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Paper Citation Record · LEDGER

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation

As of 9 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2608.05210.

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

pith.paper-citation-record.v1
2608.05210 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:13:19.650039Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

  • verified exact1
  • verified fuzzy41
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b8df51f1-c909-4fc6-88ac-79019b0d1435 · outbound

This paper cites Introducing Claude Haiku 4.5.https://ww w.anthropic.com/news/claude-haiku-4-5, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Introducing Claude Haiku 4.5.https://ww w.anthropic.com/news/claude-haiku-4-5, 2025

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.700617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.373132Z digest=sha256:bc06c89401c870799ac4a2bbc89c5b7f94028e48eadf1725910eb263173a2611

Observation 4e95f0c7-1d70-4b64-a5f9-08dd466e2f5b · outbound

This paper cites Prompting for Multimodal Hateful Meme Clas- sification.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Prompting for Multimodal Hateful Meme Clas- sification

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.685512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.378929Z digest=sha256:e09ddcafe25b467a984b588f4137ddaa64e9d17d38710abdc38f98a09a3e7083

Observation 0c4cb8b6-fa16-40d1-8deb-816a7bfbcaa0 · outbound

This paper cites JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-08T18:13:19.384136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:13:19.384136Z digest=sha256:db9503817dccace93e7d74682eda3451f24b351b452435970a7d3a809f077c59

Observation 08aa5874-bd96-41be-ab41-0201d0c36519 · outbound

This paper cites Neeko: Model Hijack- ing Attacks Against Generative Adversarial Networks.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Neeko: Model Hijack- ing Attacks Against Generative Adversarial Networks

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.669160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.389552Z digest=sha256:27bdbfd337b75c3bc40847cf66f0e781a383867406540460a9d48bfe91a034cd

Observation cbe96fd7-32ed-4183-b62c-e7fe42417532 · outbound

This paper cites Jail- breakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Jail- breakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.652429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.395036Z digest=sha256:047868e7f7fb8e011f203ee7f363ca7d2664a80933f2799c771d3e8a0942c5ed

Observation fae457ce-3a89-48a0-9e2e-bf7620c4ac60 · outbound

This paper cites Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-08T18:13:19.400840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:13:19.400840Z digest=sha256:c88af36d2a13dbf5514da030bae72b2cdf3976ca61f9ba5ae87ee940c2764b57

Observation 79a956ef-5b84-4c13-933a-81432d48b1ac · outbound

This paper cites Reading Poison: Science and Story in Nazi Children’s Propaganda.Children’s Literature in Education, 2022.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Reading Poison: Science and Story in Nazi Children’s Propaganda.Children’s Literature in Education, 2022

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.638366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.408183Z digest=sha256:a8499301521be73359a69520fb3de6a997b7ed24291610573392e51dccc5d7f0

Observation 567f7a67-537c-4c18-a30c-51c3b81ad870 · outbound

This paper cites Gemini 2.5 Flash Image.https://ai.goo gle.dev/gemini-api/docs/models/gemini-2.5- flash-image, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Gemini 2.5 Flash Image.https://ai.goo gle.dev/gemini-api/docs/models/gemini-2.5- flash-image, 2025

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.623127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.412877Z digest=sha256:dfef630b400a583c32e4d12f4947f0e6668941dd164e3a8f2d93c25b38cea043

Observation 5322831a-8378-44ce-9a95-608c2cdb9311 · outbound

This paper cites Gemini 2.5 Flash Image (Nano Banana).http s://ai.google.dev/gemini-api/docs/models/ge mini-2.5-flash-image, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Gemini 2.5 Flash Image (Nano Banana).http s://ai.google.dev/gemini-api/docs/models/ge mini-2.5-flash-image, 2025

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.604429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.419780Z digest=sha256:53550f1abc0330caff4b7654a035baf2048b85868b976b9463011de1de4d9eeb

Observation 82a98755-a5e4-4c43-aa75-685426af651d · outbound

This paper cites Gemini 3 Pro Image.https://ai.googl e.dev/gemini-api/docs/models/gemini-3-pro- image, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Gemini 3 Pro Image.https://ai.googl e.dev/gemini-api/docs/models/gemini-3-pro- image, 2025

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.586091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.426092Z digest=sha256:ec14c32b08683403222185b9ebb02b4905cae33b7b561b594cc251168ddafbd7

