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Paper Citation Record · LEDGER

Neural Collapse: A Review on Modelling Principles and Generalization

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2206.04041.

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

pith.paper-citation-record.v1
2206.04041 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:22:07.431949Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

12
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d0814836-105b-4ad0-9f8b-d136d63360e3 · inbound

Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning cites this paper.

Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning Neural Collapse: A Review on Modelling Principles and Generalization

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-12T20:56:52.003008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:56:52.003008Z digest=sha256:f2556efa099249dd978ef1962f2381cd26b804e7089976c6a7f4709ae63527f2

Observation 2a48d8ac-ed95-4afc-9510-904fa43b91f4 · inbound

Superposition Yields Robust Neural Scaling cites this paper.

Superposition Yields Robust Neural Scaling Neural Collapse: A Review on Modelling Principles and Generalization

Reference 38

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T06:36:25.580041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-22T14:38:44.789822Z digest=sha256:5cbf8b26aa9dbb2b5a5149445c3adc357ec7fc5a7f20bb71e58bda0a70d2c6ac

Observation 3487a408-09a7-4115-bd16-98eafa94599d · inbound

Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets cites this paper.

Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets Neural Collapse: A Review on Modelling Principles and Generalization

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T15:30:49.906797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:30:49.906797Z digest=sha256:d999bbd63bd41b5df78ebf81e0e41b5af7bf232d0b2d747fef6bfc1aa15a84d0

Observation 950a49b5-cdf4-445e-8e06-ded169d96f66 · inbound

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations cites this paper.

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations Neural Collapse: A Review on Modelling Principles and Generalization

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:20.976793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:20.976793Z digest=sha256:fb5f08d19404cc2261497a1d05e81e2c8dea6095a9e0c18ef927725485c11255

Observation f97d82b0-a8d7-4014-9b2c-1eaff7091f5e · inbound

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios cites this paper.

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Neural Collapse: A Review on Modelling Principles and Generalization

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T15:49:06.687319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:49:06.687319Z digest=sha256:0d0467cf09ffd708c20484afd2397840c1a19ad5acd411b391fa9bafd7914104

Observation f0e1369d-40ec-478c-82bf-94d7d5869042 · inbound

Feature learning is decoupled from generalization in high capacity neural networks cites this paper.

Feature learning is decoupled from generalization in high capacity neural networks Neural Collapse: A Review on Modelling Principles and Generalization

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T14:17:37.133481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:17:37.133481Z digest=sha256:0384f402c1439582485b6607ecfac09dbe7e759ad3be87d8d506c10818faf1e0

Observation d83254de-0cfb-4133-bc54-24991b8a0789 · inbound

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

There Will Be a Scientific Theory of Deep Learning Neural Collapse: A Review on Modelling Principles and Generalization

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:21:08.656048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-09T20:11:17.616190Z digest=sha256:c55b685b9ded2fa0305f828c4bb8c45fbcc875bdc36bc7ac33d2467605170622

Observation 0406925d-959f-40ff-bd23-6e767b6c2fe8 · inbound

How Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse cites this paper.

How Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse Neural Collapse: A Review on Modelling Principles and Generalization

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-08T19:34:04.858825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-08T19:29:49.018621Z digest=sha256:7ed880a4bb80396e00722d062ea7f15e463d989bd9f28480faed139ac55eb32e

Observation 4c24e092-5964-4154-86fe-835946959f55 · inbound

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment cites this paper.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Neural Collapse: A Review on Modelling Principles and Generalization

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:05:34.765302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-08T19:02:26.047252Z digest=sha256:d4d639c86546b682f84bdad5609c32a839b93576737de67bad224b581be69078

Observation a181cd52-a30c-44a6-8529-1b1abae5c447 · inbound

Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets cites this paper.

Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets Neural Collapse: A Review on Modelling Principles and Generalization

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-13T02:32:06.168537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-13T02:31:56.483055Z digest=sha256:c2e6e1b2e322cc8c09f7cdf4fc1f446b2120491a7767c8881d32ebac041560a3

Observation 476a716f-d74e-4b77-8105-aee9f88d1301 · inbound

Learning from almost nothing: How neural networks survive heavy input corruption cites this paper.

Learning from almost nothing: How neural networks survive heavy input corruption Neural Collapse: A Review on Modelling Principles and Generalization

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-03T04:17:36.696557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-27T14:06:36.969337Z digest=sha256:2049116b3ebfa3c9a2bb9f420167560193540002de9cb884d531f7b110fa5267

Observation 87a09b96-cfcb-4a85-aac0-c6884f9b567b · inbound

Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment cites this paper.

Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment Neural Collapse: A Review on Modelling Principles and Generalization

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-07-09T11:16:11.444678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-07-09T11:08:15.865799Z digest=sha256:d9b2446d9fd4d0ae162407d3815375a4cf82a9ac052177df6bdf51f1e3bdbc1b

Observation 62f82a29-a10f-4a1e-965f-79ea3ef81ab2 · inbound

How to Tame Grokking: Representation Geometry as a Control Signal cites this paper.

How to Tame Grokking: Representation Geometry as a Control Signal Neural Collapse: A Review on Modelling Principles and Generalization

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-14T04:01:12.361370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T04:01:12.361370Z digest=sha256:ec05e9f70e90ac248553c638a7bc791e6ac523ae021cc365be3165ae75d9102d

Observation eaa26e45-5c23-4d2e-a206-9cac8d5c9913 · inbound

NAE: Normalizing AutoEncoder cites this paper.

NAE: Normalizing AutoEncoder Neural Collapse: A Review on Modelling Principles and Generalization

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-16T00:22:07.431949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:22:07.431949Z digest=sha256:78731ab84f1dc2f76066ba0ada3385066843f2a31f7b5d183aaaa505301a3b08