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Paper Citation Record · LEDGER

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

As of 21 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 8 inbound Pith citation observations for arXiv:2505.11049.

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

pith.paper-citation-record.v1
2505.11049 v1

Coverage vector

measured 95 of 95 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:05:17.701839Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:56:52.768314Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

95 of 95 outbound references displayed

  • verified exact1
  • verified fuzzy8
  • unresolved86
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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

Outbound references

Observation a67d602a-7f1a-4fe9-8115-3c338a3d1cc9 · outbound

This paper cites GPT-4 Technical Report.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-15T21:05:17.276990Z digest=sha256:96446bf2a7ff0b37ff558ecd5c4093f2036e82380fb1e7dbd19be5968b376a34

Observation 059dc69b-7f28-481b-be96-4716422589e1 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Flamingo: a visual language model for few-shot learning

Reference 2

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source=pdf_text observed=2026-08-15T21:05:17.282637Z digest=sha256:43716dbb6a020af445b57775bfcae5f3f5a20304252b3ca3fedca1b7a857cdb2

Observation 83d84771-0ed3-4111-bb82-78d040990475 · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning A General Language Assistant as a Laboratory for Alignment

Reference 3

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source=pdf_text observed=2026-08-15T21:05:17.287377Z digest=sha256:c3848cd7845e9ab8eafc5deaadc000e812a9a1d32ccdf31a7454ff34761aadaa

Observation f694b416-de5d-4862-813e-b4d8b62c550a · outbound

This paper cites Azure ai content safety.https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/, 2024.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Azure ai content safety.https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/, 2024

Reference 4

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source=pdf_text observed=2026-08-15T21:05:17.292503Z digest=sha256:cad418390736d42c96310a0ea46480bcce310ac7edc36696ea65c24b6cf84da4

Observation 9ca163ae-a265-40ce-a37a-53b0cc82497a · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 5

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source=pdf_text observed=2026-08-15T21:05:17.297130Z digest=sha256:115c94428ff78c5b3cc7d934fda727e79a057cbaa76c1a87c4d220ea5063126a

Observation 9e76503d-7782-48e2-a935-ebb166284a53 · outbound

This paper cites R1-v: Reinforcing super generaliza- tion ability in vision-language models with less than $3.https://github.com/Deep-Agent/ R1-V, 2025.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning R1-v: Reinforcing super generaliza- tion ability in vision-language models with less than $3.https://github.com/Deep-Agent/ R1-V, 2025

Reference 6

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source=pdf_text observed=2026-08-15T21:05:17.302458Z digest=sha256:a93041da81b5c43291e99783979254871ea7c706e54250a8c2bdb9de9b8d2eb8

Observation 77ce474a-c83f-4189-937e-caf3cdee3be1 · outbound

This paper cites Sharegpt4v: Improving large multi-modal models with better captions.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Sharegpt4v: Improving large multi-modal models with better captions

Reference 7

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source=pdf_text observed=2026-08-15T21:05:17.307692Z digest=sha256:796ebdd79f5feb79597563ce8dc34ffa69e9bf3bd0b149c1c9b296c12698f467

Observation 0037bc2a-e4a9-43ed-b33e-b6f8f1e4dde8 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 8

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source=pdf_text observed=2026-08-15T21:05:17.312329Z digest=sha256:cbc451d5a8af43547645a3240400184dd7207b8780be2cbfa3330bedd72a043a

Observation 81adb8cd-07d9-4948-814c-3be92b933378 · outbound

This paper cites How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites

Reference 9

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source=pdf_text observed=2026-08-15T21:05:17.316854Z digest=sha256:6815dce87c5f26b9e47911d0c83cedb06538de44f55c5fe0f70b14b8feafe00f

Observation d2616f7c-47d4-4f4c-bb1a-79948b94c085 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 10

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source=pdf_text observed=2026-08-15T21:05:17.321765Z digest=sha256:c8c09dbf5e63f5f15e415fabad3a255cf9b34200836181b9d5e4d80cf296f728

Observation a9a5519a-a09f-454e-9774-509c90d902ec · outbound

This paper cites Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations

Reference 11

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source=pdf_text observed=2026-08-15T21:05:17.326452Z digest=sha256:44e254bfb98cb6373bc03a49003618ac86783163e567dd6ae72f3d3c5c08fc59

Observation dadadb3f-6a3c-461b-a374-7e97571984fb · outbound

This paper cites Llm agents for education: Advances and applications.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Llm agents for education: Advances and applications

Reference 12

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source=pdf_text observed=2026-08-15T21:05:17.331925Z digest=sha256:2127ea5b2cf89a43f4ea7ee90cf713adb077524d5a6276cb2f809d47d3b30ddc