Observation 3a5ad046-a450-431e-b769-415fd98ca468 · outbound

This paper cites Gemini 3.1 Flash Image Preview.https://ai .google.dev/gemini-api/docs/models/gemini- 3.1-flash-image, 2026.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Gemini 3.1 Flash Image Preview.https://ai .google.dev/gemini-api/docs/models/gemini- 3.1-flash-image, 2026

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.568555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.431418Z digest=sha256:6e302610f2663b4e568841e645217e5e8471a46a9db9d5b5fe6a59d47df631f3

Observation d0f80d7f-65ee-4f57-a5aa-35a6965e2e61 · outbound

This paper cites Gemini 3.1 Flash-Lite.https://ai.googl e.dev/gemini-api/docs/models/gemini-3.1- flash-lite, 2026.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Gemini 3.1 Flash-Lite.https://ai.googl e.dev/gemini-api/docs/models/gemini-3.1- flash-lite, 2026

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.553620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.435807Z digest=sha256:435a5a99e8290869a1abde5702456ea25bd53b22a125551887a57041fcb85b22

Observation c042b95e-54ab-4359-b0e0-74d85fe888cd · outbound

This paper cites an unresolved cited work.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-08T18:13:20.537842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.440898Z digest=sha256:320fd315b3033c592fb8f54e3f1dc7fa9707c37d13ab194a9cab95af6f27bdda

Observation 6620bbcf-2450-475a-a28c-1bae05bcebd4 · outbound

This paper cites LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.521938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.446041Z digest=sha256:5ad2a2b6f79b4ab2e74d21e951dec21d2a74bbe598d3c7285bf1aec057336b30

Observation c6f4fb43-8b31-4be6-89e9-92f102bc4e2a · outbound

This paper cites LlavaGuard.ht tps://github.com/ml- research/llavaguard,.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation LlavaGuard.ht tps://github.com/ml- research/llavaguard,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.506781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.451607Z digest=sha256:c0cbf34d1a1f16dab62de0a22ae82c8dd7965cb8a7d0dd0d55d3326cf5bc2924

Observation 5f1f6224-5ed1-4a43-b2e2-7012938380ca · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.490426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.456528Z digest=sha256:11da7a0d7cfac405045cb49dd2fa2f50d8de16e81a8fc0ed81e3e8c91f034679

Observation b4ac8935-75b3-4e0a-a026-bb429233c6ee · outbound

This paper cites JailbreakDiffBench: A Comprehensive Benchmark for Jailbreaking Diffusion Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation JailbreakDiffBench: A Comprehensive Benchmark for Jailbreaking Diffusion Models

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.473767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.461109Z digest=sha256:896d6971ba7716ce5381a1ce66191cfcc065f986c2069ef7c6b1c237579805fe

Observation d4911182-6795-409b-849f-33964424a3b7 · outbound

This paper cites Experiment with Gemini 2.0 Flash Native Image Generation.https:// developers.googleblog.com/experiment-with- gemini-20-flash-native-image-generation/,.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Experiment with Gemini 2.0 Flash Native Image Generation.https:// developers.googleblog.com/experiment-with- gemini-20-flash-native-image-generation/,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.457062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.465619Z digest=sha256:3187cbdc39177a43dd52e56350e8889376d28fb1f80a601c5c1596b821f4d947

Observation f90e6fb7-1586-41bf-a8a1-f2b201929087 · outbound

This paper cites The Hateful Memes Challenge: De- tecting Hate Speech in Multimodal Memes.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation The Hateful Memes Challenge: De- tecting Hate Speech in Multimodal Memes

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.440445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.471574Z digest=sha256:d8fbb080f019424a4ea3a445a3603fdca53dc9679498f91d79dee711f7b89f35

Observation 4eb9a4e9-fa8d-4d6f-99e4-5f359f54d28c · outbound

This paper cites Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-08T18:13:19.477317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:13:19.477317Z digest=sha256:a2d18034f4c45ced1a86e1c9f4c089790a3030e0c58bbd7c67f9543fd6ca7596

Observation 9cdbc5b3-f96f-4936-969b-27f3b6eba75a · outbound

This paper cites When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.425405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.483022Z digest=sha256:aa0be21f1a76f398ff1f4973cb34ab63be62f5d6e82922bd561f57e96e3e3e76

