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Paper Citation Record · LEDGER

On Evaluating Adversarial Robustness of Large Vision-Language Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2305.16934.

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

pith.paper-citation-record.v1
2305.16934 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:56.073322Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

34
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7442ff59-8a58-4ba6-bee4-88fd924ebde8 · inbound

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models cites this paper.

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-10T20:25:34.436669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-10T20:25:33.854923Z digest=sha256:ee971a83b72188fb016a7187857889a739cc9c321f5df5b974f94c793d9064b1

Observation eb4cc318-4996-4b55-900f-58425f4d006e · inbound

A Survey on Multimodal Large Language Models cites this paper.

A Survey on Multimodal Large Language Models On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 210

Resolution
verified exact
arxiv_id, observed 2026-05-16T02:56:42.252119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-16T02:56:41.658658Z digest=sha256:51abc08a89b660fd1720f2d8092d93492a614995320249331e6fb6119f8d7912

Observation 30e9df8f-a65d-4564-a533-ea4f7e275d56 · inbound

Improved Baselines with Visual Instruction Tuning cites this paper.

Improved Baselines with Visual Instruction Tuning On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-12T19:11:33.995274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-12T19:11:33.783746Z digest=sha256:1a4297436920c228d09777841941ded9bef9b33434cdb2aae9040cc4b086b69c

Observation 6a3186e4-6543-4af0-b472-adabcc4c5372 · inbound

MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI cites this paper.

MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-15T05:37:41.779305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-15T05:37:41.401736Z digest=sha256:caff94ed50837ea44b246867a905307e281e0c479264ea6f74eb93c863593a79

Observation b088cfe2-7099-4481-9238-10f15653e530 · inbound

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment cites this paper.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:56.073322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:56.073322Z digest=sha256:cda6c7959fc7ab352d9a46e96a37f873fdf1762858dca27e20a1b6cf3364cb44

Observation a9546250-14fd-4632-86b1-3034e8ad0d68 · inbound

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models cites this paper.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T12:34:35.513830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:34:35.513830Z digest=sha256:45054af1f1506bc95860d9bc7b2917d2c0ebe3016679a8cee40204d6720aa9cf

Observation 0038e15b-b7a1-4c4b-aaf9-f509da3a0e09 · inbound

Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations cites this paper.

Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 44

Resolution
malformed identifier
no resolver link, observed 2026-08-06T21:07:15.240988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:07:15.240988Z digest=sha256:afc9eac0aa808254241316126bbe46f923746230695e5ec2451857d653898b8c

Observation cda584ab-df6f-45e8-aea3-9c6bf1b0c536 · inbound

Adversarial Video Promotion Against Text-to-Video Retrieval cites this paper.

Adversarial Video Promotion Against Text-to-Video Retrieval On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-19T00:06:55.161030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-19T00:05:07.182361Z digest=sha256:8e84f44edc7024e82c96f6185163a3ef9d5fcb152d9b456c2c04eddbcfcdef70

Observation fc3e027f-41fb-48fd-8285-5f1fd1a19a74 · inbound

VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models cites this paper.

VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T13:45:27.722293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:45:27.722293Z digest=sha256:151f94e92fbb21e0107706c04585144e52c0b24cd596784569e63f3b84ae5c98

Observation e2ddc1d4-7a33-4d9e-9245-0a1b1ecf9675 · inbound

XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs cites this paper.

XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-04T05:42:17.709405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:42:17.709405Z digest=sha256:fcec133c3fa6ecf36fc85b96dd8c45d7617aaf1dfaa2d152891ff167b111f5de

Observation ebf39a73-3632-4b4d-aebc-c712808bb424 · inbound

Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulation cites this paper.

Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulation On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:10:26.155596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-10T13:09:35.407790Z digest=sha256:441b0e8b19d1bcdfd4f7d9fff0de6009945d22dbff61b13eb7b72bc6f6bbcbea

Observation cd69c92f-9e38-4a83-bf85-7cb92161c1c1 · inbound

Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models cites this paper.

Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-06-26T04:38:59.172985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T04:35:51.583460Z digest=sha256:8954e18de9c502f74902efe74748fd154c2a1f9955ecc12c9988309509de64ec

Observation bd6f487a-47b5-48d9-bf04-b42e8b7fbe16 · inbound

Attacking Graph Foundation Models Through Their Shared Representation cites this paper.

Attacking Graph Foundation Models Through Their Shared Representation On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-01T15:05:41.576996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:05:41.576996Z digest=sha256:f1918eb767d6fe6f1b5a1fbda097923f228f9657df33c5b027df5a32f868c8b1