From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models | Paper Citation Record

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This paper cites https://huggingface.co/sentence-transformers/ all-mpnet-base-v2 , 2021.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models https://huggingface.co/sentence-transformers/ all-mpnet-base-v2 , 2021

Reference 1

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This paper cites Uncovering unknown unknowns in machine learning.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Uncovering unknown unknowns in machine learning

Reference 2

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This paper cites Dices dataset: Diversity in conversational ai evaluation for safety.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Dices dataset: Diversity in conversational ai evaluation for safety

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

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models A General Language Assistant as a Laboratory for Alignment

Reference 4

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models unknown unknowns

Reference 5

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This paper cites Constitutional AI: Harmlessness from AI Feedback.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Constitutional AI: Harmlessness from AI Feedback

Reference 6

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This paper cites Inspecting the geographical representativeness of images from text-to-image models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Inspecting the geographical representativeness of images from text-to-image models

Reference 7

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This paper cites Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning

Reference 8

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This paper cites Easily accessible text-to-image generation amplifies demographic stereotypes at large scale.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Easily accessible text-to-image generation amplifies demographic stereotypes at large scale

Reference 9

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This paper cites Typology of risks of generative text-to-image models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Typology of risks of generative text-to-image models

Reference 10

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This paper cites AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

Reference 11

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This paper cites Ai red teaming through the lens of measurement theory.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Ai red teaming through the lens of measurement theory

Reference 12

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This paper cites Understanding practices, challenges, and opportunities for user-engaged algorithm auditing in industry practice.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Understanding practices, challenges, and opportunities for user-engaged algorithm auditing in industry practice

Reference 13

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This paper cites Toward user- driven algorithm auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Toward user- driven algorithm auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior

Reference 14

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This paper cites Build it break it fix it for dialogue safety: Robustness from adversarial human attack.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Build it break it fix it for dialogue safety: Robustness from adversarial human attack

Reference 15

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This paper cites Prompt templates: A methodology for improving manual red teaming performance.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Prompt templates: A methodology for improving manual red teaming performance

Reference 16

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This paper cites The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels

Reference 17

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This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 18

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This paper cites Harm Amplification in Text-to-Image Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Harm Amplification in Text-to-Image Models

Reference 19

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 20

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This paper cites Intersectionality in AI safety: Using multilevel models to understand diverse perceptions of safety in conversational AI.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Intersectionality in AI safety: Using multilevel models to understand diverse perceptions of safety in conversational AI

Reference 21

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This paper cites Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate

Reference 22

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This paper cites The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models

Reference 23

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This paper cites The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

Reference 24

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This paper cites BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models

Reference 25

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This paper cites Learning diverse attacks on large language models for robust red-teaming and safety tuning.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Learning diverse attacks on large language models for robust red-teaming and safety tuning

Reference 26

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Stable diffusion safety checker model card

Reference 27

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This paper cites Stable Bias: Analyzing Societal Representations in Diffusion Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Stable Bias: Analyzing Societal Representations in Diffusion Models

Reference 28

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Dynaboard: An evaluation-as-a-service platform for holistic next-generation benchmarking

Reference 29

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models vladmandic/nudenet - neural network for nudity detection, 2024

Reference 30

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Midjourney documentation and user guide

Reference 31

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Social biases through the text-to-image generation lens

Reference 32

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Adversarial NLI: A new benchmark for natural language understanding

Reference 33

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models DALL-E 2 system card,

Reference 34

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models DALL-E 3 system card,

Reference 35

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Is a picture of a bird a bird: A mixed-methods approach to understanding diverse human perspectives and ambiguity in machine vision models

Reference 36

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Red teaming language models with language models

Reference 37

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Cultural Incongruencies in Artificial Intelligence

Reference 38

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Adversarial Nibbler: An open red-teaming method for identifying diverse harms in text-to-image generation

Reference 39

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 40

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Zero-shot text-to-image generation

Reference 41

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Hierarchical text-conditional image generation with CLIP latents, 2022

Reference 42

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Supporting human-ai collaboration in auditing llms with llms

Reference 43

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This paper cites Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI

Reference 44

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Q16: Safety benchmarks for language models, 2024

Reference 45

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models High-resolution image synthesis with latent diffusion models

Reference 46

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Two contrasting data annotation paradigms for subjective NLP tasks

Reference 47

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Re- imagining algorithmic fairness in india and beyond

Reference 48

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This paper cites Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models

Reference 49

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This paper cites Ignore this title and hackaprompt: Exposing systemic vulnerabilities of llms through a global prompt hacking competition.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Ignore this title and hackaprompt: Exposing systemic vulnerabilities of llms through a global prompt hacking competition

Reference 50

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This paper cites Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation

Reference 51

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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models A mathematical theory of communication

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:27.770039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e4b4250-0426-41f8-8808-a706fd58b70e · outbound

This paper cites Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.557550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2600446-b9a7-4709-8149-88659cea577d · outbound

This paper cites The psychosocial impacts of generative ai harms.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models The psychosocial impacts of generative ai harms

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:27.629956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9ab11608-2161-4274-b54b-48ef07133ebb · outbound

This paper cites Learning from the worst: Dynamically generated datasets to improve online hate detection.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Learning from the worst: Dynamically generated datasets to improve online hate detection

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:27.475905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 945abdb6-f726-4778-be18-a602c55ed7d2 · outbound

This paper cites Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:45:20.809523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d1df1e90-7729-4487-9562-9f28d2f234f6 · outbound

This paper cites MMA-Diffusion: MultiModal Attack on Diffusion Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models MMA-Diffusion: MultiModal Attack on Diffusion Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.570531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f4ce4b1-eec0-4b31-a529-0b72a386439d · outbound