Observation 0e25186b-e2d5-4ca8-b163-51a22d110d67 · outbound

This paper cites T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Pri- vacy in Image Generation.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Pri- vacy in Image Generation

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.406392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.488345Z digest=sha256:c198124aa650b2b31d2b1d504305883054071f4db64fe4c2148b979e5db496bf

Observation 53db67f4-9c8c-4c3d-91ff-e286c378dda0 · outbound

This paper cites From Meme to Threat: On the Hateful Meme Understand- ing and Induced Hateful Content Generation in Open- Source Vision Language Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation From Meme to Threat: On the Hateful Meme Understand- ing and Induced Hateful Content Generation in Open- Source Vision Language Models

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.391393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.492657Z digest=sha256:ed316694fddc918cb4f425921db714a1abf6f1b7d3f4c14ec0e44cdd62b6c5d0

Observation df55676e-648f-4e95-a10e-73c2a9887cb8 · outbound

This paper cites Improving Hateful Meme Detec- tion through Retrieval-Guided Contrastive Learning.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Improving Hateful Meme Detec- tion through Retrieval-Guided Contrastive Learning

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.376188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.497038Z digest=sha256:42c74f70bcda9b9e3db9176a5e3b42e5abdd9e2a2929a9ea6c8bb1f7c78d6d85

Observation 30b5bc6b-abc8-4194-a761-80fef94b5b8a · outbound

This paper cites Llama Guard 4 12B.https://huggingface.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Llama Guard 4 12B.https://huggingface

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.358252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.501380Z digest=sha256:c3f8c7fa3d849aea950d459840c7256227de3d49305a04aebd0aaad02b2af6e2

Observation fd34bc4a-a943-441f-ac19-7626a846439a · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-08T18:13:19.505754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:13:19.505754Z digest=sha256:adbf7c3aa4d7a00140c2d1bf616aff24ced0113fcc5f2a28bff2dd9eb30dfcc7

Observation 890fb396-1a31-4fb4-8779-af9465645c87 · outbound

This paper cites an unresolved cited work.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-08T18:13:20.342851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.510936Z digest=sha256:46807db08162c2f257bcec34440b0bbaa3db0fcb0bfebfe799b22ac41dcfe6c0

Observation b3c3b240-a7f1-4067-8724-309dac68b20b · outbound

This paper cites OpenAI Moderation API.https://develo pers.openai.com/api/docs/guides/moderation.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation OpenAI Moderation API.https://develo pers.openai.com/api/docs/guides/moderation

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.328673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.515688Z digest=sha256:df651acf70de6066bb57aed351a6830b0fef3af55bd39749dbf14e2c63f06696

Observation 4418e2c2-c19b-4bdb-9ea0-5d54339ad475 · outbound

This paper cites GPT Image 1.5.https://developers.ope nai.com/api/docs/models/gpt-image-1.5, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation GPT Image 1.5.https://developers.ope nai.com/api/docs/models/gpt-image-1.5, 2025

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.312374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.521670Z digest=sha256:0438ba4952dd70395bd6245d1278e01c52abfabf1821fd4ca17698c73f366c7a

Observation c76d6114-50ba-4d8d-8e37-6d4cc7bd7f0f · outbound

This paper cites Introducing 4o Image Generation.https: //openai.com/index/introducing-4o-image- generation/, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Introducing 4o Image Generation.https: //openai.com/index/introducing-4o-image- generation/, 2025

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.296477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.526757Z digest=sha256:aaba387dd4721b147d9abe263e1c0f17d64fa6f4a4e7ca6321a6e3dea6a400e2

Observation 4aa3056c-e6aa-45fd-a0fc-af2aa2d45e33 · outbound

This paper cites GPT Image 2.https://developers.opena i.com/api/docs/models/gpt-image-2, 2026.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation GPT Image 2.https://developers.opena i.com/api/docs/models/gpt-image-2, 2026

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.278746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.531884Z digest=sha256:394a884f23f3dd59554c8ce3ff874ab3ac103842089ed526769d1aebb6dbc06a

