Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 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 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:54:36.990546Z

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 2026-08-09T06:31:02.800959+00:00.

Observation b7c6b5ed-b662-47d4-aeac-825a38105945 · inbound

Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer cites this paper.

Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:36.990546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 902f8844-e176-4fce-ad62-704af23d6f40 · inbound

SingLoRA: Low Rank Adaptation Using a Single Matrix cites this paper.

SingLoRA: Low Rank Adaptation Using a Single Matrix Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:16.960316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 107d6887-7ae2-4a9e-bec6-70f8aee2ef48 · inbound

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs cites this paper.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.143579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abc3c35c-811b-4b2d-9a84-c1e0bec61442 · inbound

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance cites this paper.

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 118

Resolution: unresolved; no resolver link, observed 2026-08-06T11:44:05.211741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

Observation afdf5d92-3aad-43d7-98bc-10132918a436 · 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: unresolved; no resolver link, observed 2026-08-02T10:14:13.056533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43a20b59-8207-448e-8146-886ab29ac9f2 · inbound

DeepLoop: Depth Scaling for Looped Transformers cites this paper.

DeepLoop: Depth Scaling for Looped Transformers Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.727834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e09b8ad-c1b1-4069-adca-33ab7def3d1a · inbound

Scale Weight Decay and Train Better cites this paper.

Scale Weight Decay and Train Better Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-30T12:53:41.186767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.