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Paper Citation Record · LEDGER

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies

As of 9 August 2026, this Paper Citation Record lists 100 of 106 outbound references and 4 inbound Pith citation observations for arXiv:2506.24048.

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

pith.paper-citation-record.v1
2506.24048 v1

Coverage vector

measured 100 of 106 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:31:59.479125Z

measured 104 of 104 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T13:31:32.846894Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:16:11.965782Z

Reference resolution

100 of 106 outbound references displayed

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  • verified fuzzy51
  • unresolved40
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External citation measurements

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Outbound references

Observation 4d526672-a09f-4a8c-92fa-04569bfdba9f · outbound

This paper cites Discrete Cosine Transform.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Discrete Cosine Transform

Reference 1

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Observation 6e70f21c-5973-4f18-a08b-fb60a0f62ebb · outbound

This paper cites Natural gradient works efficiently in learning.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Natural gradient works efficiently in learning

Reference 2

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Observation e80f4593-c727-44f1-8e61-54aba70bcea2 · outbound

This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Square attack: a query-efficient black-box adversarial attack via random search

Reference 3

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Observation 8e580c04-dd22-4bd7-94b1-5bfeb23d18f1 · outbound

This paper cites Sorting out Lipschitz function approximation.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Sorting out Lipschitz function approximation

Reference 4

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Observation 8b5ef3a1-8d30-4eab-96e6-606f235d3ac3 · outbound

This paper cites On the Existence of the Adversarial Bayes Classifier (Extended Version).

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies On the Existence of the Adversarial Bayes Classifier (Extended Version)

Reference 5

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Observation 54b184e1-60bc-4fe7-a59c-d0d738f78c21 · outbound

This paper cites Evolutionary computation 1: Basic algorithms and operators.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Evolutionary computation 1: Basic algorithms and operators

Reference 6

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Observation 059b7005-df18-433e-b49c-e848d707c574 · outbound

This paper cites A constrained consensus based optimization algorithm and its application to finance.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A constrained consensus based optimization algorithm and its application to finance

Reference 7

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Observation edf9a586-a392-475d-b3ca-033aef9e8cf7 · outbound

This paper cites CBX: Python and Julia Packages for Consensus-Based Interacting Particle Meth- ods.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies CBX: Python and Julia Packages for Consensus-Based Interacting Particle Meth- ods

Reference 8

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Observation 377a4c53-6a02-4437-b763-db8ff05c4692 · outbound

This paper cites Constrained consensus-based optimization and numerical heuristics for the few particle regime.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Constrained consensus-based optimization and numerical heuristics for the few particle regime

Reference 9

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Observation e95a5cc3-1b62-4cd6-825d-494c3001af62 · outbound

This paper cites A discrete consensus-based global optimization method with noisy objective function.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A discrete consensus-based global optimization method with noisy objective function

Reference 10

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Observation fe120b2d-a975-40d3-9c3a-b0e05dad0bca · outbound

This paper cites Evolution strategies–a comprehensive introduction.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Evolution strategies–a comprehensive introduction

Reference 11

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Observation 4fc13ddd-9687-414d-863f-eaa4edfa4434 · outbound

This paper cites Exploring the Space of Black-box Attacks on Deep Neural Networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Exploring the Space of Black-box Attacks on Deep Neural Networks

Reference 12

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Observation 020c29b9-4344-4212-bfea-e6960d310056 · outbound

This paper cites A Survey of Black-Box Adversarial Attacks on Computer Vision Models.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A Survey of Black-Box Adversarial Attacks on Computer Vision Models

Reference 13

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Observation 5621dde7-fc4b-467d-a603-383a7a5da960 · outbound

This paper cites Consensus-based algorithms for stochastic optimization prob- lems.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Consensus-based algorithms for stochastic optimization prob- lems

Reference 14

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Observation 9c97a8fd-7b74-4e74-82f8-55d522647f98 · outbound

This paper cites Constrained Consensus-Based Optimiza- tion.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Constrained Consensus-Based Optimiza- tion

Reference 15

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Observation 86d9289f-4ecd-49b3-a9cf-82e679081f2a · outbound

This paper cites A particle consensus approach to solving nonconvex-nonconcave min-max problems.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A particle consensus approach to solving nonconvex-nonconcave min-max problems