Observation 10779891-57c5-4f9f-8b03-5648297fb04b · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-08T18:13:19.538303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:13:19.538303Z digest=sha256:591138185548e923f508eeeb6dadb166c18284fcd4c1334351fcc64fc92852ae

Observation 6945bbc6-b498-4e51-b2ca-19ee1315c39a · outbound

This paper cites Unsafe Diffu- sion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Unsafe Diffu- sion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.261149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.543433Z digest=sha256:9f40361142df4abbaec95331029e94c66530f9d4ac8c265c5a1ed8551c208540

Observation 439c9b23-1290-42ca-9f44-30c2eba08625 · outbound

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

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.245503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.548359Z digest=sha256:4cc0ff60639dc3fcc7e840cc1232b2dd52be9f4a0e344a47ad17e11c4b26ffdf

Observation b1a62c91-c177-4ad5-b4ae-f9d063e0a3e7 · outbound

This paper cites Zero-Shot Text-to-Image Generation.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Zero-Shot Text-to-Image Generation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.227383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.554229Z digest=sha256:0cca19493b8b61fb77ee5819a4485d74439636dbb026b978151c8d5a9bab741a

Observation f034c1a4-3082-460e-aa0a-21b420e44dcc · outbound

This paper cites High-Resolution Im- age Synthesis with Latent Diffusion Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation High-Resolution Im- age Synthesis with Latent Diffusion Models

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.210559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.559068Z digest=sha256:126c64cffcbe7e2914d6049ceff57e79ed26f3ddd929119f1246e5c66bcf628a

Observation 782aab3a-1c76-4c41-94db-90b0b2b2c72b · outbound

This paper cites Racist Videos Made with AI Are Going Viral on TikTok.https://www.theverge.com/new s/697188/racist-ai-generated-videos-goog le-veo-3-tiktok, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Racist Videos Made with AI Are Going Viral on TikTok.https://www.theverge.com/new s/697188/racist-ai-generated-videos-goog le-veo-3-tiktok, 2025

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.186340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.563832Z digest=sha256:110935cd1c572ce2c347b9bbbf91698837bbe7d4e360563dcb145c102fcc207e

Observation 1a1e55d8-a677-4503-a64c-1a4cbe305a35 · outbound

This paper cites an unresolved cited work.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-08T18:13:20.162186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.568087Z digest=sha256:cc50578d6e6bc6e76cfcd3ca51fb59e09c70326ab627e6e3165d6544cee7d5ab

Observation 9174ed47-80df-47c2-b9b8-eaef3782a02e · outbound

This paper cites Q16.https://github.com/ml- research/Q16, 2022.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Q16.https://github.com/ml- research/Q16, 2022

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.139000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.572537Z digest=sha256:8f762d25a0b6b0a39806b99401b887e054cf2806b8494972f889303a521e9d67

Observation 2c809939-5f03-4007-9ea3-9916a0da5670 · outbound

This paper cites Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.118847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.577045Z digest=sha256:04c6c19cf494506ade78ccd47fbd74098bbdd5c94c7bfb54aa9b69a15da67f8f

Observation c8711fe6-7d3b-4826-8c4a-83b9830f4344 · outbound

This paper cites Qwen2.5-VL-7B.https://qwen.ai/bl og?id=qwen2.5-vl, 2025.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Qwen2.5-VL-7B.https://qwen.ai/bl og?id=qwen2.5-vl, 2025

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.100429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.581521Z digest=sha256:746ae1ae5b4c5ab505343abc00a9a675c193905be83d114731935f064d155239

Observation e34c6a21-09e5-41e9-bc76-bf26b289e1d6 · outbound

This paper cites Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models? In International Conference on Learning Representations (ICLR), 2024.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models? In International Conference on Learning Representations (ICLR), 2024

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.076432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.586258Z digest=sha256:a51d8256f7d69ea55910d7d9033aaa1cd27cb861bc3d65531a3f58a3c2c62bed

Observation f81ef5d0-b5eb-48fd-a590-7fe4ebcf751f · outbound

This paper cites Chain-of-Jailbreak Attack for Image Generation Models via Step by Step Editing.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Chain-of-Jailbreak Attack for Image Generation Models via Step by Step Editing

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.048065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.591978Z digest=sha256:c4354b0245bb21f33e528b403231a744b31f7a7097f7c8d193cb9f89bbe58240

