Paper Citation Record · LEDGER

Explaining Neural Scaling Laws

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2102.06701.

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

pith.paper-citation-record.v1
2102.06701 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:44:23.653048Z

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

32
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 9a0bba4a-1bdf-4ac3-8869-f851ef563fc6 · inbound

Scaling Laws for Reward Model Overoptimization cites this paper.

Scaling Laws for Reward Model Overoptimization Explaining Neural Scaling Laws

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T09:04:53.308887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c4561ad-67f3-4a19-88f3-6a272ebe3016 · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Explaining Neural Scaling Laws

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:35:21.548913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 201c8bd4-43fd-4822-8043-c19fc9e78edc · inbound

KAN: Kolmogorov-Arnold Networks cites this paper.

KAN: Kolmogorov-Arnold Networks Explaining Neural Scaling Laws

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:42:05.709105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b1c6e33c-c515-47c7-8242-dbacd385bc9a · inbound

The Platonic Representation Hypothesis cites this paper.

The Platonic Representation Hypothesis Explaining Neural Scaling Laws

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:03:56.793876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c97140ad-aaf8-41c2-a8d1-3c5a12baa32d · inbound

Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems cites this paper.

Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems Explaining Neural Scaling Laws

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T12:44:23.653048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9185a99d-3538-4eca-b96e-138a22de71c1 · inbound

Scaling Pre-training to One Hundred Billion Data for Vision Language Models cites this paper.

Scaling Pre-training to One Hundred Billion Data for Vision Language Models Explaining Neural Scaling Laws

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T12:12:30.637676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11090121-99b6-402d-80f1-2f4acc753923 · inbound

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law cites this paper.

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law Explaining Neural Scaling Laws

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:47.667736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 373e7a40-31f3-4bff-9395-9404b358661c · inbound

X-Factor: Quality Is a Dataset-Intrinsic Property cites this paper.

X-Factor: Quality Is a Dataset-Intrinsic Property Explaining Neural Scaling Laws

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:53.544999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75c57701-0f52-4ecd-832c-94b11c9398df · inbound

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis cites this paper.

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis Explaining Neural Scaling Laws

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T16:34:32.430145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4393baa4-451a-4bb1-9e56-ee8a2fef9a44 · inbound

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency cites this paper.

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency Explaining Neural Scaling Laws

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T11:25:09.522988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13b9de9a-095a-4407-a971-c82a4b12f1f1 · inbound

Spectral Edge Dynamics: An Analytical-Empirical Study of Phase Transitions in Neural Network Training cites this paper.

Spectral Edge Dynamics: An Analytical-Empirical Study of Phase Transitions in Neural Network Training Explaining Neural Scaling Laws

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:58.872914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b201bc90-ef91-462f-8f78-e99f3d08f930 · inbound

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches cites this paper.

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches Explaining Neural Scaling Laws

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:23:37.295413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67de2ac0-777c-4284-9b31-38080fb14c93 · inbound

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer cites this paper.

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer Explaining Neural Scaling Laws

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:05:53.450783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b371f87-83f1-4e3f-bb2f-77cc9f5468d9 · inbound

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer cites this paper.

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer Explaining Neural Scaling Laws

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:26:24.226885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2951a757-4299-4866-bb7d-78273db21879 · inbound

Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum cites this paper.

Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum Explaining Neural Scaling Laws

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:44:07.884532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19cdcbf1-a148-448c-ad21-a2bb20076662 · inbound

Asymmetric Scaling Laws from Sparse Features cites this paper.

Asymmetric Scaling Laws from Sparse Features Explaining Neural Scaling Laws

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:20:17.038286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ded477fb-e46d-4881-ab10-1252003f3a14 · inbound

Unified Neural Scaling Laws cites this paper.

Unified Neural Scaling Laws Explaining Neural Scaling Laws

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:44:02.780941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2d34c867-5db7-43e4-a928-f6835d33d607 · inbound

Comprehensive AI governance requires addressing non-model gains cites this paper.

Comprehensive AI governance requires addressing non-model gains Explaining Neural Scaling Laws

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T08:15:31.640980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6af25f02-69fd-413f-b8f5-a519da167d7b · inbound

How Optimality Structures Sparse Dictionaries: A Theory for Understanding SAE Representations cites this paper.

How Optimality Structures Sparse Dictionaries: A Theory for Understanding SAE Representations Explaining Neural Scaling Laws

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:26.188417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2ecfc6f6-cb2f-4518-8f23-c16a54e67fe0 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Explaining Neural Scaling Laws

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-26T15:39:33.184489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 37b67d1f-29a9-45eb-97b3-aa4d2fa4a050 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Explaining Neural Scaling Laws

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:57:25.353696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3a5d550-6042-42e6-82bd-20ce70f80907 · inbound

A Transport-Based Geometry of Belief-Cost cites this paper.

A Transport-Based Geometry of Belief-Cost Explaining Neural Scaling Laws

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T10:44:36.879000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ebdf280-f43e-408d-81bd-2dbec00b7e06 · inbound

Revisiting the Volume Hypothesis cites this paper.

Revisiting the Volume Hypothesis Explaining Neural Scaling Laws

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:35:40.841577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 450efecc-e8d3-4c78-8252-bca548f8c708 · inbound

Information-Theoretic Limits of Reliability and Scaling in Language Models cites this paper.

Information-Theoretic Limits of Reliability and Scaling in Language Models Explaining Neural Scaling Laws

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T14:45:34.779199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3db40943-a683-45c3-8b09-202a4cff703d · inbound

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development cites this paper.

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development Explaining Neural Scaling Laws

Reference 171

Resolution: unresolved; no resolver link, observed 2026-08-02T07:40:30.553049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.