Paper Citation Record · LEDGER

nGPT: Normalized Transformer with Representation Learning on the Hypersphere

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2410.01131.

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

pith.paper-citation-record.v1
2410.01131 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:23:40.961082Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.817010Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 387a7cb2-6a81-4870-b072-6f18762890be · inbound

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture cites this paper.

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T11:23:40.961082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88573fc3-ad48-4eaa-b307-dfd6c924cc3e · inbound

Normalized Matching Transformer cites this paper.

Normalized Matching Transformer nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:05:11.573058Z

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 7b47b369-0bb6-4d15-bd65-c14e732def0f · inbound

Superposition Yields Robust Neural Scaling cites this paper.

Superposition Yields Robust Neural Scaling nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:36:25.534812Z

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 3d9ed69f-484d-48ca-8a46-892eb45a7214 · inbound

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:01:46.189690Z

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 d37b01ab-8058-4db7-8d15-a58eb8b1d551 · inbound

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T10:25:17.146093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31d74673-2c9c-49e5-bac5-b2bbd047cde4 · inbound

Universal One-third Time Scaling in Learning Peaked Distributions cites this paper.

Universal One-third Time Scaling in Learning Peaked Distributions nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T05:01:12.799568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d37d4404-41ed-4d4c-afe2-b8aaa8b02984 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:49.967839Z

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 1995a4cf-7eb9-4beb-9620-08691b5fd5f0 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:12:41.324396Z

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 116c1d4c-9209-4a51-ae74-c921b6830765 · inbound

When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer cites this paper.

When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:10.441727Z

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 e5ba9dbe-d9d5-43d1-b2eb-7cb27ed5d92b · inbound

Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning cites this paper.

Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:26:08.092346Z

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 2f6df2fc-544a-4582-b3a9-75b4789c61fe · inbound

Demystifying Manifold Constraints in LLM Pre-training cites this paper.

Demystifying Manifold Constraints in LLM Pre-training nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:16:08.766353Z

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 740f07ba-42f1-4ad7-97f0-13eb8ed12c3a · inbound

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity cites this paper.

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:21:08.524404Z

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 b627a3f8-9cea-4adc-9bcf-1d5ab827f2cf · inbound

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives cites this paper.

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:37:19.228142Z

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 7db5a631-d2b0-4884-94ad-538826e3c8e2 · inbound

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction cites this paper.

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:57:53.678529Z

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 65f73d7f-438a-4fc4-9137-c19d6dcc5240 · inbound

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders cites this paper.

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T07:15:29.656032Z

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 1cb962d3-3aad-45f7-a761-3b2b78183328 · 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 nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 100

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.818744Z

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 f2e2411a-d06e-4433-add0-6b0d47495558 · 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 nGPT: Normalized Transformer with Representation Learning on the Hypersphere

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-02T10:14:09.204824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.