Paper Citation Record · LEDGER

Adversarial Examples Are Not Bugs, They Are Superposition

As of 21 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2508.17456.

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

pith.paper-citation-record.v1
2508.17456 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:56:12.871181Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:15:55.665105Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

verified exact13
verified fuzzy3
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63b204cc-9ddf-4253-add6-c762204ac63d · outbound

This paper cites On the complexity of neural computation in superposition, 2025.

Adversarial Examples Are Not Bugs, They Are Superposition On the complexity of neural computation in superposition, 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:07.235659Z

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Observation cd8b4c7d-b792-4682-8a2a-a9a5c6403fc3 · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning.

Adversarial Examples Are Not Bugs, They Are Superposition Towards monosemanticity: Decomposing language models with dictionary learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:07.360553Z

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Observation 2a8cb003-69ce-4f67-b6b4-0b8d3b430be9 · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

Adversarial Examples Are Not Bugs, They Are Superposition Towards Evaluating the Robustness of Neural Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:07.470698Z

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Observation a0bf3682-10f0-4870-a869-3481e749e55a · outbound

This paper cites Unlabeled Data Improves Adversarial Robustness.

Adversarial Examples Are Not Bugs, They Are Superposition Unlabeled Data Improves Adversarial Robustness

Reference 4

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Observation 58f1ac25-47db-4fdc-9508-9d001d73c52f · outbound

This paper cites Certified Adversarial Robustness via Randomized Smoothing.

Adversarial Examples Are Not Bugs, They Are Superposition Certified Adversarial Robustness via Randomized Smoothing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:07.749818Z

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Observation 764c036a-ea4e-4261-ae9e-c2685b272670 · outbound

This paper cites Update on how we train saes, 2024.

Adversarial Examples Are Not Bugs, They Are Superposition Update on how we train saes, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:17.972990Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2c851804-4247-479b-8407-5900e1c61080 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Adversarial Examples Are Not Bugs, They Are Superposition Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:07.971877Z

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Observation 5a53a7c6-d77f-4604-ac64-53bbbb46cef9 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Adversarial Examples Are Not Bugs, They Are Superposition Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.037888Z

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Observation 15ecacea-e880-4f7e-9308-1391ca3cd4c2 · outbound

This paper cites Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks.

Adversarial Examples Are Not Bugs, They Are Superposition Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:17.702238Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9880684a-46e0-482d-9837-11a056ba97da · outbound

This paper cites Toy models of superposition.

Adversarial Examples Are Not Bugs, They Are Superposition Toy models of superposition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.246092Z

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Observation 4d97c185-6ae8-4577-93b4-9e7ee1c0d031 · outbound

This paper cites Adversarial Robustness as a Prior for Learned Representations.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial Robustness as a Prior for Learned Representations

Reference 11

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Observation ddb4f7f6-00e2-408e-8f88-38dba0024771 · outbound

This paper cites Do Perceptually Aligned Gradients Imply Adversarial Robustness?.

Adversarial Examples Are Not Bugs, They Are Superposition Do Perceptually Aligned Gradients Imply Adversarial Robustness?

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:17.115636Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d01ab5e8-3054-423a-8899-02c1d07158eb · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Adversarial Examples Are Not Bugs, They Are Superposition Scaling and evaluating sparse autoencoders

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.467653Z

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Observation 26179ab0-82f3-4e02-b058-58983b1725e5 · outbound

This paper cites Adversarial Spheres.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial Spheres

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.551378Z

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Observation f29714ca-fe39-4462-83e1-487976af2830 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Examples Are Not Bugs, They Are Superposition Explaining and Harnessing Adversarial Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.717750Z

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Observation 0093fff1-3713-481d-99f2-90399319a6c0 · outbound

This paper cites On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.

Adversarial Examples Are Not Bugs, They Are Superposition On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.843235Z

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Observation 782b3e4d-0fe7-495d-a1a9-7e285baa4583 · outbound

This paper cites Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach.

Adversarial Examples Are Not Bugs, They Are Superposition Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:16.824963Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1007c1db-b546-41b6-a3b0-9e880399c106 · outbound

This paper cites Model Compression with Adversarial Robustness: A Unified Optimization Framework.

Adversarial Examples Are Not Bugs, They Are Superposition Model Compression with Adversarial Robustness: A Unified Optimization Framework

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:16.619903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c55a55d8-dc7b-4d12-a2b0-4ff95a9e29f8 · outbound

This paper cites Adversarial Examples Are Not Bugs, They Are Features.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial Examples Are Not Bugs, They Are Features

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:09.115309Z

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Observation da5af57a-04e5-4c3c-a4d2-4a9d485e4c8b · outbound

This paper cites Precise Tradeoffs in Adversarial Training for Linear Regression.