Observation d66acdf4-d0a9-467e-affb-1b2b856feaa7 · outbound

This paper cites Safe RLHF: Safe Reinforcement Learning from Human Feedback.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Safe RLHF: Safe Reinforcement Learning from Human Feedback

Reference 13

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source=pdf_text observed=2026-08-15T21:05:17.336118Z digest=sha256:823af1998927b5f6e24d69b8daadbe99950984d10df3bcfb101522ef3092f0d2

Observation d27dcaf7-13be-4aeb-8bef-d3cf7adfdf0e · outbound

This paper cites Gemini robotics brings ai into the physical world.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Gemini robotics brings ai into the physical world

Reference 14

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source=pdf_text observed=2026-08-15T21:05:17.341152Z digest=sha256:51b2fea692b22b67f9d7d1e58783d4076fabf74bed59ba83777eca054d5bd316

Observation b38f74aa-c96f-4e64-b2cc-6841eb7b6813 · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time

Reference 15

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source=pdf_text observed=2026-08-15T21:05:17.345848Z digest=sha256:fe8ade0f5de29070299381b472c7463aa55e40f3d6b238e629261efab85c1e58

Observation 5a1a86ce-35e1-4fa1-85d6-fc3bc923d838 · outbound

This paper cites VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild

Reference 16

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source=pdf_text observed=2026-08-15T21:05:17.350734Z digest=sha256:c4c4861d5da8564895035fa435a5942e1fb5f7b0e78c6840d17b174716b04593

Observation 4c0641ec-2dea-441d-9c4c-dd18a68b3c22 · outbound

This paper cites The Llama 3 Herd of Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning The Llama 3 Herd of Models

Reference 17

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source=pdf_text observed=2026-08-15T21:05:17.355013Z digest=sha256:283db68063977c653339a307ab36e814cfb657d6bf5ec177cbd212e26ad94d10

Observation 91f855a9-3818-4406-b848-5d5bc68792fe · outbound

This paper cites Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment

Reference 18

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source=pdf_text observed=2026-08-15T21:05:17.359178Z digest=sha256:b57c6027fea91e6200e391fe71824755bc1fa26ecad42cc32220c042e2eec15f

Observation c0936b09-a7b3-43ff-8f68-e03761ecaa46 · outbound

This paper cites AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts

Reference 19

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source=pdf_text observed=2026-08-15T21:05:17.363940Z digest=sha256:592b66c45bc441d6f07811aaab725e9599038924a41c75dfea1a525b01c92343

Observation a484e8de-fd23-4538-8126-30b10b8a0ae8 · outbound

This paper cites an unresolved cited work.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-15T21:05:17.368245Z digest=sha256:134537ba0ac78edd0704d5bdd0ba9ed659f25fabd7699a5bd0d260522444d968

Observation 48419eff-b2b3-43aa-8648-18699d8d518f · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts

Reference 21

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source=pdf_text observed=2026-08-15T21:05:17.372492Z digest=sha256:e4fe4f9f4fa6cc8c4f057bb58c22f8c7bd98a622c8d581dea72362544b0c3877

Observation 24f802ab-2532-4fd0-86b6-cbc7018d865f · outbound

This paper cites Mllmguard: A multi-dimensional safety evaluation suite for multimodal large language models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Mllmguard: A multi-dimensional safety evaluation suite for multimodal large language models

Reference 22

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source=pdf_text observed=2026-08-15T21:05:17.376605Z digest=sha256:3f11b55746d12b3653cab020746e2282f4668511a1bd7f766747ea197c640783

Observation 185820a3-ab7d-4162-810a-b1408d04f017 · outbound

This paper cites Visual programming: Compositional visual reasoning without training.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Visual programming: Compositional visual reasoning without training

Reference 23

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source=pdf_text observed=2026-08-15T21:05:17.380595Z digest=sha256:c90dfc74f68574ff9ce92d854d6b5c623964788b04ede428bc262cbee3514394

Observation e2e443a1-daaf-4ab0-8d93-8f8a9da1c3d6 · outbound

This paper cites HOD: A Benchmark Dataset for Harmful Object Detection.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning HOD: A Benchmark Dataset for Harmful Object Detection

Reference 24

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.384511Z digest=sha256:c34ca6b90735380651c537fe42981eeac0afa3dd08e804b6a03e5f2184615fef

Observation df4669be-510d-4d59-854e-5a83ec5e5bd2 · outbound

This paper cites WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs

Reference 25

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source=pdf_text observed=2026-08-15T21:05:17.388935Z digest=sha256:71a186460d5a22f9414472671a0e94668d19f7c49a2fa3135b7674520bd3dfb9

