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Paper Citation Record · LEDGER

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure

As of 22 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2607.21151.

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

pith.paper-citation-record.v1
2607.21151 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:23:31.950259Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

65 of 65 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved64
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 523e1f8d-aaa2-4679-8667-bb6b4fc3243e · outbound

This paper cites Qwen3-vl technical report,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Qwen3-vl technical report,

Reference 1

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source=pdf_text observed=2026-08-01T08:23:24.265213Z digest=sha256:aa1fe16a478964bcb896943cefcaaaf2a9d3d8cb749f7c3076b50fea407cd82b

Observation 0cec7b8c-3c74-45d4-8161-880fdb5788a6 · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 2

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source=pdf_text observed=2026-08-01T08:23:24.414167Z digest=sha256:fc5fe1cea6a684a630436f65edef2223f9cdbe57c09c18be358d7ab1682a9684

Observation ff3267ed-70a9-484c-bfc9-b1e7b5a9304d · outbound

This paper cites InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling

Reference 3

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source=pdf_text observed=2026-08-01T08:23:24.495566Z digest=sha256:b303ffea0ed024f16047e5a0d9a1d30ba11e32f9d17d602e276745b9b3aca51e

Observation f1501e31-11e0-4751-b687-4403fe63d4a1 · outbound

This paper cites Vision-language models for vision tasks: A survey,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Vision-language models for vision tasks: A survey,

Reference 4

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source=pdf_text observed=2026-08-01T08:23:24.732526Z digest=sha256:961adb6c9822cf7c0279f51d69ddddfac32bd9012d6325db5cb8a7e3f1c74603

Observation 0e45cb66-a857-409a-996d-155224d02cf1 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 5

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source=pdf_text observed=2026-08-01T08:23:24.901910Z digest=sha256:2277691b4a7343f80119508df8ff5aebc6aaeb7c2d45a7200f130af58abca292

Observation d54a7677-0c02-45c0-a740-7977675c926f · outbound

This paper cites Safety of Multimodal Large Language Models on Images and Texts.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Safety of Multimodal Large Language Models on Images and Texts

Reference 6

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source=pdf_text observed=2026-08-01T08:23:25.032757Z digest=sha256:b371da1e669cce6535d3130e6a67a9a5dcd63482f92c912b7dbad37a15749087

Observation a4b7d2bc-1709-4d4c-a6fd-1c95fbc5f6b3 · outbound

This paper cites Jailbreak Attacks and Defenses against Multimodal Generative Models: A Survey.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Jailbreak Attacks and Defenses against Multimodal Generative Models: A Survey

Reference 7

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source=pdf_text observed=2026-08-01T08:23:25.193531Z digest=sha256:b82770299e3b0909ea4a3acea0e9ec6d929a2e1e724fd7fe57e52e86da1f8825

Observation 4f1ed196-4b14-46ee-ab10-788259ce4909 · outbound

This paper cites Vlsbench: Unveiling visual leakage in multimodal safety,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Vlsbench: Unveiling visual leakage in multimodal safety,

Reference 8

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source=pdf_text observed=2026-08-01T08:23:25.333115Z digest=sha256:7c2e8cb28f2aa4e337f4e33da35f89b7c34a65ea1c3b964633596d0aa3da2f7b

Observation 4f03dbc6-603b-4ded-b8bf-e21a660b6bc2 · outbound

This paper cites Video-safetybench: A benchmark for safety evaluation of video lvlms,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Video-safetybench: A benchmark for safety evaluation of video lvlms,

Reference 9

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source=pdf_text observed=2026-08-01T08:23:25.472715Z digest=sha256:61b15d4f831a9d7dad0f2b6136ce30fb53934a1e6c1a9843ec1b767ac8a0a8c7

Observation 0a720c0d-0f09-49df-ac44-d317cfab5980 · outbound

This paper cites Sea: Low-resource safety alignment for multimodal large language models via synthetic embeddings,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Sea: Low-resource safety alignment for multimodal large language models via synthetic embeddings,

Reference 10

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source=pdf_text observed=2026-08-01T08:23:25.646133Z digest=sha256:1fa11cda4a95cdd4f818d0385beb5df76c5b956ffd20fec514161814a62d706d

Observation e8ace07f-185f-4fa4-83a5-e562128e3cc6 · outbound

This paper cites Figstep: Jail- breaking large vision-language models via typographic visual prompts,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Figstep: Jail- breaking large vision-language models via typographic visual prompts,

Reference 11

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source=pdf_text observed=2026-08-01T08:23:25.779852Z digest=sha256:345f69132f2453f1c370e6b64fa96ea48433c92d7650aedbf5c314737120834d

Observation 8d8a2ab6-d404-41d3-971f-ace533de2ab2 · outbound

This paper cites Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character

Reference 12

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source=pdf_text observed=2026-08-01T08:23:25.911168Z digest=sha256:4e9af348cf360a0b78a42099529a512bd9a684e391649d6a3ae8d9dd081fe636

Observation 40ece37e-0c78-405a-99c1-3cbaea5b3982 · outbound

This paper cites Ideator: Jailbreaking and benchmarking large vision-language models using them- selves,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Ideator: Jailbreaking and benchmarking large vision-language models using them- selves,

Reference 13

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source=pdf_text observed=2026-08-01T08:23:26.010574Z digest=sha256:5b4dd1e8d78d4721f6a92a4144feaa1a8eb902d6cfe885417fd56babdaf5505a

Observation 1291fbc4-d084-4448-9111-d280c0f35c09 · outbound

This paper cites JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks

Reference 14

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source=pdf_text observed=2026-08-01T08:23:26.121407Z digest=sha256:bf23001249fd6ec5b81fa627a9d7d6c95e753bfbd092214e736f175c9743b9d3

Observation 04e745c3-3952-4397-a67f-fd6de91ff9a3 · outbound

This paper cites $\textit{MMJ-Bench}$: A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure $\textit{MMJ-Bench}$: A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models

Reference 15

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Observation 8589873e-2a79-4293-8974-7c5712661bb7 · outbound

This paper cites Omni-SafetyBench: A benchmark for safety evaluation of audio-visual large language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Omni-SafetyBench: A benchmark for safety evaluation of audio-visual large language models,

Reference 16

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source=pdf_text observed=2026-08-01T08:23:26.442624Z digest=sha256:8e710dae00620da3efd6e3b67fd6def9d92233c181eab22ee7394b8a05b7e671

Observation 0799568d-d601-4bdb-b40f-01e8d412a9d1 · outbound

This paper cites Videostir: Understanding long videos via spatio-temporally structured and intent-aware rag,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Videostir: Understanding long videos via spatio-temporally structured and intent-aware rag,

Reference 17

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source=pdf_text observed=2026-08-01T08:23:26.575880Z digest=sha256:eb162d1d9898fe36291d430d7a6e5ccbfdbbabd0596aeae10ddc730aa7797648

Observation 24f3724e-cae4-4827-bc03-a84c4437508a · outbound

This paper cites CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning

Reference 18

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source=pdf_text observed=2026-08-01T08:23:26.724359Z digest=sha256:3a43f38513e0a117060b3aab91551a350e6f979020cb82c425397e92de199bbb

Observation 36976e9f-b72e-4da3-87b6-8ad0f773f60d · outbound

This paper cites Mm-safetybench: A benchmark for safety evaluation of multimodal large language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Mm-safetybench: A benchmark for safety evaluation of multimodal large language models,

Reference 19

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source=pdf_text observed=2026-08-01T08:23:26.846948Z digest=sha256:448cad30402bcd313e9a8ba5355cb78051b4f6964185d6dc20130879dd6c352b

Observation a5e9b5bb-6654-4fe7-9342-345395041551 · outbound

This paper cites Xstest: A test suite for identifying exaggerated safety behaviours in large language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Xstest: A test suite for identifying exaggerated safety behaviours in large language models,

Reference 20

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source=pdf_text observed=2026-08-01T08:23:27.008533Z digest=sha256:28308b984d5503147a06b725bdc6d86a816cdb3fdf0871fcf05d97f4f8824c48

Observation c24710b5-bd32-4c48-a740-b1448f5570fc · outbound

This paper cites HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router

Reference 21

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source=pdf_text observed=2026-08-01T08:23:27.163780Z digest=sha256:d3cf8d26a94e796e8452ada338817a09fafd17b8a683b71cf28f04b372d0ec35

Observation bbdb4910-4e60-4fde-ac4e-5eb904998a4c · outbound

This paper cites You Know What I'm Saying: Jailbreak Attack via Implicit Reference.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure You Know What I'm Saying: Jailbreak Attack via Implicit Reference

Reference 22

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source=pdf_text observed=2026-08-01T08:23:27.312864Z digest=sha256:8869973170404cca5e2384e018beda1050c58c1c198ac59dfee8c3cf1b60afd5

