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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 20 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.373132Z digest=sha256:31937495f669d1179108afd619a4843a49b93abd3e9ee8e39f90fe8f55f84378

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.378929Z digest=sha256:4d0a2efc761c15df14d3d31d28055f0f5be53730fed60c2bf0521f0b87c771c8

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:e993d8c556cb43070d3564f1336d685ce3215f5b34a343cef43aca94f5a9d93e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.389552Z digest=sha256:f2064342e0a94c0ebe6e5885662ad1a24d1c262164af02d88b76a585034deab4

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.395036Z digest=sha256:115efb49a616f9267ecff68fb5b42f53dfc7d81d5a3cea19b3a6481ba54f5b19

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:b5d3d327bc014d476e8fad9f8b522dc131ab478f356c036a946fdd0a5501b410

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.408183Z digest=sha256:087b2a874a7160cf1a31a60aa08e69cfee4bb05bb6119d9b0134a79a09ded0b7

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.412877Z digest=sha256:25018586cc030bb8237db564b0c7c2db4af069b3efe46016f5033e5e6b7bfc6f

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.419780Z digest=sha256:8cf4ff28f91d6aecbd91e3e6d8bbe2aaff304b613385f1ea7f82ff3de7623b75

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.426092Z digest=sha256:8b3cf73ac425f59edfe5218a052b81b9df13c2abf2d7e0427cadf57613ef5ea6

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.431418Z digest=sha256:82d141cf9f9b70c2b9725557e23365912671a0eea102ca55cedb49b9b1ed8141

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.435807Z digest=sha256:f4403537ca0e9acb8237470d071b4f1a414e88d35e788a8bb8bbad90e9c9f924

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.440898Z digest=sha256:694c5353f5affaca01ea24fe447784aadfbc6b000c6f5af27cb8bfe08834fa4f

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.446041Z digest=sha256:3d549cd3052e79a9358a069b931453d4057bf9ed4e26a0a49ed204b7f1316066

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.451607Z digest=sha256:18726c82bccf9b2ad4f0c835cf8db9debfc2af1ff8f108dd0364deb6c6e0b6be

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.456528Z digest=sha256:186209cb8cecfec7fc0a110fc862d7527454386d15dab20b24d1e62a6dc6228c

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.461109Z digest=sha256:d2cfa4b0e25eefb095e456b52143261f6c9b9571087d13c765ddc792c26bdf6b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.465619Z digest=sha256:ed29d2ea148c63dd4ce50ae96ebce1befe21c4902c6de6fb366c8dba3d4ba99e

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.471574Z digest=sha256:e45432723012a192d78064c45e53eda6bcf1484092b8ac3d3cfc815d19212d57

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=sha256:31bd835cec48a7ba66d20a50b11a775dada6f445ae02b2574e73ad74bb635caa

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.483022Z digest=sha256:b38416bbc52f01bb67b18e163d1f03af5710ad1b779c3e8a5edc57e6ec8ac8d7

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.488345Z digest=sha256:d1946bed4a56a95d8a8c8ab6763714e1921968571324502468ad9798f0605dfd

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.492657Z digest=sha256:dd6fd89e1c725f3e495be7da02ff85a8a69b7071130d165cc07c62fe63b00076

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.497038Z digest=sha256:003575a2e6bb92f36379794bcb54aa8e70dd1e230892042c7e8136b8ab5c1562

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.501380Z digest=sha256:fa21b049eab1a3e484a5fbaf9e3eafc525e90ff430644ade8247fab3b2e156a5

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:aba0cff8c0588806e56b5af5216629b5164c6185d765b3ece4f091615eb9d424

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.510936Z digest=sha256:0711413db474f5dba67beaeac6f89ff52b1b971cc4f123a01548d791d00a50c6

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.515688Z digest=sha256:9cdea0a7b0faebb134ac1fc58f6ce3a2daa9f9060b224ed01d7d268a9d6f82d7

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.521670Z digest=sha256:47235d921c4391ae591dd888a945d276178aa36cfbcecbc4be0c45d409f6b562

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.526757Z digest=sha256:9815b1602dcca4b60ff8e08236c1fe06463d92f1a889cb58998881706dfb4453

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.531884Z digest=sha256:184b818c3a0f904a2e8e5f24f9e8bb4ad77f7d9351ee15eef00e680d31a2d21b

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=sha256:4b3c5b5b627396fb82cd6dfcfa9d69b0f3a81444c637f56026dfb798d2a45cc1

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.543433Z digest=sha256:10582d8eecc36b98ff363c3de515482339ce1d2ecc02eaab824211e503526212

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.548359Z digest=sha256:e8583f88d55622de1371d1ebfefe20d52a6d432b178d3671213c0a351c080533

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.554229Z digest=sha256:f1dbe5107f5eaada6a71f056305370fc5aea4a5e8cf0380b33ee411b29ddbf12

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.559068Z digest=sha256:0aee02a9b630fa27e9457871c3e72c172e2574aadf7cf81cda164b031f9b631d

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.563832Z digest=sha256:e89b879266936ec525e244ac7806f528b920ec66a3d105365d030c0b71419af4

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.568087Z digest=sha256:af2ce0237f0a6a09751230705d1a0706dae11ba6cd8cf2f4c102367355b1dc9b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.572537Z digest=sha256:753b99b27e9b0f5c2c534f88008ff0bfbfcda6226f12933d88a9bf95cffbcf2a

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.577045Z digest=sha256:793ffe8850ef2470c9985db1ae29544029e5ca2c4f8361034faf86fd6ea334ff

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.581521Z digest=sha256:5e56a973b43137f27a5eecb83f8d6704e99b33b813fb9c63815fae5a368dd5cf

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.586258Z digest=sha256:c9a3256881b6ab112a1d577d2cf98d7d421d86d3fb2cc66879d61cfc06257261

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.591978Z digest=sha256:1b66816491f7abc8e9046c658db324e986901f6b6c81200b5ca2112835e1713f

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.598043Z digest=sha256:167bd86f4e95a2de54a4f9fe6a5d28a2a51fb91f8e5477fa32b301af16ba7d22

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.605513Z digest=sha256:49c0630f8b163289529821ac7ee4efe9f3386c44b1f737d84518a099f6bf2d3a

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.612102Z digest=sha256:3125ed9f07bec9f713e55f4e4dcb4ec1354ea708e043d4f97dad81a3226600a7

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=sha256:5195004a001ca24a361be85d58e59c32dc61da1078da539b73b136e367bef952

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.624324Z digest=sha256:ad56698bbe4fc0d1e6869c8ce5439479c2515de8cb48b98a91225528dec37c7c

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.630248Z digest=sha256:80a5b6fc8f812e0e29cf492fde9de28179f5f7a386ecffef8a61a31eaa0f23cf

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.634976Z digest=sha256:6ffc1b0c60c645eef93ac9f2ba278dd47666d4a1b399b45b6075704530f70ee9

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.639536Z digest=sha256:5d3b569a17e1818613f7629fef22dc124cf0d022c63a473baff148719ebee9a7

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.644698Z digest=sha256:6651af4e02a19aa7916f9d5e9546eeae2fbd0ae8b089509214e65e2d4eecae1d

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T18:13:19.650039Z digest=sha256:f89263e0b30a5a0837eb50df78285c3bcaf8d91b7e13acc10d550a53d01768bf

Pith citing papers

No inbound Pith citation observations are available.