Adversarial Examples Are Not Bugs, They Are Superposition Precise Tradeoffs in Adversarial Training for Linear Regression

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:09.199233Z

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Observation 74b07980-0769-4334-9456-d68030490a9e · outbound

This paper cites On the geometry of adversarial examples, 2019.

Adversarial Examples Are Not Bugs, They Are Superposition On the geometry of adversarial examples, 2019

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:56:17.514830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bbeb1611-255c-4c93-b101-c8864291b50d · outbound

This paper cites Adversarial examples in the physical world.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial examples in the physical world

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:09.416841Z

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Observation 27cf724d-8a7c-41bd-a208-225075a4f0a5 · outbound

This paper cites Certified Robustness to Adversarial Examples with Differential Privacy.

Adversarial Examples Are Not Bugs, They Are Superposition Certified Robustness to Adversarial Examples with Differential Privacy

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:09.496889Z

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Observation 061116f1-c2f6-4509-912a-84a1dda4fb5e · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

Adversarial Examples Are Not Bugs, They Are Superposition Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:09.587170Z

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Observation bd16b24d-fddc-4a11-ba82-76cb091e5f92 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Adversarial Examples Are Not Bugs, They Are Superposition Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:09.714336Z

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Observation d47158ba-a285-4746-b0cc-5707ba6776d6 · outbound

This paper cites The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure.

Adversarial Examples Are Not Bugs, They Are Superposition The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T16:56:16.215125Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eefdf447-c9df-4793-b990-a7492f744c03 · outbound

This paper cites Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness.

Adversarial Examples Are Not Bugs, They Are Superposition Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:15.872373Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 44086cb0-4c78-49d0-8735-87857003c3dc · outbound

This paper cites DeepFool: a simple and accurate method to fool deep neural networks.

Adversarial Examples Are Not Bugs, They Are Superposition DeepFool: a simple and accurate method to fool deep neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:10.125225Z

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Observation 57fcbade-8bdd-4184-af65-6cd56ff6eeba · outbound

This paper cites Universal adversarial perturbations.

Adversarial Examples Are Not Bugs, They Are Superposition Universal adversarial perturbations

Reference 29

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Observation 752ad884-7d36-4961-86e5-88d7a13b755b · outbound

This paper cites Understanding and Mitigating the Tradeoff Between Robustness and Accuracy.

Adversarial Examples Are Not Bugs, They Are Superposition Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:10.298809Z

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Observation 9164301c-ab99-47e1-b358-064705929c50 · outbound

This paper cites Overfitting in adversarially robust deep learning.

Adversarial Examples Are Not Bugs, They Are Superposition Overfitting in adversarially robust deep learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:10.384931Z

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Observation 27839f00-b087-4cf8-86ef-d1a696cd8c05 · outbound

This paper cites Berg, and Li Fei-Fei.

Adversarial Examples Are Not Bugs, They Are Superposition Berg, and Li Fei-Fei

Reference 32

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Observation 6ca28d03-b333-491b-b406-e34987a0453f · outbound

This paper cites Do Adversarially Robust ImageNet Models Transfer Better?.

Adversarial Examples Are Not Bugs, They Are Superposition Do Adversarially Robust ImageNet Models Transfer Better?

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:15.614207Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8b1bafbc-b571-44ce-84e5-f34b88de9d2d · outbound

This paper cites Adversarially Robust Generalization Requires More Data.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarially Robust Generalization Requires More Data

Reference 34

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Observation 7ff25e2c-9482-4a75-9e03-e79a294ca46d · outbound

This paper cites Adversarial Training for Free!.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial Training for Free!

Reference 35

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Observation a2b5cbb6-a3b5-4c2f-803b-8f46912ef695 · outbound

This paper cites Are adversarial examples inevitable?.

Adversarial Examples Are Not Bugs, They Are Superposition Are adversarial examples inevitable?

Reference 36

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T16:56:15.238290Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 57200a7f-d426-47ad-9be1-63678b0f5843 · outbound

This paper cites The Dimpled Manifold Model of Adversarial Examples in Machine Learning.

Adversarial Examples Are Not Bugs, They Are Superposition The Dimpled Manifold Model of Adversarial Examples in Machine Learning

Reference 37

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Observation 7ad75d55-42ef-4169-839b-aeecbed723e0 · outbound

This paper cites On the Effectiveness of Low Frequency Perturbations.