Observation ea9bd991-09de-47c9-9bb2-4d0721a37be1 · outbound

This paper cites Spa-vl: A comprehensive safety preference alignment dataset for vision language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Spa-vl: A comprehensive safety preference alignment dataset for vision language models,

Reference 23

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source=pdf_text observed=2026-08-01T08:23:27.461462Z digest=sha256:c86d2b82e164498e819b78ff44da43fde497e7a96f824a158402447e0e15a56e

Observation 4b465fa7-55d3-4f53-aa60-62705d4cfda4 · outbound

This paper cites Msr-align: Policy-grounded multimodal alignment for safety-aware reasoning in vision-language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Msr-align: Policy-grounded multimodal alignment for safety-aware reasoning in vision-language models,

Reference 24

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source=pdf_text observed=2026-08-01T08:23:27.575849Z digest=sha256:4411ce8fcf695de982d54eb6d820832ae10775e7afa3e58dc8f63c82461a03d5

Observation 7e02f988-e635-400b-a83d-0e25b20f020b · outbound

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

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Reference 25

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source=pdf_text observed=2026-08-01T08:23:27.722142Z digest=sha256:d7f0277858057a88eca4b777ce1518d918b8e6df36f096d39fac0fdb90e679d8

Observation 310881d8-b230-4136-9ee3-2b7a756e426e · outbound

This paper cites Adashield: Safeguarding multimodal large language models from structure-based attack via adaptive shield prompting,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Adashield: Safeguarding multimodal large language models from structure-based attack via adaptive shield prompting,

Reference 26

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source=pdf_text observed=2026-08-01T08:23:27.870185Z digest=sha256:167d80737767c096d4a268716083d144b541c3a4a9f96c80a2df96bdb7748e08

Observation 3b66fdb5-54da-4621-91ef-69c857226bd6 · outbound

This paper cites Bluesuffix: Reinforced blue teaming for vision-language models against jailbreak attacks,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Bluesuffix: Reinforced blue teaming for vision-language models against jailbreak attacks,

Reference 27

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source=pdf_text observed=2026-08-01T08:23:28.009260Z digest=sha256:75cbf497fc510b7b76835c5cecae64f77fee5e720b668090bcab082133c3c69f

Observation 01f990d7-6f45-4a36-bd9c-700efb04c40f · outbound

This paper cites E2AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure E2AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models,

Reference 28

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source=pdf_text observed=2026-08-01T08:23:28.133850Z digest=sha256:61858195063b03c83e09e5e19560a863ed751f3f5a112bfbb78690717aa1d40a

Observation 07f65600-5646-4f35-ae3c-5cc7e167741d · outbound

This paper cites Eta: Evaluating then aligning safety of vision language models at inference time,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Eta: Evaluating then aligning safety of vision language models at inference time,

Reference 29

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source=pdf_text observed=2026-08-01T08:23:28.293786Z digest=sha256:16f300433aeea70417c4d464e1b00f9b25777b1ce8121c340a49c6b2658f123f

Observation 226d2725-39a9-453a-a544-0f92bc29216e · outbound

This paper cites Safedecoding: Defending against jailbreak attacks via safety-aware decoding,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Safedecoding: Defending against jailbreak attacks via safety-aware decoding,

Reference 30

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source=pdf_text observed=2026-08-01T08:23:28.455095Z digest=sha256:78e7edd261c04e1368ab59c419c0438bfd7b058c182db3e406b3cdff5a434254

Observation 74c85e18-a11e-4e1a-a77f-4188d01526cf · outbound

This paper cites Defending multimodal backdoored models by repulsive visual prompt tuning,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Defending multimodal backdoored models by repulsive visual prompt tuning,

Reference 31

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Observation 0041fd77-69c9-4866-858f-31066c132e23 · outbound

This paper cites Gated differentiable working memory for long-context language modeling,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Gated differentiable working memory for long-context language modeling,

Reference 32

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source=pdf_text observed=2026-08-01T08:23:28.634357Z digest=sha256:692626b1a4efa6952c244dce51b6ade1cc42972dc50ff0887af21914da512abf

Observation aac1f20a-73f5-42a2-bf28-0c476ef2a120 · outbound

This paper cites Test-time attention purification for backdoored large vision language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Test-time attention purification for backdoored large vision language models,

Reference 33

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source=pdf_text observed=2026-08-01T08:23:28.719510Z digest=sha256:f67ccc32803802203287e435523c45cd3b175f60da92616171e7d31240a11c66

