Paper Citation Record · LEDGER

Chinchilla Scaling: A replication attempt

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2404.10102.

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

pith.paper-citation-record.v1
2404.10102 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 34 of 34 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:23.146179Z

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

0
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 9d989cec-7771-41d1-a130-01b1618bc8a9 · inbound

Optimization Hyper-parameter Laws for Large Language Models cites this paper.

Optimization Hyper-parameter Laws for Large Language Models Chinchilla Scaling: A replication attempt

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:45:48.784575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ab8ee623-1696-41c5-9a48-ad5dda08854a · inbound

Loss-to-Loss Prediction: Scaling Laws for All Datasets cites this paper.

Loss-to-Loss Prediction: Scaling Laws for All Datasets Chinchilla Scaling: A replication attempt

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T17:07:23.146179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d09775b-c7bb-4c8d-a997-34c0692e3751 · inbound

Neural Scaling Laws Rooted in the Data Distribution cites this paper.

Neural Scaling Laws Rooted in the Data Distribution Chinchilla Scaling: A replication attempt

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T18:30:15.448355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c63a44d2-e7a8-4a39-a917-635caeaf1f3a · inbound

Physics of Skill Learning cites this paper.

Physics of Skill Learning Chinchilla Scaling: A replication attempt

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T17:20:50.993062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d100234-d5ee-4cf5-ae7b-91a8a8b4af6a · inbound

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

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training Chinchilla Scaling: A replication attempt

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-09T21:56:50.749126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ad549d4-7fac-4b5a-90c7-53a13cc61fbb · inbound

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection cites this paper.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Chinchilla Scaling: A replication attempt

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T17:01:36.058241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c7bd792-94c3-45a6-9bb4-2e7f9ac512d9 · inbound

Superposition Yields Robust Neural Scaling cites this paper.

Superposition Yields Robust Neural Scaling Chinchilla Scaling: A replication attempt

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:36:25.448330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4401eddd-a9a3-452e-b4c9-229dfdb62623 · inbound

MuLoCo: Muon is a practical inner optimizer for DiLoCo cites this paper.

MuLoCo: Muon is a practical inner optimizer for DiLoCo Chinchilla Scaling: A replication attempt

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:26.228230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 448cd705-652d-45d9-b596-7f5c2e36200b · inbound

Beyond Text Compression: Evaluating Tokenizers Across Scales cites this paper.

Beyond Text Compression: Evaluating Tokenizers Across Scales Chinchilla Scaling: A replication attempt

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:05.875330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac512a91-f9ef-4f58-b9d1-981c11415bfe · inbound

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models cites this paper.

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models Chinchilla Scaling: A replication attempt

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:29.262637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd45fb1a-1888-4f8f-ab8c-f8fde07665ae · inbound

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search cites this paper.

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search Chinchilla Scaling: A replication attempt

Reference 110

Resolution: unresolved; no resolver link, observed 2026-08-07T05:07:39.948694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6e24277-4ffe-4c8e-a410-ed578df587e1 · inbound

Language Models Improve When Pretraining Data Matches Target Tasks cites this paper.

Language Models Improve When Pretraining Data Matches Target Tasks Chinchilla Scaling: A replication attempt

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:53:08.286130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e702ae16-0621-4282-b387-61a0c3b6388e · inbound

Inverse Depth Scaling From Most Layers Being Similar cites this paper.

Inverse Depth Scaling From Most Layers Being Similar Chinchilla Scaling: A replication attempt

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-03T04:07:45.197041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1525955-5590-43a4-aeaf-7aa0dcb00ef6 · inbound

How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models cites this paper.

How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models Chinchilla Scaling: A replication attempt

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:19:47.323235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b70aedf-3606-47dc-8cc0-8990dc73df1e · inbound

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws cites this paper.

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Chinchilla Scaling: A replication attempt

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T07:27:28.975922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e11477ab-2a84-442b-b7dd-c2dac43d37d7 · inbound

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws cites this paper.

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Chinchilla Scaling: A replication attempt

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:05:36.318692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ea57ab62-0ad0-429c-9653-0610045ca839 · inbound

Predicting Large Model Test Losses with a Noisy Quadratic System cites this paper.

