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All tested GUI agents are alarmingly vulnerable to environmental injection attacks, with success rates reaching over 66%, revealing a pressing need for improved safety measures.
StrataCL achieves up to 1.9x faster LLM inference and significantly reduces training times by streamlining communication in distributed AI systems.
A single misleading document can increase the false-conclusion adoption rate of Deep Research agents to over 54%, revealing a critical vulnerability in AI-driven research processes.
Frontier LLMs can be induced to generate biologically hazardous sequences, with attack success rates reaching up to 100%.
Achieving superior safety alignment with LLMs using only 100 harmful samples, SafeSteer drastically cuts alignment costs while maintaining model performance.
AgentSchool offers a powerful new way to simulate educational environments, moving beyond simple role-play to model learning as a dynamic state transition and providing a testbed for long-horizon memory and multi-agent coordination.
Self-evolving agents can now learn more efficiently in resource-constrained environments by explicitly structuring experience into a tool graph memory that facilitates planning and tool reuse.