Observation 81a69b56-690e-451c-95b5-8dec922b5162 · outbound

This paper cites Mrfd: Multi-region fusion decoding with self-consistency for mitigating hallucinations in lvlms,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Mrfd: Multi-region fusion decoding with self-consistency for mitigating hallucinations in lvlms,

Reference 34

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source=pdf_text observed=2026-08-01T08:23:28.799554Z digest=sha256:227dbdb526316575b877b3050ad428db644b84512babecbebb261f0f8c257448

Observation 118b9f87-2a9e-433f-abf5-bfa77a4b287c · outbound

This paper cites TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models

Reference 35

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source=pdf_text observed=2026-08-01T08:23:28.879633Z digest=sha256:f7e49f292e07f28b195dd635a97b0133f9f4d4afb8928162a0077f082e269f1d

Observation b26c8ed6-704d-491b-972e-e90a15e84d13 · outbound

This paper cites Dimo-gui: Advancing test-time scaling in gui grounding via modality-aware visual reasoning,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Dimo-gui: Advancing test-time scaling in gui grounding via modality-aware visual reasoning,

Reference 36

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source=pdf_text observed=2026-08-01T08:23:28.977259Z digest=sha256:e83c68cb5b49aba2ce408fcfa21c4c82f68614ef5e7cc9b35fb8d1a4e2272af8

Observation 65a08c8c-3f88-4687-80e8-befe5ba759d4 · outbound

This paper cites Improving generaliz- ability and undetectability for targeted adversarial attacks on multimodal pre-trained models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Improving generaliz- ability and undetectability for targeted adversarial attacks on multimodal pre-trained models,

Reference 37

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source=pdf_text observed=2026-08-01T08:23:29.061898Z digest=sha256:f9c0dfc814296f4755af0564836684e2786e857e0ee510d21098364c25ed689e

Observation bf77cec6-6d0c-4de2-84f0-5d857f8d57aa · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Representation Engineering: A Top-Down Approach to AI Transparency

Reference 38

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source=pdf_text observed=2026-08-01T08:23:29.149242Z digest=sha256:46be4da2c3038d30eac92abfcf20e537c243a5d684c01fd5ae5690191bda3a6a

Observation 802859be-1d11-4bcd-8a5e-615a765b77f2 · outbound

This paper cites Refusal in language models is mediated by a single direction,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Refusal in language models is mediated by a single direction,

Reference 39

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source=pdf_text observed=2026-08-01T08:23:29.228401Z digest=sha256:db11f6e8a7476d31a86a7b034eaf35aba3dda0fc571865ff7cfa027c7d19cb00

Observation 7182405a-2387-43d1-9069-b4d8d7b7f80c · outbound

This paper cites Guardreasoner-vl: Safeguarding vlms via reinforced reasoning,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Guardreasoner-vl: Safeguarding vlms via reinforced reasoning,

Reference 40

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source=pdf_text observed=2026-08-01T08:23:29.306894Z digest=sha256:39930646e506c1aceb7f091ca22decfd5790f45c7935557348ba6b210d0c95d3

Observation fba53fc4-82a2-42ab-8546-6cd5b542d37f · outbound

This paper cites Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models

Reference 41

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source=pdf_text observed=2026-08-01T08:23:29.396060Z digest=sha256:228817a20dce59d15619e443ec4ac9ff53fa365c9aa43f2ba2cce51d11fbcb33

Observation 2241f3d5-bfa8-4ef7-a6fa-57d22577eecf · outbound

This paper cites Jail- breaklens: Visual analysis of jailbreak attacks against large language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Jail- breaklens: Visual analysis of jailbreak attacks against large language models,

Reference 42

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source=pdf_text observed=2026-08-01T08:23:29.481321Z digest=sha256:a65d3c40999a3241378f3815ffc69566aa2ebcfa95d0bf920a791942dd297da8

Observation 7391216b-c470-4022-8132-13e4d62df029 · outbound

This paper cites "not aligned.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure "not aligned

Reference 43

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source=pdf_text observed=2026-08-01T08:23:29.581759Z digest=sha256:c4c050b603f60c635429a04040fd2fe0611e1da5693e559b00da06ad2e9a691e

Observation 4b4b0dab-6a3f-4a77-a3dd-70cbd4b3efb0 · outbound

This paper cites Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning

Reference 44

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source=pdf_text observed=2026-08-01T08:23:29.666893Z digest=sha256:213d3d78b9c48e4fc029296be8b40c9b5efef76db8ab3a33f22f65910cfbca50

