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Attack strategies can now evolve and adapt in real-time, leading to a staggering 48.6-point improvement against state-of-the-art models.
Verified data synthesis can dramatically elevate the skill-use performance of language models, producing thousands of executable trajectories that enhance agent capabilities.
Achieving trillion-parameter performance with just 35 billion parameters by scaling agent horizons reveals a new frontier in model efficiency.
LLM-based agents can now autonomously enhance their own harnesses, leading to performance boosts of up to 18% without human intervention.
Mismatched levels of "mind-reading" between AI agents tank their ability to collaborate, but a simple adaptive strategy can fix it.
Structured composition unlocks significantly better agent performance compared to flat skill invocation, even with the same skill set.
