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Paper Citation Record · LEDGER

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration

As of 12 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2505.20362.

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

pith.paper-citation-record.v1
2505.20362 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:13:13.424314Z

measured 39 of 39 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:07:59.063691Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

37 of 37 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 16cada07-72ec-4172-89e3-5591155a726d · outbound

This paper cites online" 'onlinestring :=.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration online" 'onlinestring :=

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:10.437145Z digest=sha256:a5075934f158f8504c4a03846af80c816714fa83d227f02a19199a0ab03eb67c

Observation e7399e71-d3f7-4ebe-a9e2-591d15ea0bfe · outbound

This paper cites write newline.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration write newline

Reference 2

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source=arxiv_source observed=2026-08-07T14:13:10.488504Z digest=sha256:98a7de2c251bfbe7c8b51f983e36a8e5f4eb2c3dbbe413445f5fe13646a7b79b

Observation 81e80564-1a07-4aa5-959f-f837f2bf0819 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-07T14:13:10.541137Z digest=sha256:d467d8486c6dfe8d8150ee2d7a9f6cea22d078eed1e1e886f75829878ad67b7e

Observation 757e901d-a2c2-4422-893d-698d58ec5626 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-07T14:13:10.588227Z digest=sha256:7b553bd2e7264a616bd51efebc56a1a578e0d22f033b264d4ce3cf1a5e6fab01

Observation b6fc06ed-e64e-4e71-898c-be2f8fc1a7f2 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-07T14:13:10.650729Z digest=sha256:0f04f01bb791ed86969c698fdf3d480ab4d89823bd12fe5f75320699d0f6cf4a

Observation 85030306-0453-4f53-ba6c-4f12428412b5 · outbound

This paper cites OR-Bench: An Over-Refusal Benchmark for Large Language Models.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration OR-Bench: An Over-Refusal Benchmark for Large Language Models

Reference 6

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source=arxiv_source observed=2026-08-07T14:13:10.702663Z digest=sha256:16ddf6e76eb2140f50847d9224a5070e437a38150153c510ddb723a2f8f3af99

Observation 53ff340c-4dc8-4e24-b84b-d33585f1cf08 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 7

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raw_fallback, observed 2026-08-07T14:13:14.917559Z

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=2026-08-07T14:13:10.772442Z digest=sha256:5f2eb7501ed0360c3e3fff474c959183df3625813a4e6f2613edb207ccf66e77

Observation 03bc869b-8a66-43da-83aa-dfba094f8186 · outbound

This paper cites Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models

Reference 8

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source=arxiv_source observed=2026-08-07T14:13:10.866739Z digest=sha256:36917b477ee98d1713119a0a1a76a26db88c854be6ecfb013b1711859eb33861

Observation 8b2d501b-3688-4e17-800e-716001b4f071 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-07T14:13:10.973145Z digest=sha256:09b17f7d018ced9f59617c42fb5c958681565552bdf278ed0501acb05596fdec

Observation 4e5d3ddd-d4d4-47c3-b843-058c58bc6c12 · outbound

This paper cites A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models

Reference 10

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source=arxiv_source observed=2026-08-07T14:13:11.081906Z digest=sha256:2dad8eb17144a183625bc071a074073684bbd9cca05bcf9f3f5ce2f600c668ec

Observation 4ff5cd9d-7983-411d-a328-87439cb68029 · outbound

This paper cites FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts

Reference 11

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source=arxiv_source observed=2026-08-07T14:13:11.421412Z digest=sha256:0709bb80f9511c23d36ef456c72f11f67ed7d5ddc40a2b0d0364397e15ef97fd

Observation 77ec74a2-6f50-4222-acfa-3040a92819e5 · outbound

This paper cites Weinberger.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Weinberger

Reference 12

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source=arxiv_source observed=2026-08-07T14:13:11.809646Z digest=sha256:921f3a140fb149c832bfa1f60dc4529987b28030a870b9b830545bc80d743989

Observation d1fc01ec-e71f-4d9e-ae8d-6a71dcc48782 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-08-07T14:13:11.908997Z digest=sha256:a66f9638ede7486532a869f9a645ec18735f69a33b0f02e95f9466e8dfbff78b

Observation 93ea056e-af44-44a8-ac98-0bac0b23ea15 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 14