Observation efbcb1ad-39ac-4840-b030-692cdfce5d57 · outbound

This paper cites Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector

Reference 45

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source=pdf_text observed=2026-08-01T08:23:29.745621Z digest=sha256:0eea4b6a1ff4e475f4c63671fca768c7ca18e4f82fdc195fb8cd2995841a03cf

Observation c89cbb58-65f9-46d4-a841-f7f94bb71fb4 · outbound

This paper cites VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety Alignment Gap.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety Alignment Gap

Reference 46

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source=pdf_text observed=2026-08-01T08:23:29.831115Z digest=sha256:7cc449058517d3469808aa79885f4d2e24fd33dbe0b63b027b9dbbafd7a4b5e0

Observation 9dd1bd8a-8244-4790-afa5-b31096496715 · outbound

This paper cites Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study,

Reference 47

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source=pdf_text observed=2026-08-01T08:23:29.961493Z digest=sha256:3e1a7c6040282937df8abd45026d26e33a758c82966cca39fb0d62442e3bf522

Observation 5b3c30c2-be6d-4e5c-80de-9472927ce71b · outbound

This paper cites HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States

Reference 48

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source=pdf_text observed=2026-08-01T08:23:30.045950Z digest=sha256:f769d39dea951447cbae7cf639ae92dc63b1c2fe91f26f19e7f6b5dd15f208b4

Observation 2cb625d7-3ab6-4ba9-9b7f-6bca1a30cb20 · outbound

This paper cites Brainvis: Exploring the bridge between brain and visual signals via image reconstruction,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Brainvis: Exploring the bridge between brain and visual signals via image reconstruction,

Reference 49

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source=pdf_text observed=2026-08-01T08:23:30.102859Z digest=sha256:365268f3ae20925488cfdb04f52f02aaeac67d683667307af0352221fc63a298

Observation ff7c5261-a453-457f-8426-f2b941488006 · outbound

This paper cites Tuning vision-language models with candidate labels by prompt alignment,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Tuning vision-language models with candidate labels by prompt alignment,

Reference 50

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source=pdf_text observed=2026-08-01T08:23:30.217304Z digest=sha256:60987ad1e9bcdb71b68e3a2787a55467285afd5b51be18ee0b7133d1f6dd6003

Observation aa737389-de1b-479e-8a3e-c0a65151bfb0 · outbound

This paper cites Refineshot: Rethinking cinematography understanding with foundational skill evaluation,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Refineshot: Rethinking cinematography understanding with foundational skill evaluation,

Reference 51

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source=pdf_text observed=2026-08-01T08:23:30.354643Z digest=sha256:34f24b29fd4c1bb5040ca290e70de59febd8a7149cae321a99dcafafcd1545c2

Observation 2707110f-43c0-40e8-9446-3278e2f9779a · outbound

This paper cites What should a streaming video model remember?.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure What should a streaming video model remember?

Reference 52

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source=pdf_text observed=2026-08-01T08:23:30.430349Z digest=sha256:2a8b7fa5c2c213f15774e81d4bb3c67f010f002fffe0b807c3c08db35eb505e4

Observation fc9c40da-8dca-4354-8b8a-9e8f9e7a5591 · outbound

This paper cites VLSU: Mapping the Limits of Joint Multimodal Understanding for AI Safety,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure VLSU: Mapping the Limits of Joint Multimodal Understanding for AI Safety,

Reference 53

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source=pdf_text observed=2026-08-01T08:23:30.536722Z digest=sha256:6a95149e9ec8137be36eb0300046ab8c19e9b00ecf2878ac48014bda6f34264b

Observation 58db01ab-fba1-4b02-b875-2df991562a9e · outbound

This paper cites Vistawise: Building cost-effective agent with cross-modal knowledge graph for minecraft,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Vistawise: Building cost-effective agent with cross-modal knowledge graph for minecraft,

Reference 54

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source=pdf_text observed=2026-08-01T08:23:30.608162Z digest=sha256:afbdf5390ba82049040237988e129493d56c01d44de7249f8ce521209e94bd0c

Observation 42dea20b-383b-4903-befe-c2cf52b315fb · outbound

This paper cites Safe inputs but unsafe output: Benchmarking cross-modality safety alignment of large vision-language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Safe inputs but unsafe output: Benchmarking cross-modality safety alignment of large vision-language models,

Reference 55

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source=pdf_text observed=2026-08-01T08:23:30.700547Z digest=sha256:6c28f2b1cd918d9983fdffb4da28db4bb900c535aec06474496bd26aea4703e2

