Paper Citation Record · LEDGER

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks

As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2507.02119.

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

pith.paper-citation-record.v1
2507.02119 v2

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:48:55.792391Z

measured 57 of 57 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T06:58:38.927268Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T07:00:43.255075Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation 1f8507b7-d662-4d9a-a6f9-a37c495b3814 · outbound

This paper cites GPT-4 Technical Report.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks GPT-4 Technical Report

Reference 1

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Observation ff16d3b2-6874-4029-bc2a-03c5d1a16f45 · outbound

This paper cites and Fisher, D.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Fisher, D

Reference 2

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Observation d210d09d-bd03-4756-84ab-8877e7587d6b · outbound

This paper cites High dimensional analysis reveals conservative sharpening and a stochastic edge of stability.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks High dimensional analysis reveals conservative sharpening and a stochastic edge of stability

Reference 3

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Observation 803ba54a-c036-4b78-a89f-9aacfaba5bff · outbound

This paper cites Power lines: Scaling laws for weight decay and batch size in llm pre-training.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Power lines: Scaling laws for weight decay and batch size in llm pre-training

Reference 4

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This paper cites Finite size scaling analysis of ising model block distribution functions.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Finite size scaling analysis of ising model block distribution functions

Reference 5

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Observation 00ddcf25-616c-4d98-97c1-562523c70530 · outbound

This paper cites and Pehlevan, C.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Pehlevan, C

Reference 6

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Observation 7c5eee10-f120-4418-be25-ed004d944912 · outbound

This paper cites Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit

Reference 7

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Observation 34cf55a9-2ec1-4ee3-979d-31fb7e50ae16 · outbound

This paper cites A Dynamical Model of Neural Scaling Laws.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks A Dynamical Model of Neural Scaling Laws

Reference 8

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Observation 14832c06-88b9-461a-8b12-2d859f17a77f · outbound

This paper cites How Feature Learning Can Improve Neural Scaling Laws.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks How Feature Learning Can Improve Neural Scaling Laws

Reference 9

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This paper cites Infinite limits of multi-head transformer dynamics.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Infinite limits of multi-head transformer dynamics

Reference 10

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Observation b2a49a53-e9c1-4dc0-bdee-154cb355f9c6 · outbound

This paper cites Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability

Reference 11

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Observation 4d05b138-7864-4521-a851-7c0cf4d60ba8 · outbound

This paper cites Adaptive Gradient Methods at the Edge of Stability.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Adaptive Gradient Methods at the Edge of Stability

Reference 12

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This paper cites M., Damian, A., Talwalkar, A., Kolter, Z., and Lee, J.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks M., Damian, A., Talwalkar, A., Kolter, Z., and Lee, J

Reference 13

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This paper cites Optimal learning rate schedules in high-dimensional non-convex optimization problems.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Optimal learning rate schedules in high-dimensional non-convex optimization problems

Reference 14

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This paper cites C., Noci, L., Li, M., Bordelon, B., Bergsma, S., Pehlevan, C., Hanin, B., and Hestness, J.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks C., Noci, L., Li, M., Bordelon, B., Bergsma, S., Pehlevan, C., Hanin, B., and Hestness, J

Reference 15

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Observation ad86be4a-4417-4076-9c0f-e4185cd2952a · outbound

This paper cites Kingma, J.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Kingma, J

Reference 16

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Observation 0231a66f-5898-4deb-a78f-3976de64ab0d · outbound

This paper cites Scaling Exponents Across Parameterizations and Optimizers.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Scaling Exponents Across Parameterizations and Optimizers

Reference 17

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Observation a27cd3c4-c959-4c6c-ade8-6651aebda303 · outbound

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Unresolved cited work

Reference 18

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Observation 9b472174-aad9-4b29-a6f6-280c995a9936 · outbound

This paper cites Monte Carlo methods in financial engineering, volume 53.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Monte Carlo methods in financial engineering, volume 53

Reference 19

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Observation 894f4596-0b96-4b61-a659-2679eddf39c2 · outbound

This paper cites and Fisher, D.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Fisher, D

Reference 20

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Observation 01e15fe3-4ed2-492b-8957-bad8c1daf31e · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Gaussian Error Linear Units (GELUs)

Reference 21

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Observation 4e936d7a-dbe8-4e16-ba21-53b8d78e44a6 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Deep Learning Scaling is Predictable, Empirically

Reference 22

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Observation a53d9abd-f9f5-46f0-9a61-462a98c5354a · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Training Compute-Optimal Large Language Models

Reference 23

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This paper cites Scaling Laws for Neural Language Models.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Scaling Laws for Neural Language Models

Reference 24

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This paper cites F., Blundell, J.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks F., Blundell, J