Reference 16

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Observation d3fddb2f-c963-420b-b20d-ba1ad0567a01 · outbound

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A mean curvature flow arising in adversarial training

Reference 17

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Observation 77ef3670-dec6-498f-b981-52e5e9c3b568 · outbound

This paper cites Polarized consensus-based dynamics for optimization and sampling.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Polarized consensus-based dynamics for optimization and sampling

Reference 18

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Observation 362f91e4-3d03-43f0-a5d2-8e3e470ff783 · outbound

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning

Reference 19

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Observation 0b991505-918c-4ea3-a2df-1437baca459c · outbound

This paper cites CLIP: Cheap Lipschitz training of neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies CLIP: Cheap Lipschitz training of neural networks

Reference 20

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Observation 075cdb57-22b6-45ec-9b62-7ca40480fedd · outbound

This paper cites MirrorCBO: A consensus-based optimization method in the spirit of mirror descent.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies MirrorCBO: A consensus-based optimization method in the spirit of mirror descent

Reference 21

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Observation 896928c7-8af2-4462-8a45-1e72b96405a3 · outbound

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Discrete Consensus-Based Optimization

Reference 22

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This paper cites Towards evaluating the robustness of neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Towards evaluating the robustness of neural networks

Reference 23

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This paper cites An analytical framework for consensus-based global optimization method.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies An analytical framework for consensus-based global optimization method

Reference 24

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Observation cfd28fb5-989c-4f3b-92a6-ffdb6a68e8d8 · outbound

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A consensus-based global optimization method for high dimensional machine learning problems

Reference 25

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Observation 1d93b9c1-7437-406e-a799-bc5a2daabb44 · outbound

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Consensus-based sampling

Reference 26

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Carrillo et al

Reference 27

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Consensus-based Optimization and En- semble Kalman Inversion for Global Optimization Problems with Constraints

Reference 28

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A Consensus-Based Global Optimization Method with Adaptive Momentum Estimation

Reference 29

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Zoo: Zeroth order optimization based black-box attacks to deep neural net- works without training substitute models

Reference 30

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies ImageNet: A large-scale hierarchical image database

Reference 31

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This paper cites There are No Bit Parts for Sign Bits in Black-Box Attacks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies There are No Bit Parts for Sign Bits in Black-Box Attacks

Reference 32

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Certified robustness via dynamic margin maximization and improved lipschitz regularization

Reference 33

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

Reference 34

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Convergence of anisotropic consensus-based optimization in mean-field law

Reference 35

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Consensus-based optimization methods con- verge globally

Reference 36

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Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A pde framework of consensus-based optimization for objectives with multiple global minimizers

Reference 37

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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.

source=pdf_text observed=2026-08-06T21:31:52.056624Z digest=sha256:6a8abcdf3f8cbfdac33ab0e492077e618d402398d5683f30d897b86f4f7d92b7

Observation c0da9ed4-1a75-44cd-a104-ccfe8eb13b0f · outbound

This paper cites Regularity and positivity of solutions of the Consensus-Based Optimization equation: unconditional global convergence.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Regularity and positivity of solutions of the Consensus-Based Optimization equation: unconditional global convergence

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:52.127881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:52.127881Z digest=sha256:5c9fa2516ffcf5ec00f4dd333b1f7c1ec0f95e6d57146f0cfce6015af78e9c4d

Observation becad310-49a9-4329-850d-e2194ddde430 · outbound

This paper cites Consensus-based optimization on hypersurfaces: Well-posedness and mean- field limit.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Consensus-based optimization on hypersurfaces: Well-posedness and mean- field limit

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.517655Z

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.

source=pdf_text observed=2026-08-06T21:31:52.207444Z digest=sha256:c61c9777d669adbc98b1f04c0b723e88d7d0d7f8e3ca52b97c42f5fa8775578c

Observation 7b570553-4421-4bd2-a8dd-916e4e6322b6 · outbound

This paper cites Anisotropic Diffusion in Consensus-Based Optimization on the Sphere.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Anisotropic Diffusion in Consensus-Based Optimization on the Sphere

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.477728Z

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.

