Zhong-Qi Wang

$\phi$-mesic nuclei, previously unobserved, are predicted to form in high-energy $pp$ collisions, offering a new frontier for nuclear physics exploration.

Static bias evaluations miss the mark; DeepBias reveals hidden vulnerabilities in LVLMs through an adaptive probing process that evolves with the model's responses.

VecFormer slashes the computational cost of graph transformers while boosting out-of-distribution generalization by operating attention on quantized "graph tokens" instead of individual nodes.