Paper Citation Record · LEDGER

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment

As of 9 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2605.02183.

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

pith.paper-citation-record.v1
2605.02183 v1

Coverage vector

measured 32 of 32 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-05-08T19:02:26.047252Z

measured 32 of 32 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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verified fuzzy27
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External citation measurements

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Outbound references

Observation 71c260e8-424e-4a59-bf11-a157d0c73247 · outbound

This paper cites Cuda: Curriculum of data augmentation for long-tailed recognition.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Cuda: Curriculum of data augmentation for long-tailed recognition

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.782555Z

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 63a21006-f283-42ad-8425-d792f50a4692 · outbound

This paper cites Prevalence of simplex compression in adversarially robust neural networks.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Prevalence of simplex compression in adversarially robust neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.771546Z

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Observation a28055d2-e48c-407b-9305-31f550465343 · outbound

This paper cites Long-tailed adversarial training with self- distillation.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Long-tailed adversarial training with self- distillation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.775950Z

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Observation f8c40d90-4c11-4ab3-bc55-9e42645ec052 · outbound

This paper cites Global and local mix- ture consistency cumulative learning for long-tailed visual recognitions.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Global and local mix- ture consistency cumulative learning for long-tailed visual recognitions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.779580Z

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Observation b082e14c-4dd9-4a28-8cb7-09cbe3f55694 · outbound

This paper cites FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:05:34.773826Z

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Observation e951d174-e448-4905-a93c-250f81b5a166 · outbound

This paper cites Las-at: adversarial training with learnable attack strategy.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Las-at: adversarial training with learnable attack strategy

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.857759Z

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

This paper cites Neural Collapse: A Review on Modelling Principles and Generalization.

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

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Observation 46fd7e7a-59d5-45c0-ba1a-53871f0386d2 · outbound

This paper cites Comparative study of adversarial training methods for long-tailed classification.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Comparative study of adversarial training methods for long-tailed classification

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.854336Z

Source-reported events for the cited work

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Observation 628d1c03-1b12-43fc-b039-2d1f013d9107 · outbound

This paper cites Alleviating the effect of data imbalance on ad- versarial training.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Alleviating the effect of data imbalance on ad- versarial training

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.786501Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ca215450-1636-4132-a094-41cdf3b07628 · outbound

This paper cites Enhancing the adversarial robustness via manifold projec- tion.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Enhancing the adversarial robustness via manifold projec- tion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.829183Z

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 6993bc55-8b0e-4249-aea2-3cedc853f89b · outbound

This paper cites Breadcrumbs: Adversarial class-balanced sampling for long-tailed recognition.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Breadcrumbs: Adversarial class-balanced sampling for long-tailed recognition

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.802379Z

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Observation 824f802f-ad7b-441c-8105-f7d8c6236fce · outbound

This paper cites an unresolved cited work.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-26T03:37:18.808527Z

Source-reported events for the cited work

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Observation 78cc8e94-f972-46be-bd52-29308517d305 · outbound

This paper cites Balanced meta-softmax for long- tailed visual recognition.Advances in neural information processing systems, 33:4175–4186.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Balanced meta-softmax for long- tailed visual recognition.Advances in neural information processing systems, 33:4175–4186

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.798538Z

Source-reported events for the cited work

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Observation 62abbbfa-821a-43d6-9f29-aeab52d29b9f · outbound

This paper cites Geometrically regularized transfer learning with on-manifold and off-manifold perturbation.arXiv preprint arXiv:2505.15191.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Geometrically regularized transfer learning with on-manifold and off-manifold perturbation.arXiv preprint arXiv:2505.15191

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:05:34.770548Z

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Observation 6a838524-d2d8-4435-b83a-066af16c5fb4 · outbound

This paper cites Diffult: Diffusion for long-tailed recognition without external knowledge.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Diffult: Diffusion for long-tailed recognition without external knowledge

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.790006Z

Source-reported events for the cited work

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Observation 3ac4e559-c3dd-426d-8674-378d8c419a6a · outbound

This paper cites Rethinking classifier re-training in long-tailed recognition: Label over-smooth can balance.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Rethinking classifier re-training in long-tailed recognition: Label over-smooth can balance

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.842807Z

Source-reported events for the cited work

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Observation 82937e52-a5b4-4883-b1bd-66db45c81b65 · outbound

This paper cites Improv- ing adversarial robustness requires revisiting misclassified examples.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Improv- ing adversarial robustness requires revisiting misclassified examples

