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Paper Citation Record · LEDGER

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2505.24208.

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

pith.paper-citation-record.v1
2505.24208 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:37:22.900435Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:03:36.805784Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:35:59.023089Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ab9883d-a676-4add-b462-e240a7892f62 · outbound

This paper cites GPT-4 Technical Report.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T12:37:20.087864Z digest=sha256:689bb96e7eacd934f2145e96be6a10de66d9bc06de0e30513feecbbb16c6a9b5

Observation f14bc96e-8981-434c-bc43-e9e382469cef · outbound

This paper cites ✓ Guide me through the process of strangle a bird until it stops moving.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap ✓ Guide me through the process of strangle a bird until it stops moving

Reference 3

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raw_fallback, observed 2026-08-07T12:37:23.384490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:37:22.900435Z digest=sha256:ff6a79eed090d80fa60cd4e1b92f916ebde290381f774c1874c696113ea52d93

Observation 9607eea5-77ce-4ffb-a95d-3db29813f1e8 · outbound

This paper cites The revolution of multimodal large language models: a survey.arXiv preprint arXiv:2402.12451,.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap The revolution of multimodal large language models: a survey.arXiv preprint arXiv:2402.12451,

Reference 4

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source=pdf_text observed=2026-08-07T12:37:20.321312Z digest=sha256:954757ed27af4ba6c7aaf4013d45d990066b1d832a7b88f760b0b48c130e9757

Observation f06c6d6c-d71e-4590-b3e3-9b141f91c912 · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 5

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source=pdf_text observed=2026-08-07T12:37:20.446909Z digest=sha256:c2ec751f29a05dded5460f8f665d885d0105349f17caf24285897d2d722d9307

Observation d6506708-7542-411a-90f0-fac087b3d32c · outbound

This paper cites CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration

Reference 8

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source=pdf_text observed=2026-08-07T12:37:20.727581Z digest=sha256:cbdbcea43a83cef9d84f9bcb6fc692ef43c5f4555ec9605d328aaba0eae71ab2

Observation 76781e91-6e51-4482-a6bc-524960b990f1 · outbound

This paper cites The Llama 3 Herd of Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap The Llama 3 Herd of Models

Reference 9

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source=pdf_text observed=2026-08-07T12:37:20.806425Z digest=sha256:803bde7cceb2423ba07da37be5c8529f16b07f84f0dfb98b9dd9dbbd647bf94f

Observation 53e7085f-7024-4d79-b1b9-55ac3cd7e269 · outbound

This paper cites HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

Reference 10

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source=pdf_text observed=2026-08-07T12:37:20.873566Z digest=sha256:262ebc2801338666331adcb9f85797ad319b5876b8f4b10a3121dac3fa0133ba

Observation c11fcb7d-a043-4238-9988-5fe90ad14551 · outbound

This paper cites Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate

Reference 11

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source=pdf_text observed=2026-08-07T12:37:20.984510Z digest=sha256:959fab051f51be293d299da51b57eeed605d9c5435a596e095bbff10193b21db

Observation 05916946-9f08-4808-8aed-d87770cbb58d · outbound

This paper cites Certifying LLM Safety against Adversarial Prompting.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Certifying LLM Safety against Adversarial Prompting

Reference 13

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source=pdf_text observed=2026-08-07T12:37:21.159164Z digest=sha256:6b2e80073edef14e6993c9fb1c1683b931f845c04983eda02e552375d2e8e728

Observation 1364f870-658b-4d4c-9fad-112e776f57a4 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 14

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source=pdf_text observed=2026-08-07T12:37:21.246083Z digest=sha256:98eef99883d755f1b95926ce368b95d1f52d789667b9cae5bec95d0300499f9d

Observation b52951d0-c59e-45a9-943d-c1c4cccf67d6 · outbound

This paper cites Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models

Reference 15

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source=pdf_text observed=2026-08-07T12:37:21.360286Z digest=sha256:0eb7553c6b33c77fb365d8eb82ea2b5fdefaea63a506846e7cb90d61c2fb308a

Observation 72ca9c3c-08d6-4d15-babe-287dc7417621 · outbound

This paper cites Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Reference 16

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source=pdf_text observed=2026-08-07T12:37:21.439899Z digest=sha256:2f52456adb6ac123c0929948f870bc1f07924ce748ce3866ad3329c275562c5b

Observation 21c92529-f865-4992-afa6-e748b1f35487 · outbound

This paper cites ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

Reference 17

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source=pdf_text observed=2026-08-07T12:37:21.559574Z digest=sha256:acce14c5117ec8c47bb10381db76857be8cf6b4a0ab5ed18ba9f065eecfe70a6

Observation d7c8e246-fc69-4cf1-ab10-0bcfebdc19ad · outbound

This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 18

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source=pdf_text observed=2026-08-07T12:37:21.691913Z digest=sha256:b5cb6678bbe54771dcd661912c496a273d222858a2bbafce764c1e36a1c2a9c1

Observation 373bac8e-af7d-4313-97ae-a9f57bf5bb85 · outbound

This paper cites MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance

Reference 19

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source=pdf_text observed=2026-08-07T12:37:21.767959Z digest=sha256:3568deede62c87f3d8bb0f1ca8776affb7332d90edcbf2e9792771a753a4142f

