Paper Citation Record · LEDGER

Growing Neural Networks: Dynamic Evolution through Gradient Descent

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.18012.

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

pith.paper-citation-record.v1
2501.18012 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T01:13:15.449012Z

measured 42 of 42 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 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

42 of 42 outbound references displayed

verified exact2
verified fuzzy11
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f71e704-cfa5-4834-bd9b-3f6242bbbded · outbound

This paper cites This requires careful design of differentiable transitions between network sizes.

Growing Neural Networks: Dynamic Evolution through Gradient Descent This requires careful design of differentiable transitions between network sizes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:32.710736Z

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 5fd71507-d9e8-4b84-8122-db67ad8b2765 · outbound

This paper cites Both approaches require a smooth transition function ψ(x) that approximates a step function while maintain- ing differentiability.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Both approaches require a smooth transition function ψ(x) that approximates a step function while maintain- ing differentiability

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:32.569436Z

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 cc4cfd25-6b6c-43c5-8ebc-2beaab720887 · outbound

This paper cites on” (white squares), one partially “on.

Growing Neural Networks: Dynamic Evolution through Gradient Descent on” (white squares), one partially “on

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:32.369330Z

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 bb4d4a92-a679-4c96-aeb3-e3a5fb91079b · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:32.346994Z

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 735cce40-5449-4849-9c94-6d544eb1a496 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:32.325820Z

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 87b67cd6-caaa-4709-93e6-3032eee4c827 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:32.244149Z

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 d7b29cae-d710-444f-ba26-09f20789fdd1 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:32.022749Z

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 4f394f5f-dba0-48dc-9c9b-505cec9709e1 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.483211Z

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 6f581d36-34de-4a91-ac02-6c06dd59d7fb · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.830410Z

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 df6d7c43-ba29-4ed7-a92c-2e695e0310bb · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.721664Z

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 05fe98ef-4d24-4219-8a99-0107b7f13370 · outbound

This paper cites This technique of using an auxiliary weight to set neural network constraints was first used by Jin et al.

Growing Neural Networks: Dynamic Evolution through Gradient Descent This technique of using an auxiliary weight to set neural network constraints was first used by Jin et al

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.972985Z

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 ed5b0959-c034-4659-b951-745e7bfe25ed · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.703197Z

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 ad7e9ca8-22eb-46eb-be38-e67fc766fe3d · outbound

This paper cites (22) As N increases, new neurons smoothly activate and join the computation, as shown in figure 1.

Growing Neural Networks: Dynamic Evolution through Gradient Descent (22) As N increases, new neurons smoothly activate and join the computation, as shown in figure 1

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.685774Z

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 12484037-2b64-47d7-8c0b-83445a1003d1 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.665164Z

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 f96bc5ee-0804-4b00-a403-7afcb0152d0b · outbound

This paper cites bigger is better.

Growing Neural Networks: Dynamic Evolution through Gradient Descent bigger is better

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.646948Z

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 3dce20bf-a6f3-4203-8e31-0b7fd9aaf11d · outbound

This paper cites Language Models are Few-Shot Learners.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Language Models are Few-Shot Learners

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.230609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11d52dd7-1975-4382-8c40-fc232c9599a0 · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Energy and Policy Considerations for Deep Learning in NLP

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.237816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c12a7aa-e4ac-4ff8-b168-067ac940b242 · outbound

This paper cites Ai and compute,.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Ai and compute,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.628847Z

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 f4752ad8-341a-471a-86f7-0115620aabb8 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Growing Neural Networks: Dynamic Evolution through Gradient Descent The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.250799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab07f22f-8929-4368-8702-a9837140da90 · outbound

This paper cites Compression of Neural Machine Translation Models via Pruning.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Compression of Neural Machine Translation Models via Pruning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.258005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 574ca022-04ec-4e1a-b9e4-6899a6f436b7 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.610957Z

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 620c0fe3-900d-4f4d-999b-84f00d547ee8 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 22