source=pdf_text observed=2026-08-06T21:31:52.265690Z digest=sha256:67b0e09587f99a6f41502e8d7654ff6568ce034b6e8f94493638e19b3d94735c

Observation 0ee894a3-a384-4cf0-b76f-61d43f44b72b · outbound

This paper cites CB$^2$O: Consensus-Based Bi-Level Optimization.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies CB$^2$O: Consensus-Based Bi-Level Optimization

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:52.416049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:52.416049Z digest=sha256:b05c4213d39256058f1c0689d055ae61644ba48cc61e592d53ce769b8db3503a

Observation 2d47c84c-d6d1-4fa3-b3da-2630abcad716 · outbound

This paper cites Defending against diverse attacks in federated learning through consensus- based bi-level optimization.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Defending against diverse attacks in federated learning through consensus- based bi-level optimization

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.432207Z

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.

source=pdf_text observed=2026-08-06T21:31:52.513541Z digest=sha256:937706ef6211111ee4b4d1ceb9edb80c064b0ce366c74e0b491ad8c704fbb526

Observation 7acb2d4d-08e6-44ce-8c61-52b8d94128bb · outbound

This paper cites Exponential natural evolution strategies.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Exponential natural evolution strategies

Reference 43

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:52.578761Z digest=sha256:4a095501a1717c7c489958625f1cd5214a79bc3224c954a88c47f639fa90599c

Observation c0aa7f64-4094-4a56-9cdc-30e2f27166c5 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Explaining and Harnessing Adversarial Examples

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:52.646349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:52.646349Z digest=sha256:5a7d6beafccabd58a8a24c9dd1227e078e894ccad157ed40635f88d8e38a294d

Observation d21e5aee-1848-4572-9903-9976ef700db8 · outbound

This paper cites Mean-field particle swarm optimization.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Mean-field particle swarm optimization

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.357431Z

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.

source=pdf_text observed=2026-08-06T21:31:52.719005Z digest=sha256:e0662e859573f1833074b46537eb9c7a3319448956ed39029395b0e189e07818

Observation 01814dc8-84e3-45a4-a985-005038fa14f0 · outbound

This paper cites Simple black-box adversarial attacks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Simple black-box adversarial attacks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.332275Z

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.

source=pdf_text observed=2026-08-06T21:31:52.811860Z digest=sha256:59ca05a25f413492557e2dca2ad8476973f55f8712e258f3e39c3ad516295635

Observation 48fbcb56-52e9-4679-84ab-7fa6a3198137 · outbound

This paper cites The CMA evolution strategy: a comparing review.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies The CMA evolution strategy: a comparing review

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.299409Z

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.

source=pdf_text observed=2026-08-06T21:31:52.883051Z digest=sha256:221d47a99e0734b47f4f57700a3e79a7bb9769338ef4e018231edaf7a94e3df7

Observation 589d26a2-b2cf-4f79-a094-93c71eb7c82c · outbound

This paper cites Completely derandomized self-adaptation in evolution strategies.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Completely derandomized self-adaptation in evolution strategies

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.261774Z

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.

source=pdf_text observed=2026-08-06T21:31:52.990341Z digest=sha256:8da75a80e2f49fa21a5eb135c7dd92fece3ca61f3b20448a10ff28a965307744

Observation de7a9651-ca30-4cb0-bfcd-d57b2496f7df · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on ImageNet clas- sification.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Delving deep into rectifiers: Surpassing human-level performance on ImageNet clas- sification

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.205345Z

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.

source=pdf_text observed=2026-08-06T21:31:53.074258Z digest=sha256:54a7ee4d24eb8baf7afb0d44811ec139b01286e1dc21230f0344cb8a35281047

Observation b3ad64e1-a1d7-4ea7-9454-952f34f501be · outbound

This paper cites Micro-Macro Decomposition of Particle Swarm Optimization Methods.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Micro-Macro Decomposition of Particle Swarm Optimization Methods

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:32:01.172868Z

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.

source=pdf_text observed=2026-08-06T21:31:53.180755Z digest=sha256:b9e9ad8ee380eec338f84cea132118009c437c8798095ca73e3aab4f9e007ddb

