Paper Citation Record · LEDGER

A Two-Phase Perspective on Deep Learning Dynamics

As of 20 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2504.12700.

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

pith.paper-citation-record.v1
2504.12700 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:27:49.499597Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy11
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9e80c18b-605e-46bb-a8a2-a19a44eb19c1 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

A Two-Phase Perspective on Deep Learning Dynamics Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.389192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 243b8f4d-f8e5-49df-93c4-6a44c9ce3193 · outbound

This paper cites Omnigrok: Grokking Beyond Algorithmic Data.

A Two-Phase Perspective on Deep Learning Dynamics Omnigrok: Grokking Beyond Algorithmic Data

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.394756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f79a466-9aac-49cd-8814-524576b7e37f · outbound

This paper cites Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data.

A Two-Phase Perspective on Deep Learning Dynamics Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.400092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62bcf852-774c-4319-b882-f04b73193402 · outbound

This paper cites Deep Networks Always Grok and Here is Why.

A Two-Phase Perspective on Deep Learning Dynamics Deep Networks Always Grok and Here is Why

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.405956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68fddc4c-0d10-44b5-a1b5-afc480e7716f · outbound

This paper cites Explaining grokking through circuit efficiency.

A Two-Phase Perspective on Deep Learning Dynamics Explaining grokking through circuit efficiency

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.411603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff1d6786-dcdb-44aa-98b1-b7d56ed3a6fc · outbound

This paper cites Grokking and the Geometry of Circuit Formation,.

A Two-Phase Perspective on Deep Learning Dynamics Grokking and the Geometry of Circuit Formation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.910840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f47d8d08-c895-4cee-b8d4-a1623797ef93 · outbound

This paper cites Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition.

A Two-Phase Perspective on Deep Learning Dynamics Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.422435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 859e1c7d-fa47-424d-a059-cc31a15aaf0a · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

A Two-Phase Perspective on Deep Learning Dynamics Progress measures for grokking via mechanistic interpretability

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.427633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3cafea8-ef25-4089-b5a6-4e7867eab224 · outbound

This paper cites Zoom in: An introduction to circuits,.

A Two-Phase Perspective on Deep Learning Dynamics Zoom in: An introduction to circuits,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.895185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a94b629-7d64-4dbb-954e-b6d82ab4d27f · outbound

This paper cites Hidden progress in deep learning: Sgd learns parities near the computational limit,.

A Two-Phase Perspective on Deep Learning Dynamics Hidden progress in deep learning: Sgd learns parities near the computational limit,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.880189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3511184-a246-4665-b36c-10942137da35 · outbound

This paper cites Grokking as Compression: A Nonlinear Complexity Perspective.

A Two-Phase Perspective on Deep Learning Dynamics Grokking as Compression: A Nonlinear Complexity Perspective

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.442057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f24a3bf-13c8-4f65-9153-2ef851fb1066 · outbound

This paper cites Deep double descent: Where bigger models and more data hurt,.

A Two-Phase Perspective on Deep Learning Dynamics Deep double descent: Where bigger models and more data hurt,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.863846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3707e4d1-75d8-41f8-9c6d-b24e9b488170 · outbound

This paper cites Double trouble in double descent: Bias and variance (s) in the lazy regime,.

A Two-Phase Perspective on Deep Learning Dynamics Double trouble in double descent: Bias and variance (s) in the lazy regime,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.848765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57492f93-52d6-4699-a974-b4ee53ff9ef1 · outbound

This paper cites A brief prehistory of double descent,.

A Two-Phase Perspective on Deep Learning Dynamics A brief prehistory of double descent,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.832904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cd50dd40-e71e-40bd-bfac-f60eaa7b190d · outbound

This paper cites The information bottleneck method.

A Two-Phase Perspective on Deep Learning Dynamics The information bottleneck method

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.460886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a8d423f-b906-4940-8e01-9f806a124a11 · outbound

This paper cites Deep learning and the information bottleneck principle,.

A Two-Phase Perspective on Deep Learning Dynamics Deep learning and the information bottleneck principle,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.813452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1f69bb5e-120a-4ff1-b985-db1853642e70 · outbound

This paper cites Opening the Black Box of Deep Neural Networks via Information.

A Two-Phase Perspective on Deep Learning Dynamics Opening the Black Box of Deep Neural Networks via Information

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.470319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8765667-8208-4731-9164-325e0e318003 · outbound

This paper cites The renormalization group: Critical phenomena and the Kondo problem,.

A Two-Phase Perspective on Deep Learning Dynamics The renormalization group: Critical phenomena and the Kondo problem,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.793992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9a0482e4-4954-49b6-8b9b-537c2defec9e · outbound

This paper cites An exact mapping between the Variational Renormalization Group and Deep Learning.

A Two-Phase Perspective on Deep Learning Dynamics An exact mapping between the Variational Renormalization Group and Deep Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T12:27:49.479944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69eecf8b-f28e-4a04-b1e2-7a9e2b475b96 · outbound

This paper cites Mutual information, neural networks and the renormalization group.

A Two-Phase Perspective on Deep Learning Dynamics Mutual information, neural networks and the renormalization group

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.775990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e3b1a5c-609f-4f3f-9e49-6372eb04a3e7 · outbound

This paper cites Is deep learning a renormalization group flow?.

A Two-Phase Perspective on Deep Learning Dynamics Is deep learning a renormalization group flow?

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.759287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e1d11958-4f8d-4f76-bada-4e9997dc0431 · outbound

This paper cites Short-sighted deep learning,.

A Two-Phase Perspective on Deep Learning Dynamics Short-sighted deep learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:27:49.743619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf13d3e2-1d1c-4338-b338-5bbfcb6ae4b8 · outbound

This paper cites Why Unsupervised Deep Networks Generalize.

A Two-Phase Perspective on Deep Learning Dynamics Why Unsupervised Deep Networks Generalize

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:27:49.543685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.