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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 2026-08-08T06:32:00.761636+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=sha256:974c58ddabdcf172b672c8ab9f3412a29088946231d67e63a37724960da4a8ca

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:37:22.900435Z digest=sha256:a6be97d86d030aaec716a64075948cbdfcf5037f786342c6f4d753cf0c202f2d

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:ea579930bee7691d5d5a02c90162bbdc013ffa1f24df0e6353a8a032d7dcf7a9

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=sha256:9821917e90903f507b5ed655f00466e6f4d54afcd96850e1c1460437d4b36561

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=sha256:119c4b10efad46ba7672445e7fbe2d7145a677a73778dd8111cf5c64bf641424

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=sha256:51d5dce6b38e0e08dd292060ea1e1a2dfb72e762aecab47679678d494677358c

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=sha256:4eb0e1c88fba85fe206d44c7e626269db9cd8135334e927f332a619e3344766e

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=sha256:18ce6949ef86b915c40a6aca4aacbc569e476d9784d84ae5600411846b42d2d7

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=sha256:12b32cf979fdd7210659a7b9401249814e72cde2d2add1d16ea33da6bc0ec86c

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:d012f40eeb9fa246f87c8e55b2278e1bd04cf99a38311e1283c3461d92fb222b

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=sha256:4107da4150ee08ac20768d11d451f58fa31f24bf592378e0ba7c9e30361ac66c

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:b35572ac4cecc088af719a82703262deee3500cb5b74d2513a484273a41eca56

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=sha256:01ef504fac3ca5e0968540d4c37846db9cb449faa36962afc2a9a1a8c24bcec8

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=sha256:1f5350ec1a2e24951d814421780b005b015534e2a88ad9cd57f01fcdaa02e6fa

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=sha256:50c2c08f9acd48038cb3e86591fa48abdad61b09a3e9b5b38819176d310cae90

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=sha256:45c6484d9957525421171c7e3ab460ca5b545d79506102e5fc89fd5e095ce45f

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:bf8b16e902860ae62b8e4281c2aec80cf34441186a304af83cc967f2b4d967b8

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=sha256:37d28491c2e2bf6d4be98c77e2a2942cf28a6512cb665be0302959196468170d

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=sha256:59fccfc42a0d6fb27d717505c219c7c10893e4ad10c70700149fd5d8dcfdcd49

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=sha256:598b8b520d0f7ef0dbab581c7f5203bceec78f472483936c7efe3b2d3b5f99f8

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=sha256:9d61f83e7e576ed392683d63036fc10309917ee1b2ca678282332554a20d7352

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=sha256:1a14f31e271debecbcdb56b3284b9a009d33afb8bba633a46fcf3b1959eb5ce5

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=sha256:4a455dd7c3d396a59171b7bd62616a3562fcc4605ccc1081a07dd7ad729e667f

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:cfd5fe9b32d4c8821cced2c37658795ce192d116f10d1ddc4d18b0775f342ac8

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:b6ce15d3e2154590d024239bbc628e7610cd868bd885330994c4032aa612ac4b

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=sha256:19ec67a9c2d8dd6bb00e65905ef6299a2379d03fdb8e29c35ee004529912b288

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:eadab8ed172bc21068cfa1cd01eda6b097d0b9bdbe4f262efcc86dcfa432db98

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=sha256:5f3e377e8af4d919cde0467ad632231b1a5cf251e714f96f8293f1f3f31083e0

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:ccfcc669e4a96594656c05497120ed8a9118ca54e37c3d3c39c362281e240071

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=sha256:79d51f30ed5daa19e4a18bd4e7fce1283032a8c5a1849d31396adbfd2a9873a1

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T18:03:36.805784Z digest=sha256:a13a556e68f7608bc9f318b95f7072047e76fb9402a3adb782832780560bbbfe