Observation 1fb079d3-1f93-4f80-a761-0878cc588708 · outbound

This paper cites Image-Perfect Imperfections: Safety, Bias, and Au- thenticity in the Shadow of Text-To-Image Model Evo- lution.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Image-Perfect Imperfections: Safety, Bias, and Au- thenticity in the Shadow of Text-To-Image Model Evo- lution

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:20.019847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.598043Z digest=sha256:5f2d8c78c1aa4dec5ee227095f9f0a0c21a770a144b6543384e93d2394eca403

Observation f28573e1-4410-4c46-8e7c-064e2f0a77ea · outbound

This paper cites MMA-Diffusion: Mul- tiModal Attack on Diffusion Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation MMA-Diffusion: Mul- tiModal Attack on Diffusion Models

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:19.995250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.605513Z digest=sha256:062c3850b3505cad9b9b3c609811fc959b73415845bf7478fee6e057d7ae0502

Observation 8bdf2703-ab58-4fcc-8fca-578a575a407e · outbound

This paper cites SneakyPrompt: Jailbreaking Text-to- Image Generative Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation SneakyPrompt: Jailbreaking Text-to- Image Generative Models

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:19.977717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.612102Z digest=sha256:4218c7f8c6a6e0c2a88108c7a9d53cb19bea8a9e3401521f2cf9513614f2d81d

Observation ceb928d8-9b90-4684-a4a5-26602c295775 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-08T18:13:19.618648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:13:19.618648Z digest=sha256:433c24f449315be9b088dcb11c0040fb0c21a8c1152f0cb087b00b2fff86bd78

Observation 7cac9bff-d814-4a16-aeac-e3b40a83c6ff · outbound

This paper cites When Safe Concepts Become Unsafe: Multi-Concept Compositional Vulnerabilities in Text-to-Image Models.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation When Safe Concepts Become Unsafe: Multi-Concept Compositional Vulnerabilities in Text-to-Image Models

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-08T18:13:19.728060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.624324Z digest=sha256:0b2e81ce60e5d473465e92839eddeb89b65a51813b5ae309f92c72f4a973d9e8

Observation 3bf2067b-4420-4b6c-a469-1454d154c9ea · outbound

This paper cites When Memory Becomes a Vulnerability: To- wards Multi-Turn Jailbreak Attacks against Text-to- Image Generation Systems.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation When Memory Becomes a Vulnerability: To- wards Multi-Turn Jailbreak Attacks against Text-to- Image Generation Systems

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:19.960914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.630248Z digest=sha256:49888748f4f2b72a069b53ca431518be50cbb3136257f27387431db3acb8450a

Observation a77f09cd-c6b2-44ad-af45-b6e8708dc13c · outbound

This paper cites an unresolved cited work.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-08T18:13:19.942429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.634976Z digest=sha256:f34e9e463348a1ee46abb7f341704ab55204c4b09757441abee785d633bc1e32

Observation 1e443072-a1da-41f1-bbf4-8578ede9db37 · outbound

This paper cites A meaning-bearing unit may be an event, state, utterance, comparison, revelation, or change in attitude that introduces information relevant to the overall meaning.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation A meaning-bearing unit may be an event, state, utterance, comparison, revelation, or change in attitude that introduces information relevant to the overall meaning

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:19.921725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.639536Z digest=sha256:0e4b06a41fb1b571f32f480a0dcf6cb2f08d82746ad591f924bb3ebebcfe2eca

Observation 8d89eeb5-03d9-433c-b293-8413d20a0117 · outbound

This paper cites an unresolved cited work.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-08T18:13:19.892069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.644698Z digest=sha256:cc6a5b73d58d0f55dc75698f4332c75e8278e90ec93569b55294dcf7c14e4757

Observation eb6f515e-25ea-4c92-9918-2fa20080397b · outbound

This paper cites Annotators evaluated the implication of the full narrative rather than requiring any individual sentence, event, or image to be independently hateful.

Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation Annotators evaluated the implication of the full narrative rather than requiring any individual sentence, event, or image to be independently hateful

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:13:19.871231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T18:13:19.650039Z digest=sha256:d5613827341e6fe70aea5e8aa90dc651025efcf830ed4572a5d0cc8904c16c3d

Pith citing papers

No inbound Pith citation observations are available.