Paper Citation Record · LEDGER

Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2405.15459.

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

pith.paper-citation-record.v1
2405.15459 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:40.343242Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:04:38.769434Z

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 10a1e903-c85c-402b-b5c9-ed75f1db0ffd · inbound

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning cites this paper.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:40.343242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e5f4b3a-f9dd-4acf-8a26-1dd0705cb82b · inbound

Optimal Spectral Transitions in High-Dimensional Multi-Index Models cites this paper.

Optimal Spectral Transitions in High-Dimensional Multi-Index Models Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T11:55:22.485129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d891adcf-4063-4d6b-beea-bcf753a75f3a · inbound

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions cites this paper.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T15:34:16.569798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77158d68-8908-4806-9133-922debe71e6c · inbound

On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective cites this paper.

On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:40:54.390971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90d0321a-5735-4f92-959a-e46a227a22f9 · inbound

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model cites this paper.

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 166

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T01:39:38.361285Z

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 fd157416-7612-468c-82eb-d28254755e62 · inbound

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models cites this paper.

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:14:53.232224Z

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 3398e3d1-7dda-44d3-8b4e-544a0252fc96 · inbound

How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis cites this paper.

How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:04:38.771038Z

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 0d43a4f8-e80f-443b-a125-1d097146c688 · inbound

Approximate Message Passing with Random Initialization for Phase Retrieval cites this paper.

Approximate Message Passing with Random Initialization for Phase Retrieval Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T23:35:23.380334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.