Observation ec738abf-5682-4d9e-9949-bc5c97e30644 · outbound

This paper cites Contextnav: Towards agentic multimodal in-context learning,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Contextnav: Towards agentic multimodal in-context learning,

Reference 56

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source=pdf_text observed=2026-08-01T08:23:30.785369Z digest=sha256:085a10f224196e655bc52a9de3f5ba4f7a2b40b164db97279008d5dc2c9dcfed

Observation a624a08a-09d9-4b82-ba71-dfb74e901cb5 · outbound

This paper cites Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking

Reference 57

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source=pdf_text observed=2026-08-01T08:23:30.869177Z digest=sha256:6c25170cdaa89e40b13f81d3b17dbb00f9072dad991dc499a236c8bfe2748d1c

Observation acff3c2e-36fe-41ef-85c3-3f6dad372087 · outbound

This paper cites MSTS: A Multimodal Safety Test Suite for Vision-Language Models.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure MSTS: A Multimodal Safety Test Suite for Vision-Language Models

Reference 58

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source=pdf_text observed=2026-08-01T08:23:30.999477Z digest=sha256:0761da1cee4c96df0fe29f1a577f22666c8d732578bed459fa56d99a279ddd01

Observation 329af082-73f1-40cf-927d-45246d7dec74 · outbound

This paper cites Enhanced partially relevant video retrieval through inter-and intra-sample analysis with coherence prediction,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Enhanced partially relevant video retrieval through inter-and intra-sample analysis with coherence prediction,

Reference 59

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source=pdf_text observed=2026-08-01T08:23:31.127006Z digest=sha256:0f233355b1dbaef51a3a056b402a1b70b89538fc5701dce5dbd8fa8cc4b72e3c

Observation 7ed234ea-30bd-4a88-aea4-96f441780839 · outbound

This paper cites Slang: New concept comprehension of large language models,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Slang: New concept comprehension of large language models,

Reference 60

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source=pdf_text observed=2026-08-01T08:23:31.257851Z digest=sha256:5598be000e41b181dddb9497de552d8f4de98903f06097035b8550fd3b862689

Observation 8df1f85f-83e8-4bd7-a22c-e78884379a39 · outbound

This paper cites WaMo: Wavelet-Enhanced Multi-Frequency Trajectory Analysis for Fine-Grained Text-Motion Retrieval.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure WaMo: Wavelet-Enhanced Multi-Frequency Trajectory Analysis for Fine-Grained Text-Motion Retrieval

Reference 61

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source=pdf_text observed=2026-08-01T08:23:31.376601Z digest=sha256:c89e3057cf514b4522b00dcf3f10b1ae7dbd25bf8427d319a764dc859bf8501c

Observation 530f78e2-97ce-4999-950d-9dd642de875f · outbound

This paper cites Sdr-gain: A high real-time occluded pedestrian pose completion method for autonomous driving,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Sdr-gain: A high real-time occluded pedestrian pose completion method for autonomous driving,

Reference 62

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source=pdf_text observed=2026-08-01T08:23:31.512899Z digest=sha256:25fab724eb3ccb68e327b03886af4a1a63031ecf8c763fd12599f9099055607e

Observation 81008fc1-aaf3-4e1e-88bf-16763bfc89cd · outbound

This paper cites MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction

Reference 63

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source=pdf_text observed=2026-08-01T08:23:31.649091Z digest=sha256:7ccfe907d9f65746c1b63b5adc9380afda86cf3bfefafb05d54909d7d209a3be

Observation 1f5e13f5-9c9b-4a31-8035-7a685dd5f467 · outbound

This paper cites Enhanced cross-modal 3d retrieval via tri-modal reconstruction,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Enhanced cross-modal 3d retrieval via tri-modal reconstruction,

Reference 64

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source=pdf_text observed=2026-08-01T08:23:31.730788Z digest=sha256:b06c04d9a9de43c264f85c38632fce8aae99a8e24e9eeaf0fe39825689bd3516

Observation 86184b48-d41f-42d1-8a98-0e156bc87929 · outbound

This paper cites Minicpm-v 4.5: Cooking efficient mllms via architecture, data, and training recipe,.

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure Minicpm-v 4.5: Cooking efficient mllms via architecture, data, and training recipe,

Reference 65

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source=pdf_text observed=2026-08-01T08:23:31.950259Z digest=sha256:5ced4c86dc5b194af320087beb681a7adf9a27a67cbefc853b92035408101e86

Pith citing papers

No inbound Pith citation observations are available.