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Paper Citation Record · LEDGER

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.16213.

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

pith.paper-citation-record.v1
2412.16213 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:13:18.590426Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0d157691-cb7b-4a34-b66c-657448e0234c · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 1

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raw_fallback, observed 2026-08-11T13:13:19.042257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation cd3509cf-ff48-44a9-addb-49e4f2856bb7 · outbound

This paper cites Synthesizing Robust Adversarial Examples.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Synthesizing Robust Adversarial Examples

Reference 2

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source=arxiv_source observed=2026-08-11T13:13:18.451104Z digest=sha256:4ac7de4f2e9001231c9c1e5069b83d77fcbc4e3327af7782e1692e15b53d1844

Observation f3d7c113-4aa4-440b-a08b-a30447b9bcc3 · outbound

This paper cites Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

Reference 3

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verified exact
local_arxiv, observed 2026-08-11T13:13:18.786620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.455167Z digest=sha256:863bb2df8b933ea35375ad446d16926c02bd7ffc966a083689b72a83cc3dbb8d

Observation 241e4e68-5e96-4a23-b20f-457e19b444ca · outbound

This paper cites Boosting Adversarial Attacks with Momentum.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Boosting Adversarial Attacks with Momentum

Reference 4

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no resolver link, observed 2026-08-11T13:13:18.459951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.459951Z digest=sha256:51833ca30175d62148c20cef2bfac32e48ec38a657eb7273b29a59ec62723dbd

Observation 3de9ea00-e4ce-4770-ae3d-69b1def32de4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.464684Z digest=sha256:7ca179d05dfb5da617d90fce5e0e1a041fab94bff01e036b60d1f0221ccba69a

Observation e9662292-8edd-46a0-a179-75ad07ab14a9 · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 6

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raw_fallback, observed 2026-08-11T13:13:19.027863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.469555Z digest=sha256:9ad574671b02fd59d0b3b4db21e03c9d6620661487908867278f51cc6efbf8ac

Observation ca6f294b-5500-4dde-9ab7-b2dacd7096e9 · outbound

This paper cites Deep Spatial Autoencoders for Visuomotor Learning.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Deep Spatial Autoencoders for Visuomotor Learning

Reference 7

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source=arxiv_source observed=2026-08-11T13:13:18.474192Z digest=sha256:c7c641a9f240261b02a1810108302c9ea29bdb81af78c4253102525ade34bb7c

Observation bcde7769-85b3-4cdb-819f-e24aff66f0fa · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Explaining and Harnessing Adversarial Examples

Reference 8

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.479277Z digest=sha256:0b003aaaf4a8e63e0c94101dc1118436a272533a43761af5bab157065036e2bd

Observation 66c26f92-5666-49b4-bca4-509470ea4404 · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 9

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raw_fallback, observed 2026-08-11T13:13:19.012368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.487952Z digest=sha256:0d6d13f3d4f024418521c823602532e42b13553fe2d65f17b38f52026442acf7

Observation 11d23801-668f-4d7b-862d-b3ea407321d4 · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.493080Z digest=sha256:698ccb583064a9a101ffe3f05b5137caeefaf256e1c6e3988ed825d0333800a0

Observation 4175eedb-ba3b-47a8-90b6-f7ba8854b81e · outbound

This paper cites Towards Transferable Targeted 3D Adversarial Attack in the Physical World.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Towards Transferable Targeted 3D Adversarial Attack in the Physical World

Reference 11

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source=arxiv_source observed=2026-08-11T13:13:18.496854Z digest=sha256:7fd730679971ba08bd7558f863c1727c008abcaf9ddb06fe5f0268d2714ea692

Observation 428515a0-2775-4ecd-8283-ba33f41e2261 · outbound

This paper cites D.; Wang, Z.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models D.; Wang, Z

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-11T13:13:18.981742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.501699Z digest=sha256:8df4161a5a1afcbbfe765c246717b8db3e9c4565e4e916e49b267a64da2cc945

Observation 802f7954-ed27-4922-8ef7-246900b7c4fd · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.506544Z digest=sha256:43f01766901e2cafca0bb1265883125cc3ba35be82b886bae4a30d6988f2d2e7

Observation 375d866b-7e54-4b62-a38a-4ed4fa586753 · outbound

This paper cites Adversarial examples in the physical world.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Adversarial examples in the physical world

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.510731Z digest=sha256:6ac430485de98a249a215cba80b30b4e3303c6a34847c62e15e08e4ff0f53e86

Observation d2e969cc-bb68-41ae-b99f-79fffde10a47 · outbound

This paper cites Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.515487Z digest=sha256:d921c53628ff3f4693a0da26e63b0adc482ade6fdab9782f58f4e3fb25cca665

Observation 708d5c0a-684b-44dd-8734-b4cdbe1a3d2a · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 16

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raw_fallback, observed 2026-08-11T13:13:18.956589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.519545Z digest=sha256:6e8b347d22c94a26358af15a568b19a2175e6356a2986994403a1e91c2a56730

Observation f259b160-b635-41bd-aca7-68bd6a56cb1a · outbound

This paper cites Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Reference 17

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no resolver link, observed 2026-08-11T13:13:18.523857Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.523857Z digest=sha256:b360a8b61cc92a32728c4d11448e75c0f6dd5333e312705df107fdd4d0ade020

Observation 69afe7f7-2050-454b-8a49-89b22e23353c · outbound

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

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 18

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source=arxiv_source observed=2026-08-11T13:13:18.528756Z digest=sha256:88499d7cc124d925cf5ec243dd2d46fb158bf52a747e988e062d9b1699cfd9f1

Observation df43019d-0f6a-4675-b6c5-2d83ebd2c023 · outbound

This paper cites P.; Tancik, M.; Barron, J.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models P.; Tancik, M.; Barron, J

Reference 19

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no resolver link, observed 2026-08-11T13:13:18.533843Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.533843Z digest=sha256:2a92ad84524d1a140ab711b45c8c4374887564e32ed19d965c1f763dd4955a4b

Observation b57cfa9b-9e9c-432b-97e2-67ef007358dd · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 20

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unresolved
no resolver link, observed 2026-08-11T13:13:18.538615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.538615Z digest=sha256:47455192c030aec19df42b5d3e009e4ee9edee11fd53ae8eaec61015fbe366cf

Observation 2ba2a0d9-3f76-420e-8851-62c72a0a089d · outbound

This paper cites W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al

Reference 21

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source=arxiv_source observed=2026-08-11T13:13:18.543353Z digest=sha256:f0c2d1418766b6214b8c5b01c59ff731196af5201a028c97a247a8cf52767905

Observation 492d4c23-f90c-4871-8705-53818a8791e4 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models SAM 2: Segment Anything in Images and Videos

Reference 22

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no resolver link, observed 2026-08-11T13:13:18.547912Z

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source=arxiv_source observed=2026-08-11T13:13:18.547912Z digest=sha256:06e42ddf9a94b68ce035b4d8be2b881e9288070e6dc24b9ae834c930e675c300

Observation cb5e6082-7cd3-4247-84a1-0ad1ae05a8ee · outbound

This paper cites J.; and Jamali, M.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models J.; and Jamali, M

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-11T13:13:18.914741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.556082Z digest=sha256:f89c476e2d5f64165ed93f5ad8c4035fce14d7a84b523d1f428cb6781836968c

Observation b5edd65b-2a51-4310-9d82-4bb83978ae76 · outbound

This paper cites R.; Mousavi, S.; Ghorbanpour, S.; Gundecha, V.; Gutierrez, R.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models R.; Mousavi, S.; Ghorbanpour, S.; Gundecha, V.; Gutierrez, R

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-11T13:13:18.900287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.560507Z digest=sha256:4224f30fc81897f408d5bdf7f7c85eae6ed3b512cb9696fdb45fa5930064afd3

Observation 784204e0-ce72-4b11-ab1b-1eb1661139ef · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.564624Z digest=sha256:18767d0d5635a04842b23d3e3faef9094a24e5bc782f1156c76f9eceaaf75ce5

Observation 44e45304-e94c-429a-ae6e-15de1c42ea59 · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 26

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no resolver link, observed 2026-08-11T13:13:18.568385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.568385Z digest=sha256:727e23792fba602e4362b2d2685fd54c1e829798c0785d81d9baf89a44c5742c

Observation 4e92a32b-5607-4824-96ee-fbe324e54809 · outbound

This paper cites Adversarial T-shirt! Evading Person Detectors in A Physical World.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Adversarial T-shirt! Evading Person Detectors in A Physical World

Reference 27

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no resolver link, observed 2026-08-11T13:13:18.572004Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.572004Z digest=sha256:76113f3b1c3c32e711a6872033a9102bbf0079fd08c337f7ef48512e15f9bc33

Observation ee85b270-a9e6-4940-8db5-99c7416a1b3b · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 28

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unresolved
raw_fallback, observed 2026-08-11T13:13:18.859204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.576276Z digest=sha256:9d072185adc7af7b1fe1fa776b0ec86a6e1ab53a08b7bf58bfbd82f12ec68997

Observation 3f505dcc-6a5a-4be6-bd2c-f1b438ab005c · outbound

This paper cites an unresolved cited work.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models Unresolved cited work

Reference 29

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raw_fallback, observed 2026-08-11T13:13:18.844225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T13:13:18.580963Z digest=sha256:ec12c35a40297d213685b1f31025b4f15a13ec237b2371ad58aaf503aab4c16f

Observation 395aad5c-5bed-437e-ae06-f924d8eb9e1d · outbound

This paper cites , " * write output.state after.block = add.period write newline.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models , " * write output.state after.block = add.period write newline

Reference 30

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no resolver link, observed 2026-08-11T13:13:18.585513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.585513Z digest=sha256:6685583830e40a793edb2c6e1c9c6c8ed608a388f45d439907a793044e9a3823

Observation b29280b9-a9d8-4789-be04-38d22937c5a1 · outbound

This paper cites write newline.

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models write newline

Reference 31

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:18.590426Z digest=sha256:0f9a0c97011fa47452f616644679f3546f2c1241c514cb18768e1a9a58734a7f

Pith citing papers

No inbound Pith citation observations are available.