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Paper Citation Record · LEDGER

Learning Curve Theory

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2102.04074.

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

pith.paper-citation-record.v1
2102.04074 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 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 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:21:00.760063Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T17:20:00.916264Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 54430451-229d-4bbf-bab9-6a08dda57ccc · inbound

Scaling Laws for Upcycling Mixture-of-Experts Language Models cites this paper.

Scaling Laws for Upcycling Mixture-of-Experts Language Models Learning Curve Theory

Reference 29

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no resolver link, observed 2026-08-09T10:21:00.760063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T10:21:00.760063Z digest=sha256:38f1f0551400d2908dc5f94b6fffe329828bc64ac502b38ee124f06c7b35528f

Observation 45c58d3f-80c3-4e80-a78c-d0f601f51ca5 · 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 Learning Curve Theory

Reference 29

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no resolver link, observed 2026-08-08T12:44:23.751019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T12:44:23.751019Z digest=sha256:ff49f14eec1e7c3f93298bd540aa8ea7e3d071a0af785920b8a922db062d5dcc

Observation 86728ef3-148a-4115-bd3c-0a7e2e41ad9b · 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 Learning Curve Theory

Reference 35

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no resolver link, observed 2026-08-08T12:12:30.714593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T12:12:30.714593Z digest=sha256:061e386295e2adc26f2f385d0b0e621e1c4806b6bd20aee771a62b3a85773f1a

Observation 9335bd3f-f79a-4924-a653-ccd17f123656 · inbound

Superposition Yields Robust Neural Scaling cites this paper.

Superposition Yields Robust Neural Scaling Learning Curve Theory

Reference 19

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verified exact
arxiv_id, observed 2026-05-09T06:36:25.559183Z

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.

source=pdf_text observed=2026-05-22T14:38:44.789822Z digest=sha256:b6ced89ae820908dbe30ed2e777aeff8809b0006c591103907e88b791fe7fa65

Observation 7fbdc2cd-c03f-4aed-8ad7-76703561d66d · inbound

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning cites this paper.

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning Learning Curve Theory

Reference 15

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no resolver link, observed 2026-08-07T10:31:20.400174Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:31:20.400174Z digest=sha256:057feb5f355d95e89b1177b94e42c320da5792467eef592beb3088551e343c7c

Observation cac885d1-a04b-405d-ae7c-90021ba568a7 · inbound

Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens cites this paper.

Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens Learning Curve Theory

Reference 18

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no resolver link, observed 2026-08-06T19:40:36.494987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:40:36.494987Z digest=sha256:75b83f3de4a7c8872792efbf8a0fd904c7d1d16c92815bc16461ca4495c64a3c

Observation 07a45857-41e7-4472-b49e-101600fd9895 · inbound

Universal One-third Time Scaling in Learning Peaked Distributions cites this paper.

Universal One-third Time Scaling in Learning Peaked Distributions Learning Curve Theory

Reference 2022

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no resolver link, observed 2026-08-03T05:01:12.187219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:01:12.187219Z digest=sha256:153d3613de9c53ca33c2c48a4c55237f924c5c1bcffa483a7c3bd07393e1b1fd

Observation 9d418aea-4b31-4d20-b748-bbd2a6815ef9 · inbound

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

Inverse Depth Scaling From Most Layers Being Similar Learning Curve Theory

Reference 11

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no resolver link, observed 2026-08-03T04:07:45.786899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:07:45.786899Z digest=sha256:32ea78464ecf464bdd9264e334e09c4d5fc597a22672b91c2fcd38c1f5097f42

Observation 235d3e58-be75-4b97-b5d2-4306416d93bf · inbound

Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks cites this paper.

Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks Learning Curve Theory

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:56:18.750204Z

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.

source=pdf_text observed=2026-05-12T02:55:52.782619Z digest=sha256:2c76c5ce779242f9cc97b5d868bdd6c2242bb503ca418d2809a33cd9a85dbfbf

Observation a3ab7b4c-3a6f-4f72-9e56-bc8d1fdef615 · 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 Learning Curve Theory

Reference 6

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verified exact
arxiv_id, observed 2026-06-30T14:14:45.615817Z

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.

source=pdf_text observed=2026-06-30T14:09:52.456340Z digest=sha256:3ada544cdba4d0f70f2f670e88e23a31271d8d166a4becd7a965c178b4b117aa

Observation 4b09068c-8428-4656-9670-596aecf42846 · inbound

Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains cites this paper.

Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains Learning Curve Theory

Reference 23

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verified exact
arxiv_id, observed 2026-06-30T14:34:45.618841Z

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.

source=pdf_text observed=2026-06-30T14:28:23.013420Z digest=sha256:04e96202cbb8f067565080f988ede7b7f3c98f67be9da2477e012cb073d26be6

Observation f22ae927-b23a-4854-a72a-23c05b9b162f · inbound

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization cites this paper.

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization Learning Curve Theory

Reference 12

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verified exact
arxiv_id, observed 2026-06-29T14:23:31.002966Z

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.

source=pdf_text observed=2026-06-29T14:14:25.876963Z digest=sha256:57ef51ef97997b2a9f998f23c5bb1e870da033377cee684276b7c9ca877c8647

Observation a0e73cf4-1cba-4cf7-93eb-13aca9f53cb3 · inbound

Structure and Scale in Simplicial Sequence Modelling cites this paper.

Structure and Scale in Simplicial Sequence Modelling Learning Curve Theory

Reference 29

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verified exact
arxiv_id, observed 2026-07-01T21:16:14.974663Z

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.

source=pdf_text observed=2026-06-28T17:13:00.475488Z digest=sha256:a2395579c8bc86297fe172fe6aa6c2680d6959cff16791b76cbdeb9d5902be87

Observation 46a8f6cf-b85d-4c48-b8e3-4a965914f7b3 · inbound

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

Critical Percolation as a Synthetic Data Model for Interpretability Learning Curve Theory

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:49:29.628962Z

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.

source=pdf_text observed=2026-06-26T17:41:29.317167Z digest=sha256:9beef8494b0fb29c3aeaf693549d9628a7adab59490be4fc8e38b4fa2d2832e5

Observation aeb5af57-86f2-432d-b3c5-80e2a93cba6b · 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 Learning Curve Theory

Reference 8

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verified exact
arxiv_id, observed 2026-07-04T17:20:00.917928Z

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.

source=pdf_text observed=2026-06-25T23:45:54.283436Z digest=sha256:2974edc3f8156b57cc4a94fc7be1f2a51798cea6f775920b3af0a22216a5cf29

Observation ba03f533-3a3a-408c-8684-59c3d6ad8e73 · inbound

Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling cites this paper.

Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling Learning Curve Theory

Reference 7

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metadata mismatch
arxiv_id, observed 2026-07-04T12:59:52.795739Z

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.

source=arxiv_source observed=2026-06-26T05:36:30.686491Z digest=sha256:02abe2639659d54ca4604adcaf0e244a04db06e11cb0a50fa0cb545828126537

Observation 014a5a7b-1927-423b-bd7b-7fac0db2ffe2 · inbound

Smooth Scaling Laws Hide Stepwise Token Learning cites this paper.

Smooth Scaling Laws Hide Stepwise Token Learning Learning Curve Theory

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:14:26.530134Z

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.

source=pdf_text observed=2026-06-30T06:08:22.652557Z digest=sha256:c1d7ea64e39e1347d9b3f702d65e496ed93786b4219f4f1efce0ff70d5d18dd8

Observation 3f4bf71f-0bff-4138-90f4-0f80368459b3 · inbound

Smooth Scaling Laws Hide Stepwise Token Learning cites this paper.

Smooth Scaling Laws Hide Stepwise Token Learning Learning Curve Theory

Reference 4

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T07:15:03.029543Z digest=sha256:2f3ee7041cfc8a0e357ebef74d0f4f4e247fa246c290ba57856bcc1d452e9587

Observation 0d7164c8-5714-45ea-b080-b0d2cab4c437 · inbound

Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification cites this paper.

Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification Learning Curve Theory

Reference 2

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no resolver link, observed 2026-07-13T03:17:51.964379Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T03:17:51.964379Z digest=sha256:7851cee46d9ad406e11c0aed5adca0084b44960f61b5b57da90d4cdf9a52351c

Observation c2c5a61a-614a-4487-b41e-2c254051024d · 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 Learning Curve Theory

Reference 126

Resolution
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no resolver link, observed 2026-08-02T07:40:25.534753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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