Paper Citation Record · LEDGER

Deep Networks Always Grok and Here is Why

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2402.15555.

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

pith.paper-citation-record.v1
2402.15555 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:27:49.405956Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T06:20:59.045952Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e572d749-9775-4c4f-8447-53871bc3e14a · inbound

Grokking at the Edge of Numerical Stability cites this paper.

Grokking at the Edge of Numerical Stability Deep Networks Always Grok and Here is Why

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T21:32:23.997722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c93a662c-a4ad-4b88-b089-80340c6a35b0 · inbound

Grokking vs. Learning: Same Features, Different Encodings cites this paper.

Grokking vs. Learning: Same Features, Different Encodings Deep Networks Always Grok and Here is Why

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T14:44:27.049435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

A Two-Phase Perspective on Deep Learning Dynamics cites this paper.

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 54c68532-1883-4e47-ac61-4564cddd9093 · inbound

Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model cites this paper.

Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model Deep Networks Always Grok and Here is Why

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:38.554591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c82fdf55-bd8b-4930-9ab6-0fac3db285e4 · inbound

NeuralGrok: Accelerate Grokking by Neural Gradient Transformation cites this paper.

NeuralGrok: Accelerate Grokking by Neural Gradient Transformation Deep Networks Always Grok and Here is Why

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:50:30.349783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 516a2bc2-0ad6-4865-97d4-94074518c8a5 · inbound

Mechanistic Insights into Grokking from the Embedding Layer cites this paper.

Mechanistic Insights into Grokking from the Embedding Layer Deep Networks Always Grok and Here is Why

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:31.770732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ba03143-8a1e-4c0b-89e6-ac072f76aff2 · inbound

Learning words in groups: fusion algebras, tensor ranks and grokking cites this paper.

Learning words in groups: fusion algebras, tensor ranks and grokking Deep Networks Always Grok and Here is Why

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T22:57:12.131495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a53d10fd-8ccc-461a-9e2b-64852680d8fe · inbound

Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories cites this paper.

Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories Deep Networks Always Grok and Here is Why

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:20:59.048212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 32ead466-befe-418d-a11a-4a3cf0e240d4 · inbound

The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold cites this paper.

The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold Deep Networks Always Grok and Here is Why

Reference 1970

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:00.715860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b3714d6-cf35-4d49-8942-2f6dde13a57d · inbound

HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing cites this paper.

HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing Deep Networks Always Grok and Here is Why

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:30:57.708237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 999e456e-aec6-4202-afc3-a61f8c04ed74 · inbound

Complexity of Linear Regions in Self-supervised Deep ReLU Networks cites this paper.

Complexity of Linear Regions in Self-supervised Deep ReLU Networks Deep Networks Always Grok and Here is Why

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:48.870088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d16deca6-8068-4b79-8e36-3a106b82ecdd · inbound

Topological Signatures of Grokking cites this paper.

Topological Signatures of Grokking Deep Networks Always Grok and Here is Why

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:13.421762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 999caabc-e53b-4d2c-b143-ea14816eb8fd · inbound

Emergent Generalization by Representation Learning in Artificial Neural Networks cites this paper.

Emergent Generalization by Representation Learning in Artificial Neural Networks Deep Networks Always Grok and Here is Why

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T11:48:27.847402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9121ef5-c3eb-4ef0-8b7c-e597a6aad232 · inbound

Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues cites this paper.

Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues Deep Networks Always Grok and Here is Why

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:48.885856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.