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LLMs can revolutionize hardware security by autonomously identifying vulnerabilities in Verilog designs before they become embedded in silicon.
Experience-rich memory boosts agent performance in office workflows but can also lead to misleading recall, challenging traditional evaluation methods.
Achieving 93.02% accuracy in molecular structure recognition, MinerU.Chem outperforms existing systems, unlocking new possibilities for AI-driven chemistry research.
Misinterpretations in RTL design generation can be fixed before code is even written, leading to a groundbreaking 94% functional correctness in generated designs.
Predictive collision avoidance in robotic manipulation is now achievable, with a 19.8% boost in task success over conventional methods.
Finance LLM agents can now block unauthorized actions mid-trajectory without sacrificing performance, thanks to a novel inline safety harness that adaptively routes verification between lightweight and advanced LLM judges.
Today's best AI agents can only solve 55% of real-world academic tasks that university students find challenging, revealing a significant gap between current AI capabilities and the demands of academic workflows.
