Paper Citation Record · LEDGER

Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

As of 22 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.02244.

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

pith.paper-citation-record.v1
2310.02244 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-22T06:31:00.163083+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T01:53:04.715108Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.618721Z

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 4c706d38-bffc-4f2f-b8bd-2735c200193c · inbound

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies cites this paper.

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:00:53.495472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 3a04f458-6a3a-4a6b-bf76-703cf6543861 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:21:09.150893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 0af56707-e610-460f-9ee0-198ac4b29270 · inbound

OrScale: Orthogonalised Optimization with Layer-Wise Trust-Ratio Scaling cites this paper.

OrScale: Orthogonalised Optimization with Layer-Wise Trust-Ratio Scaling Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:05:55.506654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 821d2fe6-a39f-4046-8209-a0b0731b2d3c · 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 Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:49:44.894754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation d2d0464d-499f-43a4-90c6-7343b27f61b9 · inbound

Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training cites this paper.

Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.723544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 9289830a-a35b-4792-ab8a-c0a5642b785c · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 89

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.620284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.