This paper cites How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.573789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63a3c08e-366c-4dd4-baf5-143c5113af96 · outbound

This paper cites Auditing Gender Presentation Differences in Text-to-Image Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Auditing Gender Presentation Differences in Text-to-Image Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.577098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a75fb78-2c5e-4f42-b791-74c90cafc89f · outbound

This paper cites DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:45:20.765871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 67231121-8a7e-4839-b5ad-4050d547ecd5 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:20.584079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef0bcc52-7985-4ccf-b807-0fa8d0490013 · outbound

This paper cites The prompts should be creative and novel but should utilize the style of attack.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models The prompts should be creative and novel but should utilize the style of attack

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:26.978993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ef1147e6-9b4e-43c0-be77-9bc1d3c03b62 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:26.786563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7f806ff-9193-42d6-8ed1-a40469feba42 · outbound

This paper cites For every word or phrase that you discover, replace it with a cryptic or obscure phrase or emoji.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models For every word or phrase that you discover, replace it with a cryptic or obscure phrase or emoji

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:26.604152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cdcc7160-6434-4a4c-a11e-68afe59fcc70 · outbound

This paper cites If there is more than one demographic indicator, substitute all of them and ensure that they result in meaningful combinations.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models If there is more than one demographic indicator, substitute all of them and ensure that they result in meaningful combinations

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:26.431191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c7ac5ec0-f09c-4387-9a6f-27f68c21b60d · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:26.269903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8ab082a0-849c-455e-9bec-e782e04fb651 · outbound

This paper cites You can also replace with islands such as ’Hawaii’ and ’Sicily’, territories such as ’Puerto Rico’ and ’Guam’, or autonomous regions such as ’Hong Kong’ and ’Macau’.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models You can also replace with islands such as ’Hawaii’ and ’Sicily’, territories such as ’Puerto Rico’ and ’Guam’, or autonomous regions such as ’Hong Kong’ and ’Macau’

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:26.052282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5bcda7fc-2a6e-46ca-a2c8-5b8dcd9d22a8 · outbound

This paper cites Use words like ’not,’ ’never,’ or ’none’ to convey a negative meaning, 15 and ensure that the flipped sentence still conveys the same tone and intent as the original seed prompt.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Use words like ’not,’ ’never,’ or ’none’ to convey a negative meaning, 15 and ensure that the flipped sentence still conveys the same tone and intent as the original seed prompt

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:25.829705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b757e4cc-40f8-435a-8485-6240c0f1f159 · outbound

This paper cites Ensure that the shortened prompt still conveys the same meaning and key elements as the original seed prompt, and that the tone and intent are maintained.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Ensure that the shortened prompt still conveys the same meaning and key elements as the original seed prompt, and that the tone and intent are maintained

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:25.642229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0a197f31-a8f7-428d-9a25-8a0f00b5e91e · outbound

This paper cites When substituting words, consider the shape, size, and color of the words, as well as their semantic meaning.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models When substituting words, consider the shape, size, and color of the words, as well as their semantic meaning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:25.485482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 183905c1-863c-4a46-9ff9-575a0e756c5b · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:27.281278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 219c09ff-5bf0-4409-bc93-3805e2bb5d41 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:25.265054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5a167e35-8e32-4df1-8329-6b1340279b88 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:25.101096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6b287b6d-57b5-408e-9b68-8a872993c9d0 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:27.129566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2b62397a-3e45-47e8-a330-44c6f5f34e11 · outbound

This paper cites The prompts should be creative and novel but should utilize the style of attack.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models The prompts should be creative and novel but should utilize the style of attack

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:24.909426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55ba774b-ddb1-4d75-b7ac-0de3e732d479 · outbound

This paper cites A pregnant woman in Hawaii about to give birth, assisted by a male obstetrician from Australia.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models A pregnant woman in Hawaii about to give birth, assisted by a male obstetrician from Australia

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:24.698890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 41537c77-2f70-4d2b-8e42-f27ac0dc82bc · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:24.511446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 98e47a19-0c8c-4269-ace6-bb5802ace3c4 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:24.286437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb7e9a08-a371-4e1b-b2be-074178376709 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:24.085849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 068cd7a1-e720-4b09-ae43-97647ec7060a · outbound

This paper cites These links are in footnotes in Section 1.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models These links are in footnotes in Section 1

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:23.843372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d0a3b65-645a-4f6b-a538-96c60cbb5ca3 · outbound

This paper cites These links are in footnotes in Section 1.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models These links are in footnotes in Section 1

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:23.668761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fe25d45c-a4f9-4668-92d0-14a9bf0d254f · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:23.437699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9542bdd5-271b-4872-9488-29ec7c1b0c15 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:23.227360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4154944-3ed8-431d-ba6c-6900d05841bf · outbound

This paper cites 26 Guidelines:.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models 26 Guidelines:

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:23.012682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e3eced7-ca56-4f01-a4f3-fb46b6bac74f · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:22.778840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 143ff867-cf20-4b8c-a0ab-b4b750760ad7 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:22.598724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c56701b3-639b-4511-bfcf-d39298a72c21 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:22.386629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 937fada1-68ca-4040-8855-cb4021a53964 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:22.204755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5321485d-baae-4892-9f5e-fdaa36b1bce1 · outbound

This paper cites These links are in footnotes in Section 1.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models These links are in footnotes in Section 1

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:45:21.999363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18908292-178c-4279-9694-6c71ec917ad1 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:21.824937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76a3003b-70e2-43bd-b7b2-d309e7dd2bda · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:21.610299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d6cf3a9-92f3-42a8-afee-dc0bbb4912d5 · outbound

This paper cites an unresolved cited work.

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:45:21.412005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers