Paper Citation Record · LEDGER

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models

As of 12 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2501.16650.

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

pith.paper-citation-record.v1
2501.16650 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:47:24.980984Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T21:48:48.992712Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T14:26:03.981445Z

Reference resolution

41 of 41 outbound references displayed

verified exact2
verified fuzzy11
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 05dc27ee-b118-424f-a936-014a0281c8c8 · outbound

This paper cites Representation Topology Divergence: A Method for Comparing Neural Network Representations.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Representation Topology Divergence: A Method for Comparing Neural Network Representations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T11:47:24.789560Z

Source-reported events for the cited work

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Observation 16b0b76c-3dd6-466b-90f2-f884eb98425a · outbound

This paper cites Define: • X ∈ Rn×m as X = [e1, e2,.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Define: • X ∈ Rn×m as X = [e1, e2,

Reference 2

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Observation af0f8470-2e8d-4d00-8085-ea81dd4b6b9f · outbound

This paper cites The Llama 3 Herd of Models.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models The Llama 3 Herd of Models

Reference 5

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Source-reported events for the cited work

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Observation d5fb136d-9d71-40ac-bf3a-c11c482c1e98 · outbound

This paper cites Training Compute-Optimal Large Language Models.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Training Compute-Optimal Large Language Models

Reference 7

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Source-reported events for the cited work

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Observation 0b8f7962-3c3a-42c1-bd10-c6a89c6361f1 · outbound

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Unresolved cited work

Reference 8

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Observation 6f03a463-269e-4898-be0b-47fa11a3778d · outbound

This paper cites Mixtral of Experts.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Mixtral of Experts

Reference 9

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Source-reported events for the cited work

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Observation fe4e2a0f-733d-4ae0-b424-d832c1395dae · outbound

This paper cites an unresolved cited work.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Unresolved cited work

Reference 11

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Observation 2bd6e2b6-f581-4244-bbbd-fa3d2513e788 · outbound

This paper cites Similarity of Neural Network Models: A Survey of Functional and Representational Measures.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Similarity of Neural Network Models: A Survey of Functional and Representational Measures

Reference 12

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Observation dd5e5149-a33e-4bda-8c96-0951ce195283 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 14

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Observation 87eff07f-a74a-4e6e-afa0-0866962e880c · outbound

This paper cites Beyond KV Caching: Shared Attention for Efficient LLMs.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Beyond KV Caching: Shared Attention for Efficient LLMs

Reference 16

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Observation de712690-3514-4dd2-aa11-0747dd2bdc1b · outbound

This paper cites Training language models to follow instructions with human feedback.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Training language models to follow instructions with human feedback

Reference 18

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Observation 49953636-8b04-4da7-a906-40a9c50a41a3 · outbound

This paper cites SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Reference 21

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Source-reported events for the cited work

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Observation 742f666a-d0fa-43df-bfe7-96b0b3c99ad2 · outbound

This paper cites Similarity of Neural Networks with Gradients.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Similarity of Neural Networks with Gradients

Reference 22

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Observation d43e7ff9-9acc-41a8-9deb-dc256d17c6b5 · outbound

This paper cites JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention

Reference 23

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Source-reported events for the cited work

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Observation e6288e51-2e02-473e-b75b-86b6f3a86d00 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 24

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Source-reported events for the cited work

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Observation 6fba8e80-86cd-4fbb-8070-64940cbb7e2c · outbound

This paper cites Similarity Analysis of Contextual Word Representation Models.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Similarity Analysis of Contextual Word Representation Models

Reference 25

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Observation 59f177f1-2a59-4317-9d92-c4ffb39e0fe2 · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models GLM-130B: An Open Bilingual Pre-trained Model

Reference 26

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Observation f0d1d419-7a09-40d2-a230-f7be7e0e84a0 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 27

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Source-reported events for the cited work

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Observation 4e66e345-a351-4244-8588-af7abb3a6c7e · outbound

This paper cites CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

Reference 28

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Source-reported events for the cited work