Observation fcf5b2ce-ceec-4289-8883-7fa7ddbec4b2 · outbound

This paper cites Consensus-based optimization for saddle point prob- lems.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Consensus-based optimization for saddle point prob- lems

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.157884Z

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.

source=pdf_text observed=2026-08-06T21:31:53.321962Z digest=sha256:ef2da09db0e3e99e71eb39a057c65390ddad577bc5fa7121898cdc0ab9a6c1bf

Observation d9c2aded-c32b-4e2e-8f46-63c99eaba5d1 · outbound

This paper cites Training certifiably robust neural networks with efficient local lipschitz bounds.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Training certifiably robust neural networks with efficient local lipschitz bounds

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.125355Z

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.

source=pdf_text observed=2026-08-06T21:31:53.457050Z digest=sha256:35bd85c42988f6a42abadb1abed070558796d55c402cdcb7855f5a3482045950

Observation 98e0dfdb-c25b-47b4-b67e-3e6bdd8312fb · outbound

This paper cites Evoba: An evolution strategy as a strong baseline for black-box adversarial attacks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Evoba: An evolution strategy as a strong baseline for black-box adversarial attacks

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.090141Z

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.

source=pdf_text observed=2026-08-06T21:31:53.570754Z digest=sha256:0c5cbce572589fc28340d2b7666e89c9d8ee272e8fba319f7ae7ea36212146be

Observation 4ea91fb5-9acb-4fd9-afae-4caff6684a87 · outbound

This paper cites Prior convictions: Black-box adversarial attacks with bandits and priors.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Prior convictions: Black-box adversarial attacks with bandits and priors

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.045646Z

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.

source=pdf_text observed=2026-08-06T21:31:53.669098Z digest=sha256:0032a929e1c5e14b3afa5e64a01930fb312b87c9b4684781581da26595643fff

Observation a084afd5-d2a5-4659-8473-5145c87ea5a6 · outbound

This paper cites Black-box adversarial attacks with limited queries and information.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Black-box adversarial attacks with limited queries and information

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:07.003957Z

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.

source=pdf_text observed=2026-08-06T21:31:53.762858Z digest=sha256:709e5821e09e7b44493efa0325c769e79926842fd22bdee43b0781a43323a7af

Observation 36254b46-615a-4cbf-b288-335c36be5242 · outbound

This paper cites The discrete cosine transform (DCT): theory and application.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies The discrete cosine transform (DCT): theory and application

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.970269Z

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.

source=pdf_text observed=2026-08-06T21:31:53.876894Z digest=sha256:84a39daf68f580c47142cbb5dc05456e734d28add37162f07d3649b16e0e20c9

Observation 1732a3e7-3260-4e10-b9f9-45244df3dc9d · outbound

This paper cites Convergence analysis of the discrete consensus-based optimization algorithm with random batch interactions and heterogeneous noises.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Convergence analysis of the discrete consensus-based optimization algorithm with random batch interactions and heterogeneous noises

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.950078Z

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.

source=pdf_text observed=2026-08-06T21:31:53.973246Z digest=sha256:9000c1ddbfb8863285e67115d15fe44ea1c79152d982434e8438af48c653fe5d

Observation 294a47fd-8ffe-4d6c-81bf-3635931c37b6 · outbound

This paper cites One weird trick for parallelizing convolutional neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies One weird trick for parallelizing convolutional neural networks

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:54.085392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:54.085392Z digest=sha256:5636f9fd9d4e71f81ef0ec8c256121ecdf7b4219b619a7700f91f28f585037d7

Observation 1905a8fb-27a7-4021-b7d2-77053e21ad2a · outbound

This paper cites Learning multiple layers of features from tiny images.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Learning multiple layers of features from tiny images

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.913143Z

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.

source=pdf_text observed=2026-08-06T21:31:54.179640Z digest=sha256:2d3907bb9ae77f752babe7221db5156fa8bab000d873e62dd381d2c50169138f

Observation 565a2c9c-d4bf-4ba6-95ea-35dba096223a · outbound

This paper cites ImageNet classification with deep convo- lutional neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies ImageNet classification with deep convo- lutional neural networks

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.868457Z

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.