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T01:13:30.845632Z

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 4e3cc05a-9d30-47b8-870c-996b9c033183 · outbound

This paper cites Aggarwal and K.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Aggarwal and K

Reference 23

Resolution: verified exact; doi, observed 2026-08-10T01:13:15.544228Z

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 504225b2-0626-4531-81ae-6e625b651399 · outbound

This paper cites Tunc and L.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Tunc and L

Reference 24

Resolution: verified exact; doi, observed 2026-08-10T01:13:15.509965Z

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 1d958f3a-2779-4464-9c4f-ff6a41bbcaee · outbound

This paper cites Neural Architecture Search: A Survey.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Neural Architecture Search: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.304999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6ab3b8b-d180-4a93-9bc0-da25697fcb31 · outbound

This paper cites What is the State of Neural Network Pruning?.

Growing Neural Networks: Dynamic Evolution through Gradient Descent What is the State of Neural Network Pruning?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.312961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3adb772e-d5b9-4c63-99ab-1bf3b7bfc1b5 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.350495Z

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 eb8090b1-77ca-4e79-a11b-13f7835d773b · outbound

This paper cites Fahlman and C.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Fahlman and C

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.278990Z

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 de83aef9-a7b7-4be6-8cf2-5f02b53f3068 · outbound

This paper cites Optimization Methods for Large-Scale Machine Learning.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Optimization Methods for Large-Scale Machine Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.331410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5905fdaf-88ae-4c47-bc79-8c1bdf762871 · outbound

This paper cites Allen-Zhu, Y.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Allen-Zhu, Y

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.249463Z

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 8d72bf8a-4bbf-41de-b861-2ad8af6182fc · outbound

This paper cites Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.347461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b716871e-5b7c-4718-bcdc-76d2667bb706 · outbound

This paper cites A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics.

Growing Neural Networks: Dynamic Evolution through Gradient Descent A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.355179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a180f8-3ef6-4a0a-b26c-05ecb0fd1365 · outbound

This paper cites Physics-Informed Neural Networks for Quantum Eigenvalue Problems.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Physics-Informed Neural Networks for Quantum Eigenvalue Problems

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.362660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c485027d-ad7c-4ac8-af18-1fa0e02225a9 · outbound

This paper cites Wolfram Research, Inc., Mathematica,.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Wolfram Research, Inc., Mathematica,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:31.229083Z

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 c1e3d931-58ec-4fa4-bbe0-6bcdbfb81696 · outbound

This paper cites JAX: com- posable transformations of Python+NumPy programs,.

Growing Neural Networks: Dynamic Evolution through Gradient Descent JAX: com- posable transformations of Python+NumPy programs,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.387268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 053d1113-7261-4851-ae80-245695dfb549 · outbound

This paper cites Kidger and C.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Kidger and C

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.395527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a634493-ee1f-4d96-9ea5-375b2f0f8a70 · outbound

This paper cites The DeepMind JAX Ecosystem,.

Growing Neural Networks: Dynamic Evolution through Gradient Descent The DeepMind JAX Ecosystem,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.404568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b8022d9-cec7-484f-8e71-5f1af4a297a2 · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:31.097235Z

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 000914fe-30b4-4d89-9c36-2c22c4b9de7f · outbound

This paper cites an unresolved cited work.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-10T01:13:30.970973Z

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 a72cd15a-0736-445f-8236-703e35d861cd · outbound

This paper cites Sarao Mannelli, Y.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Sarao Mannelli, Y

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T01:13:30.950682Z

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 d368004d-aecc-4120-8bf5-55451a16e8de · outbound

This paper cites Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI.

Growing Neural Networks: Dynamic Evolution through Gradient Descent Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.440696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18ab6f87-14c5-48b3-94e2-cd7596422772 · outbound

This paper cites GradMax: Growing Neural Networks using Gradient Information.

Growing Neural Networks: Dynamic Evolution through Gradient Descent GradMax: Growing Neural Networks using Gradient Information

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T01:13:15.449012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.