Observation bfc5856f-1c94-4deb-bc51-c33a869c334b · outbound

This paper cites Llava- guard: Vlm-based safeguard for vision dataset curation and safety assessment.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Llava- guard: Vlm-based safeguard for vision dataset curation and safety assessment

Reference 26

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source=pdf_text observed=2026-08-15T21:05:17.393351Z digest=sha256:b2927562cd070612d6d160f4ed2302f39379f6e3117f518ff12571cb7b3857ff

Observation 15915a3c-74a2-4415-93ec-3e26e0e5681c · outbound

This paper cites Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable

Reference 27

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source=pdf_text observed=2026-08-15T21:05:17.397773Z digest=sha256:0d51c2736431d0f8cdfb33136e070553a770dd381f3ea985c0eeaa40ea646d79

Observation 402efc18-f9f3-417d-ae4f-6505bb7f7b2c · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 28

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source=pdf_text observed=2026-08-15T21:05:17.401978Z digest=sha256:bf5e933ca65a7baaaf2a57d1d2209a2bbdb8ed38e00fb43f10d4863cce54f632

Observation 0b589666-6d6c-484c-aafb-a4d794d115f3 · outbound

This paper cites Beavertails: Towards improved safety alignment of llm via a human-preference dataset.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Beavertails: Towards improved safety alignment of llm via a human-preference dataset

Reference 29

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source=pdf_text observed=2026-08-15T21:05:17.406640Z digest=sha256:1d65eabca6fe7787f5bb5efb6dd75fb3e4600166bc1fc719f7e272dc97abde87

Observation 4c3021d7-43bc-4e6a-ad11-19357bca4517 · outbound

This paper cites Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback

Reference 30

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source=pdf_text observed=2026-08-15T21:05:17.410804Z digest=sha256:4d981c81403a60e677052550b6657277d6a8bdd4c9ceaa11bdff043c85a08999

Observation 931adb0c-5bf9-44b4-8cfd-e8cf88095c93 · outbound

This paper cites RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete

Reference 31

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source=pdf_text observed=2026-08-15T21:05:17.415290Z digest=sha256:b00b4094797e84c3f1bcc3baf38732fe4d669ed066491e19a64a913f1f810216

Observation 1e2df54e-8e48-4b3a-b3ed-74c4c5352818 · outbound

This paper cites Mistral 7B.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Mistral 7B

Reference 32

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source=pdf_text observed=2026-08-15T21:05:17.419833Z digest=sha256:b16a299c06fc57b8bf9f3d1f88e34f19c17a6ba83a3d5041adef04b73e436730

Observation 64abdd87-a7d5-4dda-b183-49ea6da922c3 · outbound

This paper cites Chat-univi: Unified visual representation empowers large language models with image and video understanding.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Chat-univi: Unified visual representation empowers large language models with image and video understanding

Reference 33

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source=pdf_text observed=2026-08-15T21:05:17.424521Z digest=sha256:afd96858da19f9bb2e96fafabe2182f9a541ca9a3f1f95641c52bacc73bc4a25

Observation 96770cc3-6118-4003-8d1a-f7ea010b7729 · outbound

This paper cites The hateful memes challenge: Detecting hate speech in multimodal memes.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 34

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source=pdf_text observed=2026-08-15T21:05:17.429292Z digest=sha256:122dd9afbe137ce5ecbcd5cffaedc34f03a44f8f55d7de0026729bbd72a88319

Observation 260024b7-94e6-4cdb-af70-da7cedfb86db · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning LLaVA-OneVision: Easy Visual Task Transfer

Reference 35

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source=pdf_text observed=2026-08-15T21:05:17.433581Z digest=sha256:41024257138fed93580e79bd052fa7e501177b75e4f97d598ee81ce8f98838ed

Observation 908556a0-3dce-45a4-be5e-46189927dcf9 · outbound

This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation

Reference 36

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source=pdf_text observed=2026-08-15T21:05:17.438082Z digest=sha256:1565347febc93824b0e80e22c208561436bb4f0d727effa9fc7772eccb7ceba4

Observation b5da9e42-6e15-4e3a-9b62-69759fa420d3 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 37

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Observation cbf10fd8-69ae-4d28-a2f4-068400f48398 · outbound

This paper cites Mmcode: Evaluating multi- modal code large language models with visually rich programming problems, 2024.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Mmcode: Evaluating multi- modal code large language models with visually rich programming problems, 2024

Reference 38

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.446457Z digest=sha256:a625b247d48b8368528a1f43f13345640dad65b65b10f0a3169d0b57267ccaf4

