Tootfinder

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
Ziyan Liu, Chunxiao Fan, Haoran Lou, Yuexin Wu, Kaiwei Deng
https://arxiv.org/abs/2507.06908

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
Athena Vakali, Ilias Dimitriadis
https://arxiv.org/abs/2506.09107 https:…

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines
AI models have become active decision makers, often acting without human supervision. The rapid advancement of AI technology has already caused harmful incidents that have hurt individuals and societies and AI unfairness in heavily criticized. It is urgent to disrupt AI pipelines which largely neglect human principles and focus on computational biases exploration at the data (pre), model(in), and deployment (post) processing stages. We claim that by exploiting the advances of agents technology,…

This https://arxiv.org/abs/2409.17458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking
The rapid progress of Large Language Models (LLMs) has opened up new opportunities across various domains and applications; yet it also presents challenges related to potential misuse. To mitigate such risks, red teaming has been employed as a proactive security measure to probe language models for harmful outputs via jailbreak attacks. However, current jailbreak attack approaches are single-turn with explicit malicious queries that do not fully capture the complexity of real-world interactions…

This https://arxiv.org/abs/2411.18289 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy
Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks, such as poisonings, fires, and even explosions. In this paper, we present responsible robotic manipulation, which requires robots to consider potential hazards in the real-world environment while completing instructions and performing complex operations safely and efficiently. However, such scenarios in real world are variable and risky for training. To address this challenge, we propose Safety-a…