source=pdf_text observed=2026-08-06T21:31:54.285544Z digest=sha256:26dcf855d1041fd781f54a54c989d2b42b46a8d7d945bf6ef22f2aceae26e3f5

Observation 05db5baa-be2d-467f-8528-2dfd43318cbf · outbound

This paper cites Gradient-based learning applied to document recognition.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Gradient-based learning applied to document recognition

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.829889Z

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.

source=pdf_text observed=2026-08-06T21:31:54.406763Z digest=sha256:e596863c8b7f494ac037042290eb812492e6a73585ad241e3a4ee4cd9be5f06b

Observation 97f85b7d-5f46-42d9-be59-21b1c60c1aa6 · outbound

This paper cites Visualizing the loss landscape of neural nets.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Visualizing the loss landscape of neural nets

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.795671Z

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.

source=pdf_text observed=2026-08-06T21:31:54.475145Z digest=sha256:7a62b36e620b6fe14cd906ed6dc82563d214f9366edea687a16eac87c731bb6b

Observation 5bacac52-2923-4eae-af0b-637132b38fa8 · outbound

This paper cites Nattack: Learning the distributions of adversarial examples for an improved black- box attack on deep neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Nattack: Learning the distributions of adversarial examples for an improved black- box attack on deep neural networks

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.751831Z

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.

source=pdf_text observed=2026-08-06T21:31:54.550971Z digest=sha256:7aa7059e426754c666fb4806704badbc518f0b9e5d34e0703b60b86737d9caa2

Observation 8926b1f7-1027-4255-abf7-c87c99447659 · outbound

This paper cites Stein variational gradient descent: A general purpose bayesian inference algorithm.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Stein variational gradient descent: A general purpose bayesian inference algorithm

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.717109Z

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.

source=pdf_text observed=2026-08-06T21:31:54.649141Z digest=sha256:10cc5d33e836b1707046bd698c3585512350525e165f4b362dc7d8166a30cb41

Observation 0ee0909f-78ed-415e-90fb-01babd51c159 · outbound

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

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:54.783773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:54.783773Z digest=sha256:bd1a5b0cbfffe620107ba94c7ddc41a0ecf8ce73a9489758792af1eeb1dc1c3a

Observation 723179bb-598e-4f38-9794-8f808a30aee4 · outbound

This paper cites MNIST Challenge.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies MNIST Challenge

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.683714Z

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.

source=pdf_text observed=2026-08-06T21:31:54.895459Z digest=sha256:0059114bbd525d9f7ed3a0f55733d15ad2d1c28c70edb7827606ed16c368d4f7

Observation 6db8c6f5-95b4-4f93-965d-ebe43d99373c · outbound

This paper cites Back in black: A comparative evaluation of recent state-of-the-art black-box attacks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Back in black: A comparative evaluation of recent state-of-the-art black-box attacks

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.651611Z

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.

source=pdf_text observed=2026-08-06T21:31:55.025429Z digest=sha256:14dca78e7f138cfd0c954710b536a28de77ef894b7cef01ec2a3bbaceb6debcb

Observation 72f14620-b2e6-4b27-b9a1-8fb03f38e422 · outbound

This paper cites Yet another but more efficient black-box adversarial attack: tiling and evolution strategies.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Yet another but more efficient black-box adversarial attack: tiling and evolution strategies

Reference 68

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:32:00.903109Z

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.

source=pdf_text observed=2026-08-06T21:31:55.161802Z digest=sha256:6cdad05c145bd279df3dfb35d8fec753f03139f8d9287393b64a29f6ed9c94ce

Observation b0368569-6f89-47ca-83c3-d766e7be4fb7 · outbound

This paper cites Parsimonious black-box adversarial attacks via efficient combinatorial optimization.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Parsimonious black-box adversarial attacks via efficient combinatorial optimization

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.618064Z

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.

source=pdf_text observed=2026-08-06T21:31:55.268272Z digest=sha256:8d2a2c7d47c95a78102e24fb72f20c9930decc0a83bd06b8d19813a496051e08

Observation 680d52fe-480e-4965-ab3d-a8b7859f1c2c · outbound

This paper cites Information-geometric optimization algorithms: A unifying picture via invariance principles.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Information-geometric optimization algorithms: A unifying picture via invariance principles