Adversarial Examples Are Not Bugs, They Are Superposition On the Effectiveness of Low Frequency Perturbations

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:14.899377Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0437336b-46b0-4ab7-92ae-44a8c3a135b7 · outbound

This paper cites Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness.

Adversarial Examples Are Not Bugs, They Are Superposition Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:14.651892Z

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Observation 7d55bcb9-87e8-4f14-a26d-896d24bec710 · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Examples Are Not Bugs, They Are Superposition Intriguing properties of neural networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:11.394170Z

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Observation 6a4799a9-4f35-4263-a535-dd0b5bbdce52 · outbound

This paper cites A high dimensional statistical model for adversarial training: Geometry and trade-offs, 2024.

Adversarial Examples Are Not Bugs, They Are Superposition A high dimensional statistical model for adversarial training: Geometry and trade-offs, 2024

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:11.458028Z

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Observation 70d771ee-da9a-45d3-ad75-47c728a8b011 · outbound

This paper cites Daniel Freeman, Theodore R.

Adversarial Examples Are Not Bugs, They Are Superposition Daniel Freeman, Theodore R

Reference 42

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Observation 0e7010dd-8347-47db-9335-f0b3aa1d652d · outbound

This paper cites Robustness May Be at Odds with Accuracy.

Adversarial Examples Are Not Bugs, They Are Superposition Robustness May Be at Odds with Accuracy

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:11.745058Z

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Observation cf67916c-72ba-4f89-847c-4e87ed84836f · outbound

This paper cites Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification.

Adversarial Examples Are Not Bugs, They Are Superposition Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:11.805323Z

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Observation 85996b4e-c02a-4b74-a7df-89ad10c763b8 · outbound

This paper cites Understanding and Enhancing the Transferability of Adversarial Examples.

Adversarial Examples Are Not Bugs, They Are Superposition Understanding and Enhancing the Transferability of Adversarial Examples

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:14.289035Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 765bc59a-9187-4bda-b67f-45bea783dc04 · outbound

This paper cites Understanding Adversarial Robustness Against On-manifold Adversarial Examples.

Adversarial Examples Are Not Bugs, They Are Superposition Understanding Adversarial Robustness Against On-manifold Adversarial Examples

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:14.049138Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8de7b180-14d1-4af3-a4f0-c882317186fb · outbound

This paper cites An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers.

Adversarial Examples Are Not Bugs, They Are Superposition An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:13.845483Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 03102df4-9a61-4df2-8d55-bfd18913ffec · outbound

This paper cites Adversarial robustness through disentangled representations.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial robustness through disentangled representations

Reference 48

Resolution: verified exact; doi, observed 2026-08-05T16:56:13.074818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99db1ee9-ba91-4c17-90c0-bda175cd3b98 · outbound

This paper cites Adversarial Robustness vs Model Compression, or Both?.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial Robustness vs Model Compression, or Both?

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:13.605861Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8fe7d1b4-58d7-478e-a0a1-eb972bc06266 · outbound

This paper cites Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning.

Adversarial Examples Are Not Bugs, They Are Superposition Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:56:13.335544Z

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Observation 03e9077a-4d79-4759-8908-337a8db934dc · outbound

This paper cites Theoretically Principled Trade-off between Robustness and Accuracy.

Adversarial Examples Are Not Bugs, They Are Superposition Theoretically Principled Trade-off between Robustness and Accuracy

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:12.772410Z

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Observation dda59ec9-e02a-4aee-919b-25ed01ffb630 · outbound

This paper cites Efficient Neural Network Robustness Certification with General Activation Functions.

Adversarial Examples Are Not Bugs, They Are Superposition Efficient Neural Network Robustness Certification with General Activation Functions

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:12.871181Z

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Pith citing papers

Observation 6a64b69b-e988-46bb-9dc3-97384aba4f57 · inbound

Superposition Without Interference? Towards Isolated Interventions via Almost Orthogonal Features in Language Models cites this paper.

Superposition Without Interference? Towards Isolated Interventions via Almost Orthogonal Features in Language Models Adversarial Examples Are Not Bugs, They Are Superposition

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T04:36:01.611166Z

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Observation 5d7e9aaf-2c04-4b6a-944a-5a058c676a9d · inbound

Superposition Without Interference? Towards Isolated Interventions via Almost Orthogonal Features in Language Models cites this paper.

Superposition Without Interference? Towards Isolated Interventions via Almost Orthogonal Features in Language Models Adversarial Examples Are Not Bugs, They Are Superposition

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T06:15:55.665105Z

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