Paper Citation Record · LEDGER

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.10613.

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

pith.paper-citation-record.v1
2507.10613 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:56:44.153432Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

46 of 46 outbound references displayed

verified exact1
verified fuzzy0
unresolved45
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8066dc3e-8748-4e57-b3bc-5a27ea86a4ce · outbound

This paper cites Effective pruning of web-scale datasets based on complexity of concept clusters.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Effective pruning of web-scale datasets based on complexity of concept clusters

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.267479Z

Source-reported events for the cited work

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Observation bdd7c99d-7bb3-4ae9-83fc-0e71fea596a1 · outbound

This paper cites Explaining Neural Scaling Laws.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Explaining Neural Scaling Laws

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.295193Z

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Observation 351d8134-db2b-471b-b267-b6752fed9911 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 3

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Observation 152a6975-968c-4c9f-9029-0c41b9f7b163 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.966457Z

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Observation 1d3a22a1-49e8-45c1-85e7-701a2f4f4e21 · outbound

This paper cites From Mathematical Reasoning to Code: Generalization of Process Reward Models in Test-Time Scaling.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs From Mathematical Reasoning to Code: Generalization of Process Reward Models in Test-Time Scaling

Reference 5

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Observation df807588-e848-424b-aea1-695e37c467a3 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.951580Z

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Observation 35c29b43-c5f6-4f55-9149-03cf68dc9f58 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.938172Z

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Observation 85c3dc1e-d079-402c-b765-3c3b14f434f4 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.923507Z

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Observation 448ab41a-a3ae-464c-ac60-03ccacd6c880 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.909320Z

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Observation f0d5ca0c-fda0-43e2-b216-553a3e5a3e38 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.894632Z

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Observation 671c4fae-1622-4f4f-a9b0-1d9431ec75cf · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.581222Z

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Observation c3e4e2f2-b507-4f90-8038-a8dc4a2cce3a · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.880101Z

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Observation 79a6fd3c-a6e1-4997-928a-cc2aca852b83 · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Language models scale reliably with over-training and on downstream tasks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.636617Z

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Observation 2a7b2c32-31db-43f0-bc5e-d78026609ee6 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.866013Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a285115f-3246-4ba1-b8a4-261f5b57b4fd · outbound

This paper cites Scaling Laws for Neural Machine Translation.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Laws for Neural Machine Translation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.681631Z

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Observation 84e2df13-ac46-4c94-b74e-20128916c624 · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Laws for Autoregressive Generative Modeling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.717878Z

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Observation 21b9fa81-02da-45de-8a0d-93fba2742c65 · outbound

This paper cites Scaling Laws and Interpretability of Learning from Repeated Data.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Laws and Interpretability of Learning from Repeated Data

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.788633Z

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Observation f0d3a553-7835-494a-853c-c73fb912f5b0 · outbound

This paper cites Scaling Laws for Transfer.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Laws for Transfer

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.818581Z

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Observation 78d9b79f-b906-4925-8433-1e1d43685aa3 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Training Compute-Optimal Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.845916Z

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Observation 5f3f0ba6-7cfe-4708-99a7-d16e2a51c914 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.850720Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 91582163-4ee3-4840-a896-8387e86ebb33 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.895885Z

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Observation 15eff6fa-854f-4790-9053-31627e0d8b7d · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.932282Z

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Observation a8b9a84f-b53a-4a72-9cd8-66cec7ada771 · outbound

This paper cites Mistral 7B.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Mistral 7B

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:43.968079Z

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Observation 5ca80b30-e063-49c2-acce-b8069d921a4b · outbound

This paper cites Scaling Laws for Neural Language Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Laws for Neural Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.004588Z

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Observation e0a0890e-0db0-4ecb-89ed-4790428426f3 · outbound

This paper cites One Epoch Is All You Need.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs One Epoch Is All You Need

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.038172Z

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Observation 2e1d8f12-caa2-4425-b462-18b5a3111765 · outbound

This paper cites An Empirical Model of Large-Batch Training.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs An Empirical Model of Large-Batch Training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.050082Z

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Observation fc82bdec-2723-483e-8705-956fcc361219 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.835046Z

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Observation 29d6b882-e7eb-436e-84b6-841d96bc1aeb · outbound

This paper cites Scaling Data-Constrained Language Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Data-Constrained Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.060494Z

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Observation 3655e6a8-347f-4e19-8a15-b59acbea6277 · outbound

This paper cites Resolving Discrepancies in Compute-Optimal Scaling of Language Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Resolving Discrepancies in Compute-Optimal Scaling of Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.065789Z

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Observation 5166039b-3259-4516-85b1-28f1c0383c0e · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.070629Z

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Observation fa4531ca-52e6-4bc8-aa33-49cab38727ca · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.075710Z

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Observation 6d867eea-4249-4856-8592-b3db67339c98 · outbound

This paper cites How to Train Data-Efficient LLMs.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs How to Train Data-Efficient LLMs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.081602Z

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Observation a6a6043a-eade-40ba-ba08-eb963eda6965 · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.086979Z

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Observation 816b083e-38ea-41fe-9305-bc2590379163 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.818943Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 44ce0db8-2ae9-4c04-8a75-6737770e5e17 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Gemini: A Family of Highly Capable Multimodal Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.097451Z

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Observation dce5d44c-b8b7-4d83-a43f-0566f84a1f5f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.103874Z

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Observation e0f9fe75-eb3b-44bf-b357-643109eaecb1 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.804155Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 260463cc-8b0c-4f67-927b-6258f7fa70f3 · outbound

This paper cites Performance Law of Large Language Models.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Performance Law of Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.114496Z

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Observation 0bf44740-4cb7-4ffb-b7f7-efa1549f4af9 · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.789353Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2b99ccd6-39be-4acc-850b-10ac02ff53fc · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.774464Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2a75cae7-1cb6-490d-9bb0-590ba3184d7e · outbound

This paper cites an unresolved cited work.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:56:44.760480Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e25b524f-8505-45fa-a843-9645bfc4b403 · outbound

This paper cites The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.133665Z

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Observation dc378ec0-4c39-468d-b6b5-76884dd7a08f · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.138484Z

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Observation 107d6887-7ae2-4a9e-bec6-70f8aee2ef48 · outbound

This paper cites Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.143579Z

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Observation bc0ac68d-6cd9-4838-ab6d-b3524fa1b973 · outbound

This paper cites online" 'onlinestring :=.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs online" 'onlinestring :=

Reference 46

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Observation 8ffd23bd-c565-4894-8db8-af64d1db8ddb · outbound

This paper cites write newline.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs write newline

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.153432Z

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Pith citing papers

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