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Paper Citation Record · LEDGER

Slimming Down LLMs Without Losing Their Minds

As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2506.10885.

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

pith.paper-citation-record.v1
2506.10885 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:43.250197Z

measured 16 of 16 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 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 exact1
  • verified fuzzy0
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b24f5cad-fbf4-4111-89a9-6f7730b111b9 · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Slimming Down LLMs Without Losing Their Minds Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:41.803051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:41.803051Z digest=sha256:b048f6178daa346232b8e07a5e64e48b5389f0d67d56a1782190159d8b7d3a1f

Observation 8481ca7f-1c56-474e-beac-5bb4e43d8cd7 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Slimming Down LLMs Without Losing Their Minds Training Verifiers to Solve Math Word Problems

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:41.870990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:41.870990Z digest=sha256:a96752c3ddf9b149668a96b4403e5122b818853debf41f43ddd8a46600863d4a

Observation f082671f-8362-49d4-9d86-5122c2bd84f8 · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

Slimming Down LLMs Without Losing Their Minds 8-bit Optimizers via Block-wise Quantization

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:41.968247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:41.968247Z digest=sha256:66d7228f1447972a56a8f0c10bd2259fdd2b6b6652786ac5aae748712f6b8e1a

Observation 85eb3b08-cf6a-41a2-a9c1-5b23d64cadb0 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Slimming Down LLMs Without Losing Their Minds QLoRA: Efficient Finetuning of Quantized LLMs

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.116500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.116500Z digest=sha256:aea7a27ed578e7f7490563cd282992890cd850a3f01a24a3fc7d1c1de3e84f7c

Observation b18180a0-ba87-4ed2-b240-0dedfd50b85f · outbound

This paper cites The case for 4-bit precision: k-bit Inference Scaling Laws.

Slimming Down LLMs Without Losing Their Minds The case for 4-bit precision: k-bit Inference Scaling Laws

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.212714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.212714Z digest=sha256:52d2ad55b01a21d442022a08e94c341b59437621861e76785668f0c24fb89e4c

Observation c3270593-c912-46f5-9d7f-db67ee0c7b6a · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Slimming Down LLMs Without Losing Their Minds BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.351229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.351229Z digest=sha256:18cac2ba956d07e450991fdf9358ebfde174f61a382b9b93d6bb582f8a920289

Observation a48c8875-4fb1-4088-ae1c-388f20cce368 · outbound

This paper cites an unresolved cited work.

Slimming Down LLMs Without Losing Their Minds Unresolved cited work

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.458557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.458557Z digest=sha256:611276b49833202e0823e58b83c420ec47450d1048bc0fbe770e84c75f748076

Observation 198e7b69-e019-4e8f-8490-854de160bbb5 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

Slimming Down LLMs Without Losing Their Minds Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.568333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.568333Z digest=sha256:e876d25e060ad8cfc6fe6fda182b107e7eda7b0cc799a577af68a9fd44fdbf11

Observation 50dda0ba-9406-4a17-be83-6b31ca54b75c · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Slimming Down LLMs Without Losing Their Minds Measuring Massive Multitask Language Understanding

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.654574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.654574Z digest=sha256:7bd4cdef26bce116597c7ed45f5f29f822d3b4e26c6e79677de88e6ae7d979d2

Observation f7f04f52-7546-4466-b74f-60d78df90262 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

Slimming Down LLMs Without Losing Their Minds Parameter-Efficient Transfer Learning for NLP

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.738755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.738755Z digest=sha256:61838dbfe91cbc6b2a9a75cdec46581a511337e7e541fd5f9104f8381b7fc1d5

Observation cacfa10c-456c-49f0-b649-efe5b3ba6a41 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Slimming Down LLMs Without Losing Their Minds LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.867009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.867009Z digest=sha256:0d964829169f0bfff8387d08578c2ff61b6e17b2d9abc435dd3610dade58f4c3

Observation db95a001-f2b9-4774-89a0-0376105d9b07 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Slimming Down LLMs Without Losing Their Minds Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:42.950788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:42.950788Z digest=sha256:37966eaa4e01150f0ca05c3e72b46811a824bdbf8a505e877b727a23210f6ff0

Observation 9023e8d4-3539-40df-9ad1-7e9a9f5087c2 · outbound

This paper cites an unresolved cited work.

Slimming Down LLMs Without Losing Their Minds Unresolved cited work

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:43.034025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:43.034025Z digest=sha256:1f8826a4dda65a436e6c36e3ca4cb9460ef3115ff69bd58abe1c6fa95af0b53f

Observation c54b3850-06c9-41b3-afbb-88dad1d87e6a · outbound

This paper cites Subakan, M.

Slimming Down LLMs Without Losing Their Minds Subakan, M

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:43.081727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:43.081727Z digest=sha256:a4c907a92c0dfb45835f36f3a9a672e4ac49bdb2dad605fbbf35294a35340e50

Observation 4a1246e9-eba9-4b8f-8bf7-4454324a8f5b · outbound

This paper cites Training Deep Neural Networks with 8-bit Floating Point Numbers.

Slimming Down LLMs Without Losing Their Minds Training Deep Neural Networks with 8-bit Floating Point Numbers

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:19:43.478897Z

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.

source=pdf_text observed=2026-08-07T04:19:43.192454Z digest=sha256:3266fbfb6640a766822854e1b22a16e016f496d4018cefc6f505ea37d7b7e9c8

Observation 120ade8d-53f7-48bf-a21f-96b4fb6d3375 · outbound

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

Slimming Down LLMs Without Losing Their Minds HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:43.250197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:43.250197Z digest=sha256:93a3e29e8c253ccf022be7b3b40ca0106e4a5ceadb3095868b69d1104c249f3e

Pith citing papers

No inbound Pith citation observations are available.