Paper Citation Record · LEDGER

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades

As of 11 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:2501.10661.

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

pith.paper-citation-record.v1
2501.10661 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:20:24.179106Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:08:51.028457Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T10:36:30.614268Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy11
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ebcb5eeb-17b3-4730-926b-6564f41dcff2 · outbound

This paper cites A): We provide a more in-depth exploration of weight distribution.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades A): We provide a more in-depth exploration of weight distribution

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.379620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c94e2261-5ef3-4bf5-bea1-ba29fe037009 · outbound

This paper cites B): Here, we delve deeper into the discussion of W∗.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades B): Here, we delve deeper into the discussion of W∗

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.369668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation efac793e-1a31-4ccf-9571-3ac60d2cf1e7 · outbound

This paper cites C): We include all experimental details presented in the main text.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades C): We include all experimental details presented in the main text

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.359508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 060a3dfe-7994-41e1-91a7-4749600a396b · outbound

This paper cites D): Other supplementary information, such as algorithm tables, will also be provided.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades D): Other supplementary information, such as algorithm tables, will also be provided

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.348356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0fc33a51-5421-49d0-b17b-5f8526012213 · outbound

This paper cites E): We will introduce relevant literature related to our work.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades E): We will introduce relevant literature related to our work

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.337890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fe347c78-6452-44c4-a7c4-ab63358de471 · outbound

This paper cites We hope that the researchers could have a happy journey, and we hope that after reviewing the appendix, they will gain a deeper understanding of our entire work.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades We hope that the researchers could have a happy journey, and we hope that after reviewing the appendix, they will gain a deeper understanding of our entire work

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.326572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f2ca8dbd-c1ba-4fd0-84ae-9bd822dad967 · outbound

This paper cites Through observation, we know that both the pretrained weights and the fine-tuned weights follow a Gaussian distribution.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades Through observation, we know that both the pretrained weights and the fine-tuned weights follow a Gaussian distribution

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.314167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a78ab74a-0288-4d9e-a8cc-6a9eeb4b0e8c · outbound

This paper cites Here, we also validate ∆W obtained from LoRA-Dash and DoRA.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades Here, we also validate ∆W obtained from LoRA-Dash and DoRA

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.302769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 94ea4269-82fa-44b8-824d-c3822c15eb67 · outbound

This paper cites The results are shown in the Table.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades The results are shown in the Table

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.292190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c706e676-6abe-4f42-8d29-788fb63b80bb · outbound

This paper cites physical experiment.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades physical experiment

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.281997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation db420fbd-d1e4-4271-9400-8aced78e84fe · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 204

Resolution: unresolved; no resolver link, observed 2026-08-10T19:20:24.116704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85a81cd4-3ead-40de-8f05-9826301677fe · outbound

This paper cites Efficient Multimodal Learning from Data-centric Perspective.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades Efficient Multimodal Learning from Data-centric Perspective

Reference 645

Resolution: unresolved; no resolver link, observed 2026-08-10T19:20:24.129770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e40e559c-3a43-45ed-b396-ef5a9c6e507a · outbound

This paper cites How Far Are We From AGI: Are LLMs All We Need?.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades How Far Are We From AGI: Are LLMs All We Need?

Reference 1126

Resolution: unresolved; no resolver link, observed 2026-08-10T19:20:24.121621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e13b7140-a963-4d07-a75f-a7b25af50ccf · outbound

This paper cites However, existing studies have rarely validated or analyzed these observations in depth.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades However, existing studies have rarely validated or analyzed these observations in depth

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:20:24.270310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17d35519-037a-46f3-a99b-714dedb2e8c1 · outbound

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

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades LoRA: Low-Rank Adaptation of Large Language Models

Reference 2799

Resolution: unresolved; no resolver link, observed 2026-08-10T19:20:24.134478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f53c591c-03b7-4c73-8f93-d8e654020f49 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 3938

Resolution: unresolved; no resolver link, observed 2026-08-10T19:20:24.125768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 23e1a371-ea3a-4709-8929-2697c3635d03 · inbound

NAN: A Training-Free Solution to Coefficient Estimation in Model Merging cites this paper.

NAN: A Training-Free Solution to Coefficient Estimation in Model Merging Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:51.028457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 518a7dea-ea8c-4159-9ac0-ab01d2a16f08 · inbound

ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training cites this paper.

ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:36:30.616234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.