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.574603Z

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.

source=pdf_text observed=2026-08-06T21:31:55.379898Z digest=sha256:172d75eb49a7ddf8df04ccf3bccd356cbadc258517108fb69f36811404e8335b

Observation b1e042f2-8d21-4670-96b9-09ec638f3a0d · outbound

This paper cites Practical black-box attacks against machine learning.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Practical black-box attacks against machine learning

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.524944Z

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.

source=pdf_text observed=2026-08-06T21:31:55.476509Z digest=sha256:7f87ee5889c9457654a0227ce464d1e5ab079e7cdd4054418e28c5bb991a091a

Observation 5c73ff39-9ab2-4636-9ba2-6a222af90121 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:55.557174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:55.557174Z digest=sha256:ce3ce7e532a61769fd52a61115742efe74e110690093244e8d1d6ae42d457347

Observation 4cda50ca-f103-45e1-b529-145556dbe222 · outbound

This paper cites A consensus-based model for global optimization and its mean-field limit.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies A consensus-based model for global optimization and its mean-field limit

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.487509Z

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.

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Observation 278c6e52-95cf-4d17-a348-513c3ed54984 · outbound

This paper cites Black-box adversarial attacks using evolution strategies.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Black-box adversarial attacks using evolution strategies

Reference 74

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:55.790959Z digest=sha256:5b69d3f4fa22e3f3449e8ef50b4fb3dd448bb6b2ffb63b543b8d5ea15cbaf97a

Observation 43198818-5d39-4b1d-8203-f4e02b21220f · outbound

This paper cites Rapin and O.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Rapin and O

Reference 75

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:55.920547Z digest=sha256:368b0e914421a22fd4de638875b999589c056d3b1f210dc88058207288227674

Observation baee5fd8-e587-4261-8ecc-05f9d3452bd9 · outbound

This paper cites Evolutionsstrategien.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Evolutionsstrategien

Reference 76

Resolution
verified fuzzy
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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.

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Observation 96f0ac1e-4e6c-4ded-bfdb-52bf6c783c19 · outbound

This paper cites Leveraging memory effects and gradient information in consensus-based optimi- sation: On global convergence in mean-field law.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Leveraging memory effects and gradient information in consensus-based optimi- sation: On global convergence in mean-field law

Reference 77

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:56.171014Z digest=sha256:025a7a95fdbeb86cf7709e45cdb4172e6a21f4ec54b61d7e8ed27b5f839289aa

Observation 92b9864e-4552-4ab3-a804-53db771c3fa5 · outbound

This paper cites Mathematical Foundations of Interacting Multi-Particle Systems for Optimiza- tion.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Mathematical Foundations of Interacting Multi-Particle Systems for Optimiza- tion

Reference 78

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:56.267893Z digest=sha256:fc1b1e3ff2cceecbf358de046ef5a2e8ede819f5d2531fdc01b3de3dd0414b17

Observation bdc0c880-3f7c-4765-8a24-6ea49a40c2f4 · outbound

This paper cites Gradient is All You Need? 2023.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Gradient is All You Need? 2023

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:56.369052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:56.369052Z digest=sha256:beeeeba9922d858f0b4ae28c07f2994d986c949a7764f632d6958f0ae0bf39b7

Observation d2af60a9-644a-41ed-b1f9-3bb994531b29 · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:56.471420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:56.471420Z digest=sha256:0ce76a6bdde65eca06d2aa85b897b8409026bb67c890fec1d6f83ea510cbad1e

Observation 45854405-d1d8-4d1f-b23c-3a3ef3e920bd · outbound

This paper cites Natural evolution strategies converge on sphere functions.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Natural evolution strategies converge on sphere functions

Reference 81

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:56.578665Z digest=sha256:fde75b06975cd14cac4d1331cffa321ccd8e21a8c6d0e87a6d86d609ac77b683

Observation 6e01bcff-c473-4f0e-8a84-21f48622ede5 · outbound

This paper cites Soft prompt threats: Attacking safety alignment and unlearning in open-source llms through the embedding space.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Soft prompt threats: Attacking safety alignment and unlearning in open-source llms through the embedding space

Reference 82

Resolution
verified fuzzy
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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.

