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Paper Citation Record · LEDGER

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

As of 7 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:feab4cb1d32c7a8f6311ec4e1731dd7f6d2f0a604dd8ab8f010ffa14334f1ebc

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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verified fuzzy
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=sha256:984f34a12eb7abc1b13a6293c7c9c6c1ef04a3221949c9a3b491e0b2e05b6c9c

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:b04c9de49290419b5c72ce3adc65e85a7d14aaa83d024be2644045a8d2711c11

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=sha256:8cbfa390988ba5cb19a54735589cb987ce9249ed292b47d044b2db7bf36e734e

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:c39bd5b44eaaf1f6d01c4d75ee3e3783f4267f9ed630cda3f11eae64c7006e61

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=sha256:3f4b0d0bf7fba7eb4bfa6510687c80bc86a248adf53de95cf5137214797881d4

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:bda93d008f7677c5a2b5a129e87ee26fb55807af50a38b094174a90c53d501c5

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:d9eb7ca748e22a2e74e675f113634dbb7d43ab8ab05f1dfe1c51876fb19dacbc

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=sha256:366b835bd000ff6a4db1510675957a499ba0b6cd06e47264d09323a280cee0e1

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=sha256:9a7be135f034649005571d6c95bf88f05104e39eda911bb9a7e0c42f13339c5b

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=sha256:2b71c522025c2fc402bf1ce2c62abca2dd570364f49b874f31df981c5e2031ee

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=sha256:2f0fc4bd46fe79a70dd406f1c4bc1b92c830b11f9eaf7fbc2dbabde56a4e89bd

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:ea3f0271120ab82d7d2acdf4f4d560fcbcea83f86c6d2b2444e8c3008567995b

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=sha256:806c02f842ffc28b8ee55c5848dee494c66f425eb9eba070e23d0e4e7d3544d6

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:eea7d83be5aede2db47863132de434260384e96ca26184ef4e6dea7af9656eca

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=sha256:6073e5231e42c8dd3fc27b9abe4b365a7167025076329c1364cccd9d358a00ae

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:f7d635d52d8e65762c87485ea6796ae83e35fe186a456e2021937098bd41c1df

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:b27255c8e4efa24b382e2ac172570f05d926a6b9fe79e1670d2262e32bbf4ebb

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:fe701228ddbad79e948dd3af86f4813c2d87ce49ecea735e1608ebee0bc32982

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=sha256:786de955b5087d61d1e3658c8388906d5b8ba4b94cdd2de04bcf245924f17b82

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=sha256:152e46eb278d125362b6c5e1d7c790ce50179c730f814fd26c7feadb1d03d863

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:d375b042afe6eb54e8f1eb2fbea9b4c8a964cc871aaf9f29e3a91f97f1f18060

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:f3c8fac2318147a384e4480e975449ae9ebc1ff2498b32cf520b592dae47d5f9

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:d0d1f7fce771c7388707d2e889426dd0175fbc5fa198fbb6348d8cff2628df81

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:bfcfce7674a4d66a17683d893d557ded710bb2bd503d0f0365925f1e65e61ff0

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:b60deaca930a116968fa185d112940b15892cda5bf84d05d8bdf189ec25653b3

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=sha256:9b973ffda9ac133cfa57c40706a95803bd10029a2d1ac961a9b8dac54846a0ae

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=sha256:8a37b68b34b2dcff2f952a3e405d812b4635f8a112e33ca9f7bf352b4c4661fe

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=sha256:19272ff103490539c3eaddc0579fdfda96beb1135d674d02aaca3ced8e56b001

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=sha256:6b8574d467a867f94abe286ab6cbdf229a8ffcd273e8c7eb20dc56ebfe964ea6

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=sha256:38849f09fb4b9de19bea218b7a1ddd4b7500c4cf4011539cdca120e6879b808a