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unresolved
raw_fallback, observed 2026-08-07T14:13:14.725409Z

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=2026-08-07T14:13:11.982242Z digest=sha256:f7acef58a6d87f2637c9a17730d65442693bfb5d4c7329803a4402ba6afcd44e

Observation f733c6bc-5689-4c0c-bdf6-a59b0f3081f7 · outbound

This paper cites Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update

Reference 15

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source=arxiv_source observed=2026-08-07T14:13:12.116754Z digest=sha256:078abe9e419bcac4d77463d4b23c2990bdd048f33535f1295e6e4edf7c5d71b9

Observation 7ee4b8e1-4130-4073-b438-9dc543485b28 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 16

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raw_fallback, observed 2026-08-07T14:13:14.516486Z

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=2026-08-07T14:13:12.242375Z digest=sha256:d3c14b8853297111e45f4033b4e08feaeb2988abca16d995cda89bba55d04fa5

Observation 2335c84e-1709-42ae-983a-0070b3ad436f · outbound

This paper cites MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?

Reference 17

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source=arxiv_source observed=2026-08-07T14:13:12.324886Z digest=sha256:a7fb63409416deb46c9aea8230515cb476d8fe936dbdb951db9cf571a7f002d7

Observation b9e927ce-9e60-4f0d-9735-9f78c7e2f7cd · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 18

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no resolver link, observed 2026-08-07T14:13:12.382153Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:12.382153Z digest=sha256:9f06b896173034d9f167c74c75f90bd0867aeb2e131b6a0c9f433733b88141e0

Observation 203c9b58-1fa3-4b98-b97e-2fc4cc43a5da · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 19

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no resolver link, observed 2026-08-07T14:13:12.445792Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:12.445792Z digest=sha256:745965607f91b36ebf1013f22b12380b0e420e0f0e5b4be3ae442c49c9a82ec0

Observation 875e0e63-aa43-4f67-8c73-a431ec91be2b · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 20

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:12.523753Z digest=sha256:a333a9b447d0d7ec237cefb8280b47e541d47d0a4847141b07e8e6f1a540421b

Observation 80801dc4-5ec2-4628-a4e1-4087905cf01e · outbound

This paper cites MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

Reference 21

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source=arxiv_source observed=2026-08-07T14:13:12.598656Z digest=sha256:028b2f6f5254f9c330df367cf8895eb05b42020ee154966f152bc0131f6b472a

Observation 792bd359-293c-488e-8682-0908dd3fa762 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 22

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no resolver link, observed 2026-08-07T14:13:12.676280Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:12.676280Z digest=sha256:1b9bf1a396b2efd89f003723283afafa6388b9678de0d032ec86b2263d69357b

Observation 1002c7b8-cfcc-48a8-8348-4ce08de6e7c8 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 23

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no resolver link, observed 2026-08-07T14:13:12.741674Z

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source=arxiv_source observed=2026-08-07T14:13:12.741674Z digest=sha256:1a0c28576535dcd216931c2ef6fdd914a247ba5546b2de3b6cb810d5307d5a7b

Observation 5a140223-9a78-4c85-8926-eea66df72346 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 24

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source=arxiv_source observed=2026-08-07T14:13:12.795863Z digest=sha256:76b140212e4b0f8d03f7d2fe01398bc70b1f718be0646b78d6e878d6815da7c6

Observation 1e142490-ee18-482a-a30c-5fe0c977bc90 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 25

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no resolver link, observed 2026-08-07T14:13:12.863170Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:12.863170Z digest=sha256:975a3dc976ef56b6a8da4d07b3eeb509a2f0937fd538d0838e373810d9d39194

Observation 63b701ce-6a4f-4f2b-9738-b18c1b19f4eb · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-07T14:13:12.908145Z digest=sha256:68b085ac95ad657a82d3d8c16df6c3ba5bfe823fe5e11ccaacfe966d3f7e88e4

Observation 7fa5291e-d46c-4355-9e38-7e4c8b1da5d8 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:12.942631Z digest=sha256:5c85218660057ce862985bb382e12493073f40b511d81d62349ee75cdb686637

Observation 41f1321a-8deb-4492-8f23-43952c081494 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 28

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no resolver link, observed 2026-08-07T14:13:12.983054Z