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Observation ecb3e5bc-80a5-4423-9a0c-96d1f77e4580 · outbound

This paper cites Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:06.033229Z

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.

source=pdf_text observed=2026-08-06T21:31:56.946317Z digest=sha256:d6463346f3e21441abd94474fc1b17fe87be6b38b905709a343a82c2b4cc26c4

Observation cd7ab500-e37b-4f64-acbf-2e115615831b · outbound

This paper cites Simple and efficient hard label black-box adversarial attacks in low query budget regimes.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Simple and efficient hard label black-box adversarial attacks in low query budget regimes

Reference 84

Resolution
verified fuzzy
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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.

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Observation 59609b39-65e5-46ef-9c7b-dbd9daf09a20 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 85

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:57.247798Z digest=sha256:3b3c3edb6dc102874f615c38d598303ef7451b97ea92ba1de1054a91ed0aa571

Observation fa17a98d-c7a1-41ac-a4f8-33f6c575351e · outbound

This paper cites Evolutionary algorithms and their applications to engineering problems.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Evolutionary algorithms and their applications to engineering problems

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:05.510496Z

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.

source=pdf_text observed=2026-08-06T21:31:57.440495Z digest=sha256:eef3be3c2343383970560308ad407f5caae0bbb0af126fb5ecb05cecbee7ad5f

Observation 40631b71-44d2-477b-91b4-962bb0d8c15a · outbound

This paper cites One pixel attack for fooling deep neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies One pixel attack for fooling deep neural networks

Reference 87

Resolution
verified fuzzy
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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.

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Observation cc032739-2ecc-44e4-80ee-56a2d2f7450d · outbound

This paper cites Sok: Pitfalls in evaluating black-box attacks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Sok: Pitfalls in evaluating black-box attacks

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.801162Z

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.

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Observation 923d94f4-0f83-4a0e-b788-6661215c153a · outbound

This paper cites Intriguing properties of neural networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Intriguing properties of neural networks

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:57.836186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:57.836186Z digest=sha256:5edafe7bc7fd957025637bff70008bcf8943cc71f7ec7f30db6cf611ed27bb23

Observation 5b5ddca7-4b72-43ef-86cc-1f927bf1986f · outbound

This paper cites Rethinking the inception architecture for computer vision.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Rethinking the inception architecture for computer vision

Reference 90

Resolution
verified fuzzy
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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.

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Observation ce36519c-2170-41ba-803f-f1889671b97a · outbound

This paper cites An optimal transport approach for computing adversarial training lower bounds in multiclass classification.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies An optimal transport approach for computing adversarial training lower bounds in multiclass classification

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.469591Z

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.

source=pdf_text observed=2026-08-06T21:31:58.172318Z digest=sha256:02fc51f891e1227886b8944616e32e9fb075f42757937ffb2d0575d2f224750d

Observation 3a843753-c1d4-4fb6-83bf-8bf4bbcdac59 · outbound

This paper cites The multimarginal optimal transport formu- lation of adversarial multiclass classification.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies The multimarginal optimal transport formu- lation of adversarial multiclass classification

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.340715Z

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.

source=pdf_text observed=2026-08-06T21:31:58.341503Z digest=sha256:fc3ac5185069fd77a52dc4f92be4a31745e150e21bdcdcc9f84faf774fb6f219

Observation f54c6ace-5a36-438a-ac49-425567a30b11 · outbound

This paper cites Black-Box Adversarial Attacks on Deep Neural Networks: A Survey.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Black-Box Adversarial Attacks on Deep Neural Networks: A Survey

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.308347Z

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.

source=pdf_text observed=2026-08-06T21:31:58.503838Z digest=sha256:b4d18c34f130df8b491f0f2f13751057ddf2528a01b00f31844660b75b02f04e

Observation c579e225-8520-42d7-861e-d95f99d6d2b6 · outbound

This paper cites Mathematical Analysis of the PDE Model for the Consensus-based Optimization.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Mathematical Analysis of the PDE Model for the Consensus-based Optimization

Reference 94

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:32:00.423366Z

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.

source=pdf_text observed=2026-08-06T21:31:58.646313Z digest=sha256:d042dfad06f3aca2609e79bfbcd0bc318ffe8d28e913290c0488b19e172b5bc0