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Observation 82837663-8add-443f-a6c0-3baf4e2fdac8 · outbound

This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 29

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Source-reported events for the cited work

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Observation 4a9ab880-d151-46f8-b573-141b76c9f3ac · outbound

This paper cites Let X, Y∈ Rn×m and let PX , PY ∈ Rm×m be permutation matrices.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Let X, Y∈ Rn×m and let PX , PY ∈ Rm×m be permutation matrices

Reference 30

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Source-reported events for the cited work

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Observation 909b7bc1-4c15-4b30-a5b1-f77afa32dbe9 · outbound

This paper cites −0.6676 0 .5171 −0.5357 −0.7310 −0.5917 0 .3399 −0.1412 0 .6185 0 .7730 # , Y =.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models −0.6676 0 .5171 −0.5357 −0.7310 −0.5917 0 .3399 −0.1412 0 .6185 0 .7730 # , Y =

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0cd29245-4d5f-40a1-a919-0e4fa3d0ccf7 · outbound

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Unresolved cited work

Reference 32

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Observation 82a0312c-b81d-41ba-b9e4-2206007b2ecc · outbound

This paper cites Meanwhile, TX and TY denote truncated identity matrices that retain the left singular vectors, ensuring that the accumulated variance meets a predefined limit.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Meanwhile, TX and TY denote truncated identity matrices that retain the left singular vectors, ensuring that the accumulated variance meets a predefined limit

Reference 33

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44cb9398-508f-492b-bc0d-891e6435ab4d · outbound

This paper cites Meanwhile, TX and TY denote truncated identity matrices that retain the left singular vectors, ensuring that the accumulated variance meets a predefined limit.

Reference 34

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Observation b61dc0a0-6574-43be-b91c-8f9d3dbf5ba2 · outbound

This paper cites ∥X − Y ∥F = √ 2m = Ω(√n).

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models ∥X − Y ∥F = √ 2m = Ω(√n)

Reference 37

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Observation 988ec9d1-67a7-463f-999f-558e392f1c38 · outbound

This paper cites This formulation indicates that a larger Off-Diagonal Average Cosine Similarity value corresponds to a lower degree of orthogonality in the matrix.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models This formulation indicates that a larger Off-Diagonal Average Cosine Similarity value corresponds to a lower degree of orthogonality in the matrix

Reference 39

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Observation 32006ad6-cb52-4cc2-8ffb-0f74e8a5f83b · outbound

This paper cites To quantify this, we define the similarity ratio as the ratio of the similarity scores between models (A) and (B) to those between models (A) and (C).

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models To quantify this, we define the similarity ratio as the ratio of the similarity scores between models (A) and (B) to those between models (A) and (C)

Reference 40

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Observation 633553c8-72a7-4ec2-bef5-ccbf2e0222bd · outbound

This paper cites Importance of the Maximization Function The maximization operation in the M AXCOSSIM function plays a crucial role in the DOCS algo- rithm.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Importance of the Maximization Function The maximization operation in the M AXCOSSIM function plays a crucial role in the DOCS algo- rithm

Reference 41

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 1984

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

Reference 2005

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models The Remarkable Robustness of LLMs: Stages of Inference?

Reference 2008

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models StableMoE: Stable Routing Strategy for Mixture of Experts

Reference 2016

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Contrasim–analyzing neural representations based on con- trastive learning

Reference 2017

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Cross-layer attention sharing for large language models

Reference 2018

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 2019

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models FLM-101B: An Open LLM and How to Train It with $100K Budget

Reference 2020

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 2021

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Language models are few-shot learners

Reference 2022

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Scaling Laws for Neural Language Models

Reference 2023

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DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment

Reference 2024

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Pith citing papers

Observation d68b3489-039f-49cc-b201-54ad94a0c68d · inbound

Low-Rank Adaptation Redux for Large Models cites this paper.

Low-Rank Adaptation Redux for Large Models DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models

Reference 136

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