Observation 99ec4a73-7b09-4444-af12-1e58b91d8d04 · outbound

This paper cites SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models

Reference 39

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source=pdf_text observed=2026-08-15T21:05:17.450679Z digest=sha256:6d5c552c50317327a7497d80b1b49bed88038bef4ad74a7700015674faada7cb

Observation 5636450b-7d98-474f-b87a-c3cda333cc39 · outbound

This paper cites Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach

Reference 40

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source=pdf_text observed=2026-08-15T21:05:17.455364Z digest=sha256:96da0579f648c94589daa4c1dedf5495a908e101dbd40e5b0f40539c6c0040b7

Observation a922e96a-11a4-4514-b2bd-68eeeb4aa23f · outbound

This paper cites Images are achilles’ heel of alignment: Exploiting visual vulnerabilities for jailbreaking multimodal large language models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Images are achilles’ heel of alignment: Exploiting visual vulnerabilities for jailbreaking multimodal large language models

Reference 41

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source=pdf_text observed=2026-08-15T21:05:17.460803Z digest=sha256:92ebf60fe3872248881a061f1709601c412630ab9a38cde22d1e028c7d57b7c5

Observation 88af99e7-e5bb-47f9-8d82-6c07a42e7bb9 · outbound

This paper cites Mitigating the Alignment Tax of RLHF.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Mitigating the Alignment Tax of RLHF

Reference 42

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source=pdf_text observed=2026-08-15T21:05:17.465819Z digest=sha256:e411bb486da6454155221773c1c16a4e78f61ea124acc8b64d054c39bd5555df

Observation fd904c7b-fa90-4073-ad78-93f07df5ee81 · outbound

This paper cites ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

Reference 43

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source=pdf_text observed=2026-08-15T21:05:17.470235Z digest=sha256:1cb589a10a8817e968ee7f1f2c74c8985eddc725a4a3ec7e49d6fd203f910def

Observation 407c0151-94ac-43a0-8876-258193b23580 · outbound

This paper cites Visual instruction tuning.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Visual instruction tuning

Reference 44

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source=pdf_text observed=2026-08-15T21:05:17.474745Z digest=sha256:7ecf1971d8821512a76c00a876534d53df8c0f3bf31cabd5e1530a315ebbc9b3

Observation c4f63714-f7ac-4731-be31-c4345d175f20 · outbound

This paper cites Improved baselines with visual instruction tuning.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Improved baselines with visual instruction tuning

Reference 45

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source=pdf_text observed=2026-08-15T21:05:17.478810Z digest=sha256:8d0d8da57f7bf5a1212c3f7970a9e2bd4f0284e5c23bdeb4f1395fcfe2678324

Observation c5806708-77f4-49b1-82bc-68be2b03e35b · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, January 2024.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Llava-next: Improved reasoning, ocr, and world knowledge, January 2024

Reference 46

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source=pdf_text observed=2026-08-15T21:05:17.482772Z digest=sha256:947fc7747a8433ca26ad0476308f56a55bc62aa62a06aa414517fe70e8d9e130

Observation eedd605e-41f3-43d9-9dbe-ebb9209e4fb1 · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety Alignment Gap

Reference 47

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source=pdf_text observed=2026-08-15T21:05:17.486536Z digest=sha256:51aa3d8b627597ffa82999f2ce6aff8d6a775b1a7e6b47728af54bea9e391e04

Observation 565b0618-a7b4-472c-89e5-698cae603b30 · outbound

This paper cites Guardreasoner: Towards reasoning-based llm safeguards.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Guardreasoner: Towards reasoning-based llm safeguards

Reference 48

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source=pdf_text observed=2026-08-15T21:05:17.490793Z digest=sha256:b336cbc8ec01d731fd48e0af7189275c432f03f88998e49222ee87bf5eec5501

Observation 94989d6d-777b-4466-9265-49011f8afb08 · outbound

This paper cites Safety Alignment for Vision Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Safety Alignment for Vision Language Models

Reference 49

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source=pdf_text observed=2026-08-15T21:05:17.495093Z digest=sha256:7524ac75c68df99516b6c3f42e1c3974dc53feb5452243915a738eb09224adbc

Observation b13de2ec-cefc-418e-bb67-ae37a45e9e7a · outbound

This paper cites Trojvlm: Backdoor attack against vision language models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Trojvlm: Backdoor attack against vision language models

Reference 50

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source=pdf_text observed=2026-08-15T21:05:17.500116Z digest=sha256:6e0400ed74c6df4bb245db51a1bccbd97fc0f149d583e50969fe1e35069e81e6

Observation 6c993b70-72f8-4b99-a2d1-0c9f408e0f74 · outbound

This paper cites Backdooring Vision-Language Models with Out-Of-Distribution Data.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Backdooring Vision-Language Models with Out-Of-Distribution Data