Observation 48f54652-7410-4fe0-ac40-20f11cac1354 · outbound

This paper cites Adversarial flows: A gradient flow characterization of adversarial attacks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Adversarial flows: A gradient flow characterization of adversarial attacks

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-06T21:31:58.832095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:58.832095Z digest=sha256:c9b26e6d6df24a2300bee0acd224b9bc7a0bd2a110b0efd9aa0c11438e24b38a

Observation 7c7d21c2-c9c1-4736-aa86-133676c182ac · outbound

This paper cites Natural evolution strategies.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Natural evolution strategies

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.255205Z

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.

source=pdf_text observed=2026-08-06T21:31:59.009251Z digest=sha256:d0855d5b7b26a38d6792a309f4f86f4ce69ecb76aa706ef3e3721d2059f20622

Observation 0186aeee-976a-4ff8-9d2d-df28e3a06eb7 · outbound

This paper cites Generating adversarial examples with adversarial networks.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Generating adversarial examples with adversarial networks

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.211986Z

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.

source=pdf_text observed=2026-08-06T21:31:59.133437Z digest=sha256:c592161a056a25f6f328c12ad9a605260b9f6bcb47b6b697fef52f74be4f8bcf

Observation 81287f03-30d8-4051-832f-8d41943a5635 · outbound

This paper cites Fast Evolutionary Programming.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Fast Evolutionary Programming

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.172434Z

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.

source=pdf_text observed=2026-08-06T21:31:59.269437Z digest=sha256:57ae71b20e954598e373d6f26d2292748c2fbd6f2d461493e5f232fa496d15c1

Observation d6c15d30-28b3-4474-96dc-b13ebae564ec · outbound

This paper cites Rethinking lipschitz neural networks and certified robustness: A boolean func- tion perspective.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Rethinking lipschitz neural networks and certified robustness: A boolean func- tion perspective

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:32:04.141052Z

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.

source=pdf_text observed=2026-08-06T21:31:59.398182Z digest=sha256:197626b79ae6f03fd8c3a0e4ef26489acbb2b5cc98fcbe40c358937ce0f862c5

Observation 88f958ce-e5e7-4701-a8ea-fe184c33f51c · outbound

This paper cites Towards query-efficient black-box adversary with zeroth-order natural gradient de- scent.

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies Towards query-efficient black-box adversary with zeroth-order natural gradient de- scent

Reference 100

Resolution
verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:31:59.479125Z digest=sha256:e6c32a598412a9f21458ebb2ed34f31854f6a49258dec2310574ec6b2513b802

Pith citing papers

Observation c4967be6-d834-43ac-803e-0d8348d14445 · inbound

Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization cites this paper.

Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies

Reference 177

Resolution
verified exact
arxiv_id, observed 2026-05-20T04:43:03.637842Z

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.

source=arxiv_source observed=2026-05-20T04:41:01.326216Z digest=sha256:b2e903df86d6525f1fa4c8a6444f3254325101f2373b3027b15fe1cc03585698

Observation e1701237-7dfb-4888-8d65-1652d3916cf8 · inbound

From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy cites this paper.

From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-06-30T18:04:58.227173Z

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.

source=pdf_text observed=2026-06-30T17:59:31.213100Z digest=sha256:60cdeb7cfb46009b8102d6cdade6159a92ed5619f8185f561ce799e52ef8f7ae

Observation 4a30d099-6c1d-4e62-bf66-64896d85510e · inbound

A derivative-free particle method for optimization in Hilbert spaces cites this paper.

A derivative-free particle method for optimization in Hilbert spaces Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-07-01T20:16:11.967230Z

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.

source=pdf_text observed=2026-06-28T21:25:06.907517Z digest=sha256:820cadf8e4efa2a9fd3b038f55f11a57d2914b55437e945d5fd5db3669146d1d

Observation 8646f045-5bc7-476c-9f46-bfd6802756da · inbound

Exploiting Structure with Anisotropic Consensus-Based Optimization cites this paper.

Exploiting Structure with Anisotropic Consensus-Based Optimization Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies

Reference 88

Resolution
unresolved
no resolver link, observed 2026-07-14T13:31:32.846894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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