbound Pith citation observations are available.