Reference 51

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source=pdf_text observed=2026-08-15T21:05:17.505279Z digest=sha256:aa393646265ae98bb3111e9aa006c14dd70de447b6acd2fb23e43451c641bea5

Observation 350a171a-cd5e-4519-bbec-485e8f6d169b · outbound

This paper cites Dolphins: Multimodal language model for driving.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Dolphins: Multimodal language model for driving

Reference 52

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source=pdf_text observed=2026-08-15T21:05:17.510185Z digest=sha256:270e9bcaf6de076248b5b266ad4597441fe51f4b9678648c4b6d45ba100f01a4

Observation 22dfca19-9866-4f6c-b324-6ddc483fcc49 · outbound

This paper cites A holistic approach to undesired content detection in the real world.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning A holistic approach to undesired content detection in the real world

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-15T21:05:19.024167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.514378Z digest=sha256:599898e0ef1c45444d9f0c2e8b973861cd8f69128405d1acb648c0e008ac2bc8

Observation ba2a63e5-328d-4701-b870-8efbd56f3f30 · outbound

This paper cites HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 54

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source=pdf_text observed=2026-08-15T21:05:17.518724Z digest=sha256:42059a80957bfaea23b198cce914e3a82b9b88e86551f0289d83524f90bf69b6

Observation a46a5da9-a4b7-4621-8e4c-82c1d8a4e3f9 · outbound

This paper cites UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models

Reference 55

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source=pdf_text observed=2026-08-15T21:05:17.524243Z digest=sha256:008ac4bf86f91b1195c4b1dc30fc1274a2cddc798a59c4dfd6a7fb45c1c9d9e0

Observation bd9910a0-561f-49e1-a6d8-b86270eb78b3 · outbound

This paper cites Learning to reason with llms.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Learning to reason with llms

Reference 56

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source=pdf_text observed=2026-08-15T21:05:17.528943Z digest=sha256:b61818e0c709e75d684a0e4b60baca26586442f019f868b671d393eca5813fd5

Observation 322483f4-974b-4bba-9ae5-53f5c5e55108 · outbound

This paper cites Openai o3-mini.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Openai o3-mini

Reference 57

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raw_fallback, observed 2026-08-15T21:05:19.001302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.533140Z digest=sha256:35384e7de9094656bf219b26ea2fa502e1f5cf1293961a80d4207decb6410f55

Observation 495f02cd-25cd-401f-979a-ada6748a15dc · outbound

This paper cites Training language models to follow instructions with human feedback.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Training language models to follow instructions with human feedback

Reference 58

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source=pdf_text observed=2026-08-15T21:05:17.537436Z digest=sha256:3639414ebbeb8149407324b9464be3137acc80c91cb7eeac7e818492aeab5c53

Observation dea9b7f1-2c64-443b-9837-d1f6c9125e4f · outbound

This paper cites LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL

Reference 59

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source=pdf_text observed=2026-08-15T21:05:17.541570Z digest=sha256:5a22937a201cca2a94d5602fef03cf17a02a7d32e23269a6c2aa5dc94e206eb4

Observation 2ea1e733-27eb-4a17-9008-8ba95f417416 · outbound

This paper cites MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

Reference 60

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source=pdf_text observed=2026-08-15T21:05:17.546000Z digest=sha256:1343e15a4a639370e542a9505f9b5232900ca22febcbc74d6899153b12988722

Observation e5e7da1d-e41f-4d83-8030-8ca66851019e · outbound

This paper cites UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images

Reference 61

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source=pdf_text observed=2026-08-15T21:05:17.550364Z digest=sha256:77e8d56655ff7429858684eb60f79b80312cf03f40577a081752a87265a99b43

Observation 3f34bf84-9f7a-477d-9f4f-64922fceec6c · outbound

This paper cites Learning transferable visual models from natural language supervision.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Learning transferable visual models from natural language supervision

Reference 62

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source=pdf_text observed=2026-08-15T21:05:17.554503Z digest=sha256:b7860f0c35a749731c96785f6fd8317a343a80332486511b4e9706c10569e7c7

Observation 9e006d53-2693-4f53-a20d-e45eaf1afb7c · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Direct preference optimization: Your language model is secretly a reward model

Reference 63

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source=pdf_text observed=2026-08-15T21:05:17.558639Z digest=sha256:fd2e43275549c01c0423d4ec9530792fbbaa42616f5ea0c43a81a210e9d6e2d8

Observation 98da4e58-7aed-4619-a01d-c242a7f062dc · outbound

This paper cites XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Reference 64

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source=pdf_text observed=2026-08-15T21:05:17.562852Z digest=sha256:c483fa6a717355295bf1d1f2d1ae21adbdcdacabba3d4597607ce2968693049b

