Make agents talk, think, and team up better. 🤖🤝🗣️🎮 This work enhances a framework for Collaborative Embodied Agents powered by LLMs. Through prompt-engineering studies and model selection, the best combo improved efficiency by 22% (with Gemma3) versus the original system. They also add speech capabilities for smoother voice-based collaboration in shared
“No real victim” is a dangerous myth. 🚫🧒⚠️🔒 This paper reviews how AI-generated child sexual abuse material (AI CSAM) can still cause harm: creating synthetic depictions, revictimizing known survivors, facilitating grooming and extortion, normalizing exploitation, and lowering barriers that may lead some users toward offending. Like a slippery slope disguised as
10,497 examples, 13 tasks: a holistic yardstick for voice-first multimodal assistants. Voice assistants are rapidly evolving into multimodal agents that must hear, speak, and see. Yet evaluation has lagged behind capability. VoiceAssistant-Eval fills this gap with a comprehensive benchmark of 10,497 curated examples across 13 task categories, spanning
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