Chenxu Wang

LLM agents can be made robust to complex, multi-turn adversarial attacks on tool use through a co-evolutionary game, outperforming prior methods on safety and capability benchmarks.

Open-set GNNs silently break when graphs violate homophily, but synthesizing pseudo-unknown proxies along cross-class displacement vectors restores robust out-of-distribution detection on heterophilic topologies.

Attack strategies can now evolve and adapt in real-time, leading to a staggering 48.6-point improvement against state-of-the-art models.

Prior knowledge about signal representations can significantly boost the performance of automatic modulation classification in shifting communication environments.