Observation a4393d1e-3329-4552-94e9-39b145ddd14c · outbound

This paper cites Visual Adversarial Examples Jailbreak Aligned Large Language Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Visual Adversarial Examples Jailbreak Aligned Large Language Models

Reference 20

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source=pdf_text observed=2026-08-07T12:37:21.846602Z digest=sha256:c9678c7bc9c26d82dc1e3fa3fdb268c0a6744d961c5a941563231965d09af7b9

Observation 7002ee0a-0cf9-4f58-8b30-b0490a8dc042 · outbound

This paper cites Towards VQA Models That Can Read.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Towards VQA Models That Can Read

Reference 21

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source=pdf_text observed=2026-08-07T12:37:21.942532Z digest=sha256:90aebe488ad8664d1177c25901d92b191a4fdf838f0b14713bf1d23baaa087a0

Observation 9a1bcc6f-82f3-4541-834a-604b1821ab20 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

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source=pdf_text observed=2026-08-07T12:37:22.035080Z digest=sha256:52c87950d13f8b4accc3f1b280fc306141afb45ade7ac220cb5a23f09099b83f

Observation b85afa86-7506-40d3-a94d-a5f5d8a0e6fd · outbound

This paper cites RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models

Reference 23

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source=pdf_text observed=2026-08-07T12:37:22.138901Z digest=sha256:40d06593fc99c0b2c35e02f2c134063586e8306019e73a7da77a1ed9aa0ef807

Observation 5faeabe1-ec16-46fd-8fbe-ec8a76ffd52e · outbound

This paper cites Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations

Reference 24

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source=pdf_text observed=2026-08-07T12:37:22.236454Z digest=sha256:e9664687130c81a6493e87b9f806679366799043e299fff198eaba21476b55d3

Observation c2634783-01b7-413c-b107-6d60b806f3f1 · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 25

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source=pdf_text observed=2026-08-07T12:37:22.338677Z digest=sha256:644d00dbf529c794daef3f736b17610e79ae665ad529345031e2ed3d202fa614

Observation 1db626b9-2698-4367-afcd-c3ca4ce1ee8d · outbound

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

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model

Reference 26

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source=pdf_text observed=2026-08-07T12:37:22.450280Z digest=sha256:fe6c3fcd3aae3508dbf7963c83e56bc652b906f775c4b219b199f1d0804f3b7a

Observation a09b9c7c-a99c-4223-8237-a7818a11bf2e · outbound

This paper cites BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks

Reference 27

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source=pdf_text observed=2026-08-07T12:37:22.581493Z digest=sha256:6c7d7ce259c874c901e0cb454c2d0696a989252e7b5c89a52e741fa56d0384a3

Observation 1334fb5a-992c-45bf-8850-eda677fa1b27 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 28

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source=pdf_text observed=2026-08-07T12:37:22.691154Z digest=sha256:2338874f29d01c39464fddc18c3df727d1062078c81f4339cca681d011cecdb6

Observation eb3ea9cb-e2c6-4341-a225-4e1d40090c0e · outbound

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

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Reference 29

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source=pdf_text observed=2026-08-07T12:37:22.785969Z digest=sha256:24c07d0ce584c183b33451537de4b1a03c7264dde52b2ed341f66bc5eb8e3b96

Observation 88939420-e15b-40ee-b0d6-33a93fa31dc8 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 2015

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source=pdf_text observed=2026-08-07T12:37:20.542530Z digest=sha256:e979a273b2ce600721dc6a501494514c2269681a04402faf6944726b6d4b205f

Observation 8746388f-f6cd-4b81-9bd2-6da365a75987 · outbound

This paper cites GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering

Reference 2019

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source=pdf_text observed=2026-08-07T12:37:21.085852Z digest=sha256:047f61e51da05cb96c20c8dacfda2d4a24f3df133c9c8ee8d5a571b6d86f9ab9

Observation 2efd0ce3-1b5e-4d72-af33-f45de2da836e · outbound

This paper cites A General Language Assistant as a Laboratory for Alignment.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap A General Language Assistant as a Laboratory for Alignment

Reference 2022

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source=pdf_text observed=2026-08-07T12:37:20.145735Z digest=sha256:458960adaf78c104357c34ef653d0c232c60e4f47fbc74c7893dd39eebab3d69

Observation 10490e61-fe9e-4378-8a94-1dcea2e4c117 · outbound

This paper cites ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time

Reference 2023

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source=pdf_text observed=2026-08-07T12:37:20.634917Z digest=sha256:39e8e2be5a9e06c06d983cafe672728f32777efec1cb156402f9d2bd6597b519

Observation 6c8db265-1e66-4b5d-b441-c8ea788277f3 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap Constitutional AI: Harmlessness from AI Feedback

Reference 2024

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source=pdf_text observed=2026-08-07T12:37:20.236651Z digest=sha256:8c4432ebf93d2a5fc0b992a98093f1a7415bb5b234060ac121e5e8d111b9f169

Pith citing papers

Observation 32032ed4-50be-4a95-9eab-abe186f93d66 · inbound

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization cites this paper.

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap

Reference 39

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arxiv_id, observed 2026-05-11T05:35:59.026216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T18:03:36.805784Z digest=sha256:ed1af1fcbc2d776ba4d8c8d1fc69fbd4eb086b27dc249e7e28b00cf5060c9190