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.836165Z

Source-reported events for the cited work

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Observation 17328e23-f5a9-483c-99d3-bb002b301221 · outbound

This paper cites Adversarial weight perturbation helps robust gener- alization.Advances in neural information processing sys- tems, 33:2958–2969.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Adversarial weight perturbation helps robust gener- alization.Advances in neural information processing sys- tems, 33:2958–2969

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.825614Z

Source-reported events for the cited work

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Observation 5dd1c641-f313-4760-81fc-51ddda3b4a49 · outbound

This paper cites Adversarial robustness under long-tailed distribution.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Adversarial robustness under long-tailed distribution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.860962Z

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 5e1f32a9-026c-4f75-b01f-7dc8cfd12d02 · outbound

This paper cites Towards calibrated model for long-tailed visual recognition from prior perspective.Advances in Neural Information Processing Systems, 34:7139–7152.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Towards calibrated model for long-tailed visual recognition from prior perspective.Advances in Neural Information Processing Systems, 34:7139–7152

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.793924Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3eb78995-f0ce-4ea6-8675-75873af7b399 · outbound

This paper cites Geom- etry of long-tailed representation learning: Rebalancing features for skewed distributions.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Geom- etry of long-tailed representation learning: Rebalancing features for skewed distributions

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.818759Z

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Observation 81839d62-3fc0-4450-92bf-35e1436cbbcb · outbound

This paper cites Taet: Two-stage adversarial equalization training on long-tailed distributions.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Taet: Two-stage adversarial equalization training on long-tailed distributions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.832650Z

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Observation e0c82c76-446c-47a5-a925-78594063c3bc · outbound

This paper cites Revisiting adversarial training under long- tailed distributions.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Revisiting adversarial training under long- tailed distributions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.815489Z

Source-reported events for the cited work

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Observation f9a829d0-60ce-401b-9743-3c9538d4fe1e · outbound

This paper cites Robust long-tailed image classification via adversarial fea- ture re-calibration.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Robust long-tailed image classification via adversarial fea- ture re-calibration

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.872168Z

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 146a15b2-b805-4a85-bfb2-c6db2d77ad80 · outbound

This paper cites Xing, Laurent El Ghaoui, and Michael I.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Xing, Laurent El Ghaoui, and Michael I

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.839692Z

Source-reported events for the cited work

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Observation 07a97abd-6100-4f0f-8840-3116f5608814 · outbound

This paper cites Adversarial examples for good: Adversarial examples guided imbalanced learning.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Adversarial examples for good: Adversarial examples guided imbalanced learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.864388Z

Source-reported events for the cited work

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Observation 29ab1683-c086-45cb-8298-c1d6008e6865 · outbound

This paper cites Deep long-tailed learn- ing: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Deep long-tailed learn- ing: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.822457Z

Source-reported events for the cited work

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Observation c483fbdf-7a9b-4210-bb3e-21ae43a48399 · outbound

This paper cites Manifold-driven decomposition for adversarial robustness.Frontiers in Computer Science, 5:1274695.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Manifold-driven decomposition for adversarial robustness.Frontiers in Computer Science, 5:1274695

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.850359Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f9215ba-c95f-41d3-b175-e217e75cda6c · outbound

This paper cites A system- atic review on long-tailed learning.IEEE Transactions on Neural Networks and Learning Systems.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment A system- atic review on long-tailed learning.IEEE Transactions on Neural Networks and Learning Systems

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.805501Z

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 9401a90a-8edc-456f-be53-4e9d390cbd23 · outbound

This paper cites Continuous contrastive learning for long-tailed semi-supervised recog- nition.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Continuous contrastive learning for long-tailed semi-supervised recog- nition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.869037Z

Source-reported events for the cited work

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Observation ee2cf4be-739e-482c-93c9-fedacd31c785 · outbound

This paper cites Bal- anced contrastive learning for long-tailed visual recogni- tion.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Bal- anced contrastive learning for long-tailed visual recogni- tion

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:37:18.811947Z

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Observation fd0444ac-97b7-41f1-8836-d028d63f99be · outbound

This paper cites Compared to Base AT, MCAT yields tighter and better-separated tail clusters while maintaining compact head representations.

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment Compared to Base AT, MCAT yields tighter and better-separated tail clusters while maintaining compact head representations

Reference 32

Resolution: malformed identifier; raw_fallback, observed 2026-05-26T03:37:18.846395Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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