Reference 25

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Observation 285b1657-f9f6-4e76-9a21-83e8c78a68f3 · outbound

This paper cites Stochastic modified equations and adaptive stochastic gradient algorithms.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Stochastic modified equations and adaptive stochastic gradient algorithms

Reference 26

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Observation 1541ff55-00bc-4e67-beb4-0ec9ee48de10 · outbound

This paper cites A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules

Reference 27

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks On the sdes and scaling rules for adaptive gradient algorithms

Reference 28

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Pastur, L

Reference 29

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Observation 3593f90c-ab38-4294-a77a-dbf7cb00324e · outbound

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

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks An Empirical Model of Large-Batch Training

Reference 30

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This paper cites Y., Singh, S., Bhatele, A., Goldblum, M., Panda, A., and Goldstein, T.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Y., Singh, S., Bhatele, A., Goldblum, M., Panda, A., and Goldstein, T

Reference 31

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This paper cites The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers

Reference 32

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Super consistency of neural network landscapes and learning rate transfer

Reference 33

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Observation f53c09ca-dfd3-44cb-a19f-41b65fc5e507 · outbound

This paper cites Sgd in the large: Average-case analysis, asymptotics, and stepsize criticality.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Sgd in the large: Average-case analysis, asymptotics, and stepsize criticality

Reference 34

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This paper cites Homogenization of sgd in high-dimensions: Exact dynamics and generalization properties.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Homogenization of sgd in high-dimensions: Exact dynamics and generalization properties

Reference 35

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Observation 17e6d158-514a-450d-ad6c-12a4ecc631b6 · outbound

This paper cites 4+3 Phases of Compute-Optimal Neural Scaling Laws.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks 4+3 Phases of Compute-Optimal Neural Scaling Laws

Reference 36

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks T., Agarwala, A., and Fisher, D

Reference 37

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Observation 5be144fc-b5ef-41b9-bb18-d0db1e2f5d94 · outbound

This paper cites Reconciling Kaplan and Chinchilla Scaling Laws.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Reconciling Kaplan and Chinchilla Scaling Laws

Reference 38

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This paper cites J., Davison, M., Bhaya, D., and Fisher, D.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks J., Davison, M., Bhaya, D., and Fisher, D

Reference 39

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Observation 524ba4f5-5eea-4805-bd48-9394a5ddabe6 · outbound

This paper cites The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training

Reference 40

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This paper cites Ubiquitous abundance distribution of non-dominant plankton across the global ocean.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Ubiquitous abundance distribution of non-dominant plankton across the global ocean

Reference 41

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Kaplan, J

Reference 42

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This paper cites Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks

Reference 43

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This paper cites Scaling Law with Learning Rate Annealing.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Scaling Law with Learning Rate Annealing

Reference 44

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks R., Geiler-Samerotte, K., H \'e rissant, L., Blundell, J

Reference 45

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Observation dbb367e9-4461-4699-9c01-b99aa9f5d739 · outbound

This paper cites Feature-learning networks are consistent across widths at realistic scales.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Feature-learning networks are consistent across widths at realistic scales

Reference 46

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Observation 16c13459-0ce0-4da9-8d2f-dab0399daa4c · outbound

This paper cites How to set AdamW's weight decay as you scale model and dataset size.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks How to set AdamW's weight decay as you scale model and dataset size

Reference 47

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This paper cites Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective

Reference 48

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Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Unresolved cited work

Reference 49

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Observation e268f4b4-f837-49ba-a19f-7c5fdae47cf3 · outbound

This paper cites Small-scale proxies for large-scale Transformer training instabilities.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Small-scale proxies for large-scale Transformer training instabilities

Reference 50

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This paper cites Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 51

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This paper cites and Hu, E.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Hu, E

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:57.192764Z

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Observation 6907316e-e6d2-48a5-97e1-881565c4a1ec · outbound

This paper cites and Littwin, E.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Littwin, E

Reference 53

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Observation b05f31e8-fb3c-44e1-b053-f8a9f689ea30 · outbound

This paper cites J., Babuschkin, I., Sidor, S., Liu, X., Farhi, D., Ryder, N., Pachocki, J., Chen, W., and Gao, J.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks J., Babuschkin, I., Sidor, S., Liu, X., Farhi, D., Ryder, N., Pachocki, J., Chen, W., and Gao, J

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:48:56.863551Z

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Observation d6b96640-4afb-48c5-a528-1ee0a2da6836 · outbound

This paper cites and Sennrich, R.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks and Sennrich, R

Reference 55

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This paper cites an unresolved cited work.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T20:48:55.792391Z

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Pith citing papers

Observation 41fd3f8c-d9fe-418e-b9d8-5b79dd298a3f · inbound

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model cites this paper.

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:00:43.256809Z

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