Paper Citation Record · LEDGER

Deriving Neural Scaling Laws from the statistics of natural language

As of 10 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 21 inbound Pith citation observations for arXiv:2602.07488.

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

pith.paper-citation-record.v1
2602.07488 v3

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:40:04.173879Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T14:45:36.168352Z

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

6 of 6 outbound references displayed

verified exact1
verified fuzzy0
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8a71536c-89a8-4af2-96e6-34553ebd0335 · outbound

This paper cites URL https: //proceedings.neurips.cc/paper_files /paper/2024/file/1dccfc3ee01871d05e3 3457c61037d59-Paper-Conference.pdf.

Deriving Neural Scaling Laws from the statistics of natural language URL https: //proceedings.neurips.cc/paper_files /paper/2024/file/1dccfc3ee01871d05e3 3457c61037d59-Paper-Conference.pdf

Reference 4

Resolution: verified exact; doi, observed 2026-08-03T03:43:33.440178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7160e199-45aa-48c3-a9de-28282ebc1ae0 · outbound

This paper cites an unresolved cited work.

Deriving Neural Scaling Laws from the statistics of natural language Unresolved cited work

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T03:40:04.173879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 476b2924-8eda-4b34-8fd6-bcd00ef32e7f · outbound

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

Deriving Neural Scaling Laws from the statistics of natural language Resolving Discrepancies in Compute-Optimal Scaling of Language Models

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T03:40:04.086576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f985a028-cf01-403a-910b-960d2ecd1aa8 · outbound

This paper cites Manning, C.

Deriving Neural Scaling Laws from the statistics of natural language Manning, C

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T03:40:03.779909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95402ec0-47ec-4740-a79e-a9db564d975c · outbound

This paper cites cc/paper_files/paper/2023/file/5b634 6a05a537d4cdb2f50323452a9fe-Paper-Con ference.pdf.

Deriving Neural Scaling Laws from the statistics of natural language cc/paper_files/paper/2023/file/5b634 6a05a537d4cdb2f50323452a9fe-Paper-Con ference.pdf

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T03:40:03.906048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3456c987-85d2-4eba-9519-7f4fd08b5d7e · outbound

This paper cites URL https: //www.pnas.org/doi/abs/10.1073/pnas.

Deriving Neural Scaling Laws from the statistics of natural language URL https: //www.pnas.org/doi/abs/10.1073/pnas

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T03:40:03.668143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7dd4028a-6f26-4526-8abe-7587b2e79dac · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Deriving Neural Scaling Laws from the statistics of natural language

Reference 254

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 153153d4-2ebf-4492-8120-e2d746b23011 · inbound

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data cites this paper.

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data Deriving Neural Scaling Laws from the statistics of natural language

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9a21648d-e2df-4efb-942e-eb48d63319e0 · inbound

Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning cites this paper.

Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning Deriving Neural Scaling Laws from the statistics of natural language

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7390913-4d3a-4736-b932-19042dc65860 · inbound

Asymmetric Scaling Laws from Sparse Features cites this paper.

Asymmetric Scaling Laws from Sparse Features Deriving Neural Scaling Laws from the statistics of natural language

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc1e90ea-a293-4355-a75f-b6cbdb6ddf98 · inbound

Sampling Data with Chains of Forward-Backward Diffusion Steps cites this paper.

Sampling Data with Chains of Forward-Backward Diffusion Steps Deriving Neural Scaling Laws from the statistics of natural language

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 393fdf00-08c9-4733-90d1-ef237ec2654f · inbound

Learn from your own latents and not from tokens: A sample-complexity theory cites this paper.

Learn from your own latents and not from tokens: A sample-complexity theory Deriving Neural Scaling Laws from the statistics of natural language

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b3b288c8-fc12-425f-8a6f-a6b9bcaac0c4 · inbound

Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention cites this paper.

Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention Deriving Neural Scaling Laws from the statistics of natural language

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5234e77b-b33d-41ea-92ad-59fa761fa66e · inbound

Scaling Laws for Neural-Network Quantum States cites this paper.

Scaling Laws for Neural-Network Quantum States Deriving Neural Scaling Laws from the statistics of natural language

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 187d14a2-82f9-4a0c-b021-f78e0c458ba7 · inbound

Neuron Populations Exhibit Divergent Selectivity with Scale cites this paper.

Neuron Populations Exhibit Divergent Selectivity with Scale Deriving Neural Scaling Laws from the statistics of natural language

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a928779-96cf-4b1c-8ec9-4aac20273c29 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Deriving Neural Scaling Laws from the statistics of natural language

Reference 155

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5617a8f9-407e-48b5-8282-9e11b7d5dff9 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Deriving Neural Scaling Laws from the statistics of natural language

Reference 155

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca3f5dc9-4a05-46d7-97fb-705cbe1d63cf · inbound

Critical Percolation as a Synthetic Data Model for Interpretability cites this paper.

Critical Percolation as a Synthetic Data Model for Interpretability Deriving Neural Scaling Laws from the statistics of natural language

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95e7d35b-3580-4837-9542-293d4854b481 · inbound

Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients cites this paper.

Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients Deriving Neural Scaling Laws from the statistics of natural language

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c010da8-4140-453a-ac71-14905de7c45f · inbound

Phase structure of the Random Language Model cites this paper.

Phase structure of the Random Language Model Deriving Neural Scaling Laws from the statistics of natural language

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 651200d8-345d-4f57-aab8-d4e705501bab · inbound

How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks cites this paper.

How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks Deriving Neural Scaling Laws from the statistics of natural language

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f09b06d-59db-4f63-bb2c-9e772d46c991 · inbound

Smooth Scaling Laws Hide Stepwise Token Learning cites this paper.

Smooth Scaling Laws Hide Stepwise Token Learning Deriving Neural Scaling Laws from the statistics of natural language

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-07T01:16:05.364568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e4e808fa-fca0-431a-8fb4-0867cef0dcd9 · inbound

Smooth Scaling Laws Hide Stepwise Token Learning cites this paper.

Smooth Scaling Laws Hide Stepwise Token Learning Deriving Neural Scaling Laws from the statistics of natural language

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-13T07:15:03.029543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9027beb0-8f55-438b-891a-45a51e1ba3d0 · inbound

Geometry of Ordinal Representations in Language Models cites this paper.

Geometry of Ordinal Representations in Language Models Deriving Neural Scaling Laws from the statistics of natural language

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-11T21:13:38.798627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a64115e4-25a2-4b85-9e3a-cf419b233f66 · inbound

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

Information-Theoretic Limits of Reliability and Scaling in Language Models Deriving Neural Scaling Laws from the statistics of natural language

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T14:45:36.168352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16a4e1ca-9a2c-46ec-95d1-46786c22edcb · inbound

Test-Time Training for Modality Order Consistency in Vision-Language Models cites this paper.

Test-Time Training for Modality Order Consistency in Vision-Language Models Deriving Neural Scaling Laws from the statistics of natural language

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T10:07:25.606783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7ce5653-2c84-4218-a016-e6032ff5be0b · inbound

Bridging Compute- and Data-Optimal Pretraining cites this paper.

Bridging Compute- and Data-Optimal Pretraining Deriving Neural Scaling Laws from the statistics of natural language

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-01T03:02:04.112893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.