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source=arxiv_source observed=2026-08-07T14:13:12.983054Z digest=sha256:cdc6b1fdd398e3052fb8b884ede94d6ec05a12ff099fff27183f65a24522be20

Observation fa3a7d77-f41a-4fd5-92b7-229e8ad53492 · outbound

This paper cites Proximal Policy Optimization Algorithms.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Proximal Policy Optimization Algorithms

Reference 29

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no resolver link, observed 2026-08-07T14:13:13.024159Z

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source=arxiv_source observed=2026-08-07T14:13:13.024159Z digest=sha256:c01b7031dfafba33ec1a8ba77451cf5810235df4bc776385a8e0f628b2766fd1

Observation 71808f9f-ce40-4cb8-8fc2-57e03e6fdf05 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 30

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unresolved
raw_fallback, observed 2026-08-07T14:13:14.181489Z

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=2026-08-07T14:13:13.070427Z digest=sha256:f99d353885367589c9d60c1ce4b264c89fc221d8628747e3d78ed20e36d7cc79

Observation 41be5dc0-46e9-49b2-8c65-9274375e4da5 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 31

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no resolver link, observed 2026-08-07T14:13:13.113254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.113254Z digest=sha256:97c8fe19e110191cf13474f3152f62fba84767cec5c977dd5f4459970eabdeed

Observation 20f30d17-d06d-43b2-b242-b28127eaadec · outbound

This paper cites SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model

Reference 33

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no resolver link, observed 2026-08-07T14:13:13.206915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.206915Z digest=sha256:2fc81cc9f2b5665741713ed154d737a8e92425363730c81a621dbe95c875aa29

Observation 148b9c6e-0732-4cc4-a47e-db6afc3f696b · outbound

This paper cites From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks

Reference 34

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no resolver link, observed 2026-08-07T14:13:13.285027Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.285027Z digest=sha256:9487626efbc8cb7f8ff45b343736487dd6ae1f5b7a55824a58a9268400207b2b

Observation d0989e3c-ff71-4b89-8654-906740d5f7b7 · outbound

This paper cites an unresolved cited work.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Unresolved cited work

Reference 35

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no resolver link, observed 2026-08-07T14:13:13.316102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.316102Z digest=sha256:dad43af2963262d3a5083c949f695ca7e27c113a348102089aed11a18a0f079c

Observation 4009b75c-1e8b-4ff0-a64a-96e3bf4449c1 · outbound

This paper cites Multimodal Situational Safety.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Multimodal Situational Safety

Reference 36

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no resolver link, observed 2026-08-07T14:13:13.345759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.345759Z digest=sha256:69f810b60a96e759135cbf12308820f10c3f7d08c5de2285fcb788e9da5fa9ab

Observation ce6b2979-7bd1-4c8c-8804-4e63a1728c37 · outbound

This paper cites Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Reference 37

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no resolver link, observed 2026-08-07T14:13:13.390836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.390836Z digest=sha256:25ba3184ef99a66862af81aafc57bfbadbf1028b63cb71dc023a4f21657f83f1

Observation 776db0e3-14c2-458d-afda-389cd6ad7443 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 38

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unresolved
no resolver link, observed 2026-08-07T14:13:13.424314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:13:13.424314Z digest=sha256:8173eeaa153905dd85adb5424ea4d13e2a78d076a4f5ef9405e21cad041bf809

Pith citing papers

Observation 8ea633dd-8b0e-4bc4-b856-61d281d5cb07 · inbound

Con Instruction: Universal Jailbreaking of Multimodal Large Language Models via Non-Textual Modalities cites this paper.

Con Instruction: Universal Jailbreaking of Multimodal Large Language Models via Non-Textual Modalities VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration

Reference 11

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unresolved
no resolver link, observed 2026-08-07T12:07:59.063691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:07:59.063691Z digest=sha256:90a285d086d9028502f278d463288f64e337b741249d1ff565a4698018fc92e3

Observation be4711d1-9126-4c22-8a4e-94d0c2540188 · inbound

PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails cites this paper.

PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration

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local_arxiv, observed 2026-07-11T01:57:49.597918Z

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source=arxiv_source observed=2026-07-11T01:49:22.500114Z digest=sha256:b6a284f85b250b45b16c125a92b1038258d6be1e6716e1596d993fc45d740b3d