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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 10 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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.373132Z digest=sha256:bd3251586c66c79d9f5631c76239a11cf361b18d42090f43ea0c899853aa9ab6

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.378929Z digest=sha256:fc17d3ebf968d52ed1f6779c39ba1a4d43cb30ad8c4836fdc49dbf2f26dbd76d

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=sha256:7c9b15c0d4fde26c2ff1406088183dde1ea7586438bfb8705cfe92242de190a5

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.389552Z digest=sha256:aedc22776b4ce5ce30981af4fa50306c664e07e828d59a7f6e07cfc272ada896

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.395036Z digest=sha256:69d24a50e3cfec2c2abf8bbf8a96700131a4c12bcb14d5cb81013039c25c11b6

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=sha256:2c5c21c82b6e7606fcbe9502580cb9b9843e855ac8cf6cca2b9980589a73af25

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.408183Z digest=sha256:d2518740211058e6fef503d6fa41171a574e2353c72b0a1d24fd357a21ac8390

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.412877Z digest=sha256:bb4478a321f3a7cdac45fdd3807b08d32f37568221c63c535e7568e38d144e7d

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.419780Z digest=sha256:46440302db79b1f6c602074eee10f3c2d3d4e82e18f7376ad24bd1be40dc1b4e

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.426092Z digest=sha256:fa79f1db2c0230e80002af88a56184184376ff4e2e968e82e7f36e81c5331e1d

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.431418Z digest=sha256:48840c0146964985c7c78eddc07a580016de4c3be7f62c26aa3256618ff8e5fc

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.435807Z digest=sha256:cf32cfb706cd87149b1056c8ffa3bee1cd1bfc75fe67d9567bdd7f181f9c6cc6

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.440898Z digest=sha256:244c035341471c26719259b5c303836ba02ba63d73b1e0ccf072c498972e5bb5

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.446041Z digest=sha256:23c757190e27059045236103a992c4761bf1ea9744fd0edd644da1735ab80e22

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.451607Z digest=sha256:f44358eae3e93e604f647e19a5c123fb8c6ec27f29365f4519eca3d1e5947a4c

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.456528Z digest=sha256:c8c9ea2c717365fffcdde11819a96e865cbe747bc9f9dca6e5db4d47b01040f6

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.461109Z digest=sha256:c9facca7bf97f9f5953bee2074f17ad1e06fab390af472818315dffa9c251a71

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.465619Z digest=sha256:75302da9a1f2a7459dbaa4a7fef268b42cbd52f9dbf86f7ae8e57ebeca8bb0ce

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.471574Z digest=sha256:617bbbc453f90299f27eb4e780097479ce4c1b462f97801482be7ba4ebebd66f

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:eaba0beef1755f19955711c55628414e2138510aae632d2798f8e4c933a5c580

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.483022Z digest=sha256:38ce759e77d5c9589a7a76e5f0f591428508e4179346caaad47aa079c7c2a4e7

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.488345Z digest=sha256:8dd60429c4b7aa6c0e9d5afe0d5ac565fb45697fb79eee6b1cbe0a990eb1e651

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.492657Z digest=sha256:8cb8dfbbe3a4465d968fe683984b64ddb853057c309f6becb9d7e04583bf2482

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.497038Z digest=sha256:42421540b5c1e9faa622067f8114cd19212ba0f7a5dded5c34c10080db776e58

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.501380Z digest=sha256:fef1d780154fd25d5182e4f5fb0c37af28b034ef9361e6c64b01a0df38909588

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:bd082d67f5185fe91ebb9c694767c11dd38cfbfb4b970995f9a79acee986a016

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.510936Z digest=sha256:675ddc7cb4db68d72e14f4fce109824df2dca4cc81ac0d7af5e10cf49325bac1

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.515688Z digest=sha256:ba47deef5d2517b85fe98817cededae332deb749977b2351640eb4c2c08c2f01

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.521670Z digest=sha256:e032e7ea8403be7b1cc2708c568590149e9703770f65ab9daddcd5abaf4fd273

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.526757Z digest=sha256:5e65419a22b19216ce8f9885f4962c1e58df68523930f5b4070d659f5dac78d3

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.531884Z digest=sha256:d707d7f5f5ee26496b490b15e9b36c1522b3dea7ddcf23c7de6e02eb20023b74

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:f76a2ebb0d687702ef9047c60e1e1b07470c09c399cefb2f30060d1ec201376e

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.543433Z digest=sha256:e27bb8726f60edf1ad5bdb5528cafd3527c2218dfd3e6fcae91d5defcbefdcf3

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.548359Z digest=sha256:34f3d23f43df267e168d351a96c9b1b142f6989cce23823bb2e08876b25157c1

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.554229Z digest=sha256:9c9267530e60b48538d33f9ead5a60824dea65bb68dc34ffc89bbf9af906c7d4

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.559068Z digest=sha256:e4f1e3c6fee308c6069ab8778b700a05febfbcbda0a9af0463b9edfe229f044c

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.563832Z digest=sha256:857a414300f74c80459edc5208ea8dc1c6d10ef118e9bf35189d3a3a735f3ec8

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.568087Z digest=sha256:0a234e8f7613918d07e65efe69605abde9d98d23a7d2b0e1a09e1a88ef3375f4

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.572537Z digest=sha256:64c0ae8c650ac0fdb1efe8c563813b208b9bd726c30027ec9eb76e4330dc46b1

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.577045Z digest=sha256:0f043e4c9178d361296e345c6a9d32beb72b8d73408f900c73b630920ae318b4

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.581521Z digest=sha256:d00a3863ca0bc3dbd232ae18b7cb39f929d0e69f1d013e681e1245e733763a9f

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.586258Z digest=sha256:d9698ffcf04569b8aabcf81d86045ad46ff8a5e97463d25d074267ac98b89485

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.591978Z digest=sha256:dbba2fa0aeb14bd1c23c7aeb009a3190174d94962141d7bb5bfbea9c1e1f4b99

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.598043Z digest=sha256:f76adb65f7c314608bd25f7331cde87df33f259c9a2beff48138709558d1eb2b

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.605513Z digest=sha256:46af5b140058e1c07ffd61c3597f2eb98e2cb26cd1248be5bbc70158531234aa

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.612102Z digest=sha256:84a5dc537400f251007e76da9a5be0e7e85cb1775afb08855ca46ec99c6b64a9

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:fa9598b41df1020b5b50ce9438ad9f98d9a5bdb320820aa4f7a98e1e5fe888f9

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.624324Z digest=sha256:62ded9ebf6dc5314fa43157799f3b1e11b2a5702c67e4e2c8b3b3d39cebf2b61

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.630248Z digest=sha256:43927554ff659ccc539ec6d59f6b95797d65863b43b1f7522509b8d4839aaa9a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.634976Z digest=sha256:a40fcfa47422261e1b43b4d4d08f9f3da7345928b9647622767f9e306c3c6195

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.639536Z digest=sha256:492af63fccfd43136d71f9bd7732857a5fe17ff21c0022741449cf800a0ce43d

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.644698Z digest=sha256:702ffb4315777728f8a414d4fa95b3531f42945e7011c0e9e6e85df691413647

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-08T18:13:19.650039Z digest=sha256:ac8f9b93bf4f0a241078f6f1438b8cdcbf7309c6bc3da27233c1e0a20bd8434d

Pith citing papers

No inbound Pith citation observations are available.