Predicting Large Model Test Losses with a Noisy Quadratic System Chinchilla Scaling: A replication attempt

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:01:25.519173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2e9dc927-d7bc-40e6-a29f-b9438e301603 · inbound

Practical Scaling Laws: Converting Compute into Performance in a Data-Constrained World cites this paper.

Practical Scaling Laws: Converting Compute into Performance in a Data-Constrained World Chinchilla Scaling: A replication attempt

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:01:18.395295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2fb46b4b-b7a0-4536-b49c-b75ff6e873f6 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Chinchilla Scaling: A replication attempt

Reference 140

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:36:19.882972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 277aa453-afdb-4133-84c4-4a0b7739aa22 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Chinchilla Scaling: A replication attempt

Reference 140

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T07:32:30.191022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3f97b60e-3b74-4d0d-8144-ba705336e3e4 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Chinchilla Scaling: A replication attempt

Reference 140

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T07:59:50.256621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5b14b588-77d5-4b18-87a5-92b3eb0d7746 · inbound

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization cites this paper.

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization Chinchilla Scaling: A replication attempt

Reference 92

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:49:44.747649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4cfface1-c5bc-47f5-84be-00733c69024f · inbound

A Theory of Training Profit-Optimal LLMs cites this paper.

A Theory of Training Profit-Optimal LLMs Chinchilla Scaling: A replication attempt

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:13:42.734033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ced08cd-0e04-4596-90a2-dd329eb418a8 · inbound

A Theory of Training Profit-Optimal LLMs cites this paper.

A Theory of Training Profit-Optimal LLMs Chinchilla Scaling: A replication attempt

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:03.635405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c08e6761-26c7-4216-94ca-ab3328d384a9 · inbound

From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression cites this paper.

From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression Chinchilla Scaling: A replication attempt

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T14:14:45.569884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4fbfef15-b471-467a-a419-c299b28b2d66 · inbound

Structure and Scale in Simplicial Sequence Modelling cites this paper.

Structure and Scale in Simplicial Sequence Modelling Chinchilla Scaling: A replication attempt

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:26:13.631536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2a6a66c0-5eb8-44df-b73a-b64f87e8590b · inbound

Data-Driven Automation cites this paper.

Data-Driven Automation Chinchilla Scaling: A replication attempt

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T04:27:36.184415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 89dc0d15-b731-46ce-a4b8-33f4a30c44e9 · inbound

Internal Data Repetition Destroys Language Models cites this paper.

Internal Data Repetition Destroys Language Models Chinchilla Scaling: A replication attempt

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:49:57.915883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ecd69d7-5771-43fe-a248-5f891bd1ff34 · 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 Chinchilla Scaling: A replication attempt

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T17:20:00.859901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ad124522-31bd-4782-aa14-b30a8e765702 · inbound

Automated High-Precision Extraction and Forensic Verification of Data-Bearing Vector Figures cites this paper.

Automated High-Precision Extraction and Forensic Verification of Data-Bearing Vector Figures Chinchilla Scaling: A replication attempt

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T10:15:44.791996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23bec904-74ad-4f1b-964d-867691c5c602 · inbound

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size cites this paper.

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size Chinchilla Scaling: A replication attempt

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:08:57.610654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b01e3b2-e5a8-44ea-b374-b56d6f92ccaf · inbound

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

Information-Theoretic Limits of Reliability and Scaling in Language Models Chinchilla Scaling: A replication attempt

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T14:45:35.151566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee69bacb-afb1-40b6-bb33-8e9adc1539e6 · inbound

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

Bridging Compute- and Data-Optimal Pretraining Chinchilla Scaling: A replication attempt

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T03:01:55.590291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04b26bc0-6c1b-4fd6-be9e-02c91177dc8c · inbound

Skaling: Chinchilla's Exponents Meet Kaplan's Coupling cites this paper.

Skaling: Chinchilla's Exponents Meet Kaplan's Coupling Chinchilla Scaling: A replication attempt

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T12:32:57.240702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.