Jia-Qi Zhang

IndustrialVLA-Bench is presented, an evidence-aware evaluation of six released VLA and WAM systems under a unified reporting schema that evaluates clean capability on LIBERO, non-language robustness on LIBERO-Plus, instruction sensitivity on LIBERO-Para, and observed execution cost.

EviGDA is proposed, an Evidence-Augmented Graph Domain Adaptation framework that complements graph representation adaptation with a graph-free local expert, preserving complementary evidence without joint training, learned routing, or target pseudo-labels.

UMI-Bridge, which uses UMI as an intermediate domain to align representations according to action equivalence rather than pixel similarity, supports action-anchored latent alignment for data-efficient robot learning and UMI-to-robot task transfer.

ReBridge-Flow resolves critical bridge mismatches in flow matching, leading to significantly improved structural consistency in image restoration.

PriMD revolutionizes multimodal emotion recognition by enabling robust performance even when critical data modalities are missing.