Google DeepMind, along with Schmidt Sciences and other partners, has announced a $10 million funding initiative to support research in multi-agent AI safety. This effort aims to address the challenges posed by the interaction of numerous AI agents across digital environments. The funding will support global researchers in developing frameworks to understand and mitigate the risks associated with these interactions. The initiative underscores the importance of establishing safety standards as AI systems become more interconnected and complex.
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Google DeepMind Launches $10M AI Safety Research Fund
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© Cole MedinAnthropic’s latest research exposes a quiet but dangerous flaw in agentic workflows: experts follow AI recommendations 86% of the time, often adopting the agent's reasoning as their own. This isn't just about accuracy; it's about cognitive capture, where human oversight dissolves into passive compliance. The study shows that even when users believe they are steering, the agent’s framing dictates the outcome. Practitioners must now actively counter this bias by forcing agents to justify deviations or prototype alternatives before committing to a path. This shift demands a new discipline in how we interact with coding assistants. We can no longer assume our intent drives the result; the model's suggestion often becomes the default truth. The real work is ensuring we remain the architects, not just the approvers.
© WIRED AICisco Talos has uncovered a new infostealer strain that actively uses commercial LLMs to profile victims and prioritize high-value targets. Dubbed Volta-Autodoxer, the malware collects credentials and browsing data, then feeds this into models like Claude Opus 4.6 via OpenRouter to generate structured intelligence dossiers. This marks a shift from passive data theft to active, AI-driven triage, allowing attackers to bypass guardrails with fallback models like Kimi K2 or Gemini. The capability foreshadows a future where lower-sophistication criminals can execute sophisticated, targeted attacks at scale.
© Matt WolfeOpenAI researchers published findings disproving the Exponential Time Hypothesis, a major result in computational complexity theory.