Observation c9afbc52-2f71-43f2-9bb4-f37455b21ef2 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 65

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source=pdf_text observed=2026-08-15T21:05:17.567008Z digest=sha256:f98da2583d9572d33a6b9c5320a8db8ddb423656a94e5e2b7b1acf96d9756761

Observation 1a629905-c02e-4eea-8856-54fc26684e73 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 66

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source=pdf_text observed=2026-08-15T21:05:17.571466Z digest=sha256:8522b20a3f3d74eacfa64ec6dc97f1e57d85fb5540635e70f8fc8330ccc9e61d

Observation d7460475-7a16-4ef9-8d78-913abeeb5ac3 · outbound

This paper cites Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors

Reference 67

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source=pdf_text observed=2026-08-15T21:05:17.575913Z digest=sha256:d62904d95cf0ce7d81b666df1b850188c68598f92eaad985cf903c10ffb73f6b

Observation 54b63eb0-ee64-44d8-88fd-c300ce913979 · outbound

This paper cites Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku

Reference 68

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source=pdf_text observed=2026-08-15T21:05:17.580157Z digest=sha256:eaa5a4ad9810dfcd0d94eb70722a0b59720a7df585e09d05b8d21ee53d129e22

Observation 176ad5d5-e7b9-4213-97c8-79acf6d62d6b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 69

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source=pdf_text observed=2026-08-15T21:05:17.584306Z digest=sha256:13905e833fdf9984a2d8ddbbc8945750d677bde137908ee4e2db05ee84c471f2

Observation 8d5a7dd0-5df2-4b29-907a-c88d3c7a5022 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Gemma 2: Improving Open Language Models at a Practical Size

Reference 70

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source=pdf_text observed=2026-08-15T21:05:17.588467Z digest=sha256:0765187c2edcb2fae0279a58d503847be3960ca09ae1f1d070c139016d3e897f

Observation e784db5b-f00e-47d0-92b5-fe31e3095eac · outbound

This paper cites Introducing deep research.https://openai.com/index/introducing-deep-research/, 2025.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Introducing deep research.https://openai.com/index/introducing-deep-research/, 2025

Reference 71

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raw_fallback, observed 2026-08-15T21:05:18.952334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.592967Z digest=sha256:56aae56c816c8b8d40bb29e60dd650d6df3443510241140bf9627d455d8b28a8

Observation dfcd2088-ae27-431f-971b-259511c2c9ae · outbound

This paper cites Introducing chatgpt.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Introducing chatgpt

Reference 72

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verified fuzzy
raw_fallback, observed 2026-08-15T21:05:18.937906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.597250Z digest=sha256:b7155bddb65579b51811baef65ebcfaea4b181c7d9dec81fc821bee57fc3c949

Observation 8cd67b09-f600-425b-9390-538f44797f8a · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning LLaMA: Open and Efficient Foundation Language Models

Reference 73

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source=pdf_text observed=2026-08-15T21:05:17.601408Z digest=sha256:2dbe89a98011182a89169da18289f6b66d20551bee1786dfdbca570c6975517e

Observation 4c37457a-a755-420c-b7a8-64a193dd5e3f · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 74

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source=pdf_text observed=2026-08-15T21:05:17.606554Z digest=sha256:0efff995f8181ca4cfccf9133910c2550e4eeadc22aae25adad7731bc0ab715d

Observation f57258b7-1e11-40f5-881c-523b8d93e231 · outbound

This paper cites SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models

Reference 75

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source=pdf_text observed=2026-08-15T21:05:17.610727Z digest=sha256:26e36d97bfa451b30e724670ce63b05fd874ee4b3d77ec345fe8fbc8ffdef061

Observation cb792141-ca0b-4196-95c1-78edaee0fe2d · outbound

This paper cites Measuring multimodal mathematical reasoning with math-vision dataset.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Measuring multimodal mathematical reasoning with math-vision dataset

Reference 76

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verified fuzzy
raw_fallback, observed 2026-08-15T21:05:18.924012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.615213Z digest=sha256:a9f29a417aecdd903b301e287d77891d4764cbcb249441f9614493be61102188

Observation 0410eec4-6da3-4d40-b259-8077c786f5c0 · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 77

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source=pdf_text observed=2026-08-15T21:05:17.619466Z digest=sha256:4e9aa678635e4582c2ed19dfe34a238a659fa8139ab970a70d2879debec86fdf

Observation 7570a97b-53aa-4b6b-83d0-9148f392dde3 · outbound

This paper cites InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance

Reference 78

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.624914Z digest=sha256:2fab832fb2f5e7f632de932c747b9679a3dec8e4b217ef5445582b7968dad3af

