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Paper Citation Record · LEDGER

Machine Learning Model Sizes and the Parameter Gap

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2207.02852.

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

pith.paper-citation-record.v1
2207.02852 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:35.553102Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

28
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b20e4d52-1f6f-4090-8738-ebc454d67824 · inbound

Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts cites this paper.

Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts Machine Learning Model Sizes and the Parameter Gap

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-24T03:08:48.107865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T03:07:32.103513Z digest=sha256:170d59fbf68695af857640a8911685b2c7ed8e9eb10a524520571d8b9a3464a9

Observation 78df2362-877b-44f2-9782-b96169109b28 · inbound

Revisiting Weight Averaging for Model Merging cites this paper.

Revisiting Weight Averaging for Model Merging Machine Learning Model Sizes and the Parameter Gap

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T18:15:59.316304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:15:59.316304Z digest=sha256:4fff2de133a868fac5bfbad47f398009968aafa2cd7b0f0959c768e83a619fe2

Observation 92f82b58-cb24-46e8-a432-ba8f2b8cce63 · inbound

GeFL: Model-Agnostic Federated Learning with Generative Models cites this paper.

GeFL: Model-Agnostic Federated Learning with Generative Models Machine Learning Model Sizes and the Parameter Gap

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T04:45:42.239172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T04:45:42.239172Z digest=sha256:de7da2d67c932354c207a2fbb8c12d0eae700c2589ab297c5d5c417a25a353bf

Observation efec145c-f2d8-4a5b-9e57-5ce95568108f · inbound

Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation cites this paper.

Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation Machine Learning Model Sizes and the Parameter Gap

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-16T06:04:35.553102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T06:04:35.553102Z digest=sha256:d38d1a5f0bd98174de11a17ef5d7fe7704ff9f855af8f3cb18c37fad81d11342

Observation 08e07525-72d9-4f3f-a194-611aeefdff20 · inbound

RanDeS: Randomized Delta Superposition for Multi-Model Compression cites this paper.

RanDeS: Randomized Delta Superposition for Multi-Model Compression Machine Learning Model Sizes and the Parameter Gap

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T21:02:49.390544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:02:49.390544Z digest=sha256:3d20c598a75ce851714dbe9a2e6bceb17d6f48c332e4c1c941bdb46b5c9650b4

Observation 3d8088db-f111-460c-87a4-5ad98c9633b2 · inbound

On the Performance of Concept Probing: The Influence of the Data (Extended Version) cites this paper.

On the Performance of Concept Probing: The Influence of the Data (Extended Version) Machine Learning Model Sizes and the Parameter Gap

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T14:36:21.382520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:36:21.382520Z digest=sha256:61ec87664a4d97f23234ad1ac218eefd8c2a1c8fc9f59b653f5874595c747af5

Observation 587a39b9-0d67-4d60-9607-6212456b2ca9 · inbound

400-Gbps/$\lambda$ Ultrafast Silicon Microring Modulator for Scalable Optical Compute Interconnects cites this paper.

400-Gbps/$\lambda$ Ultrafast Silicon Microring Modulator for Scalable Optical Compute Interconnects Machine Learning Model Sizes and the Parameter Gap

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T12:30:43.875557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:30:43.875557Z digest=sha256:0014b85469323706d674d7a962ee20c9c006949eaad9dc0395b224ef14ab1145

Observation 8b3907bc-3c5a-47a9-a401-931da6594fc4 · inbound

Time-multiplexed layer reuse for physical neural networks cites this paper.

Time-multiplexed layer reuse for physical neural networks Machine Learning Model Sizes and the Parameter Gap

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-04T07:54:47.790951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T07:54:47.790951Z digest=sha256:a7fb4982e478c58e82c70f8a8ce1b1f66288648f25c5e5e32625cd531b3a3038

Observation 0aaace37-98ec-423f-a23a-e760fa69ff30 · inbound

Analyzing Reverse Address Translation Overheads in Multi-GPU Scale-Up Pods cites this paper.

Analyzing Reverse Address Translation Overheads in Multi-GPU Scale-Up Pods Machine Learning Model Sizes and the Parameter Gap

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:38:14.407175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-13T20:37:49.558450Z digest=sha256:7f50ae3833df8bb1430465623d2f5784ec1680dd75fef716f8b04b5c26c99231

Observation 3db68399-9138-47dc-835a-1159c35914ca · inbound

Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges cites this paper.

Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges Machine Learning Model Sizes and the Parameter Gap

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-13T23:13:24.925406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-13T23:08:56.507529Z digest=sha256:71dbab458d65170def0ffedc2d8aee3149883dba48b2431c030b90a847088bf9

Observation eb16774e-4073-4f8d-8d98-fd8822b35711 · inbound

Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering cites this paper.

Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering Machine Learning Model Sizes and the Parameter Gap

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:55:06.156015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-06-30T22:54:03.054871Z digest=sha256:cdd0fb0c6b9d68fb46d6dc5d643f3776db87ff60d12a21a541398e2b488e6a32

Observation b3580a0f-fb3e-4596-adb1-16a3567ec601 · inbound

Foundational values for foundation models cites this paper.

Foundational values for foundation models Machine Learning Model Sizes and the Parameter Gap

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-11T18:27:21.610124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:27:21.610124Z digest=sha256:256b467caa51b7a9ee71b6ffc88dc28995d41c389e4f9199583afdb2dc5c3170