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Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversaries increasingly use mosaic attacks: multi-turn sequences whose individual fragments are innocuous in isolation yet assemble into a harmful payload. We develop a theor...
Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI techniques are increasingly applied to safety-critical scenarios, it is crucial to understand whether VLMs possess robust spatial memory and make reliable decisions. In...
The Fetch.ai architecture is introduced, an industrial-strength platform designed to bridge the gap by facilitating the integration of classical MAS principles with modern AI capabilities, and provides a principled architecture for moving beyond current agent implementations towards open, collaborative, and economicall...
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