Observation 3bf8de48-2809-4101-991a-ce9ab1768fa5 · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Adashield: Safeguarding multimodal large language models from structure-based attack via adaptive shield prompting

Reference 79

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raw_fallback, observed 2026-08-15T21:05:18.909918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.629762Z digest=sha256:5c3f2ae461db6d3c13292efc0cb8022628a59431a1df86b8ffa4b87a01fb152d

Observation af966ca0-c6bf-48e9-be01-f3095f067f15 · outbound

This paper cites FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis

Reference 80

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no resolver link, observed 2026-08-15T21:05:17.633724Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.633724Z digest=sha256:f1db8da81c55393e7eb88520c2336e18176736d4516415d7c0d865a2efefbe0b

Observation 69ed006a-72cf-4d12-8bd9-e83d56a72687 · outbound

This paper cites Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training

Reference 81

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.638168Z digest=sha256:85b88e8d3c74308f941aae8d1a2aa9516d498107815d23c99b6b6855cf19344f

Observation 75699b0f-f82f-4117-ba8d-787d3a1ccafa · outbound

This paper cites Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments, 2024.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments, 2024

Reference 82

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no resolver link, observed 2026-08-15T21:05:17.642856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:05:17.642856Z digest=sha256:d4365d20bacbf54455ed2a9cc159c3decfddc69732b5aadbd4bd1111fe71e9da

Observation 36c59657-8a5f-47a0-93b0-37920037b8c9 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 83

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no resolver link, observed 2026-08-15T21:05:17.648013Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.648013Z digest=sha256:164316071cd17f9bf3b8d23ef5f59263f38131a4265d8f9a773c1d4ca6221fb1

Observation dae07785-89cf-4442-9190-6316d7c3b48b · outbound

This paper cites Qwen2.5 Technical Report.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Qwen2.5 Technical Report

Reference 84

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no resolver link, observed 2026-08-15T21:05:17.652638Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.652638Z digest=sha256:f3a2d07a6f7f23bc87ade94b9b62aee1ddc16ee040195ff85b794c06633b66b2

Observation b0c04a05-0cf3-4594-a805-247db0fc2cfd · outbound

This paper cites R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Reference 85

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no resolver link, observed 2026-08-15T21:05:17.658163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:05:17.658163Z digest=sha256:dcbcc4cd40fc1671e0a25ccdb6f27ee1e3a32771ce72b39cd57e4571a875bcf8

Observation 37de5f92-a0df-48d1-9695-13beff68f5ff · outbound

This paper cites A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations

Reference 86

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no resolver link, observed 2026-08-15T21:05:17.663337Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.663337Z digest=sha256:a978eea5607a3f565f4c231548f1f9ae1d82c00d2c36dcd90f132b04d4e847c0

Observation 697064b2-2ed7-4c2a-8bd7-9fd8e551c40c · outbound

This paper cites Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts

Reference 87

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no resolver link, observed 2026-08-15T21:05:17.667716Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.667716Z digest=sha256:dde3f143a6a6bb77384206fbeb3ecf2af65f18662a33d5de3ff0435fc13c7c6f

Observation 93d880d1-eb2f-485b-a5af-ec6172f42702 · outbound

This paper cites Bad news: Clickbait and deceptive ads on news and misinformation websites.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Bad news: Clickbait and deceptive ads on news and misinformation websites

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:05:18.877089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T21:05:17.671772Z digest=sha256:f83001479d90158319cc6e2b3d2033b5ec3317ac42b34f483fb6b1dccb1bf602

Observation a263dac6-bd43-4e38-852f-cef4217c13d9 · outbound

This paper cites ShieldGemma: Generative AI Content Moderation Based on Gemma.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning ShieldGemma: Generative AI Content Moderation Based on Gemma

Reference 89

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no resolver link, observed 2026-08-15T21:05:17.675851Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.675851Z digest=sha256:0626205dc1da2e7df9498f50453eb5764cf5be8f4c7c5b78b61335fdac72f9a9

Observation 82ce7c0d-7aab-4be3-b927-d3944b69929e · outbound

This paper cites JailGuard: A Universal Detection Framework for LLM Prompt-based Attacks.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning JailGuard: A Universal Detection Framework for LLM Prompt-based Attacks

Reference 90

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no resolver link, observed 2026-08-15T21:05:17.680565Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.680565Z digest=sha256:69d3cf1b0863cf26ac5dc6e7a8dfda16a13d789575920ab3587a74dccd991ba1

Observation 9c06d25b-f0e5-44d6-9b3c-896d0ff2e226 · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model

Reference 91

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source=pdf_text observed=2026-08-15T21:05:17.684763Z digest=sha256:e99f262cb725d0d23a6a3a4bf645f7338cc84703addd1ec33f1cedff520bbf7f

Observation 6ffe61f5-78b3-4105-aaa8-a6843d8e7136 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 92

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no resolver link, observed 2026-08-15T21:05:17.689104Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.689104Z digest=sha256:a886c5679ca7551a6050aae9369ca687c4ab8f16c68a8ac53fca5da6ca54c5d2

Observation fa293d81-72aa-451b-a037-650627d50755 · outbound

This paper cites Easyr1: An efficient, scalable, multi-modality rl training framework.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Easyr1: An efficient, scalable, multi-modality rl training framework

Reference 93

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no resolver link, observed 2026-08-15T21:05:17.693337Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.693337Z digest=sha256:d1a963747d03dd279a0ab77df14a3c7dfd12c42ae2e3c0f13e0632604d147063

Observation 251eb9f7-da9f-433d-8452-a1b098bfe3a1 · outbound

This paper cites Visual In-Context Learning for Large Vision-Language Models.

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Visual In-Context Learning for Large Vision-Language Models

Reference 94

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source=pdf_text observed=2026-08-15T21:05:17.697495Z digest=sha256:ce42cd7943c87f6e2de1b8c3fed7b11829cea2c8393c31d0bcf1a0489d1728e8

Observation 553a30e5-35a0-4aa7-bd48-09c04608b4cd · outbound

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

GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Reference 95

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:05:17.701839Z digest=sha256:3ee87a435967fce8d35073213215d1ce4ae5861d6602078f1d775e38ff4a3ede

Pith citing papers

Observation 051c2e60-111f-4a55-af41-74eb7cd6d308 · inbound

Backdoor Cleaning without External Guidance in MLLM Fine-tuning cites this paper.

Backdoor Cleaning without External Guidance in MLLM Fine-tuning GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 45

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no resolver link, observed 2026-08-07T14:56:52.768314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:56:52.768314Z digest=sha256:6d6b896f8b1e953968870daa6c0cb23233f965cd219155fc7dcbedd53bc6fb6c

Observation c410194b-5efe-44fa-be0b-30da1f4a9e0f · inbound

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models cites this paper.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 45

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no resolver link, observed 2026-08-06T23:50:58.778782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:50:58.778782Z digest=sha256:1b57e4f86a7d825b5a61619c4747b989cf4cb4af66f24adc5f48a2c2d1fb3c80

Observation 95e07cbb-957b-427a-85e6-c69613736d1c · inbound

Agentic Web: Weaving the Next Web with AI Agents cites this paper.

Agentic Web: Weaving the Next Web with AI Agents GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 142

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no resolver link, observed 2026-08-06T13:05:39.587941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T13:05:39.587941Z digest=sha256:896d53ecc1ca3888420c2afd3f1dcb2f09ded5268686b4db18071f96eeac2908

Observation 60da59e6-f61a-459e-bab8-d9b9168187f0 · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 133

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no resolver link, observed 2026-08-05T10:39:06.976193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:39:06.976193Z digest=sha256:57605eb0b01c8db5f594f4230cc5d216221bd4c8f809cc99a8b17d8f6d6ca982

Observation 3d6b8d6d-4372-4639-b0c1-1623d1bf4361 · inbound

EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models cites this paper.

EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 33

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no resolver link, observed 2026-08-02T20:14:04.067928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:14:04.067928Z digest=sha256:a5552ca58a66581bffadc2bfbbab2af56e392d959bb69aa7f7f86f8d6d252357

Observation c30ea03c-5e62-460b-93eb-e59047fb9000 · inbound

RuleSafe-VL: Evaluating Rule-Conditioned Decision Reasoning in Vision-Language Content Moderation cites this paper.

RuleSafe-VL: Evaluating Rule-Conditioned Decision Reasoning in Vision-Language Content Moderation GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T03:15:54.719220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-11T03:13:05.662351Z digest=sha256:2289b4ad74a81c22c2d31adebe6800a035c95237b83d8d067a7982287cec5d5a

Observation b0705dee-1da8-4612-8130-928437d6063e · inbound

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations cites this paper.

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 52

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verified exact
arxiv_id, observed 2026-05-14T20:19:26.804410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-14T20:13:10.814899Z digest=sha256:908a6feda6c651204d587d562ff8617e2762ebb585c082fa0f01329f1ccbf6e4

Observation 486c9195-9b14-4023-bb9b-b966f0c70219 · inbound

WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections cites this paper.

WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

Reference 39

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T14:45:49.566560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-30T20:16:13.413064Z digest=sha256:5a571d1046a0b06f4780cc7102effbe1bb07217d16180f6358eab3507c65e82c