Vision-language-conditioned robot policies integrate perception, language understanding, and control for general-purpose manipulation. However, existing evaluations often focus on task success, isolated physical constraints, semantic refusal, or realized physical damage, providing limited insight into where safety fail...
Jin-Zhu Luo, Qi Zhang, Wei Wang et al.· 0 citations
Reducing the representation within retained tokens provides an effective complement to token pruning for aggressive VLA compression, and can be applied to language values, allowing visual and language representations to be compressed without removing additional tokens.
COEC (Calibrated Orthogonal-Equivalence Compensation), a training-free compensation framework that applies alternating left and right orthogonal rotations to the retained weight, improves perplexity on every model and zero-shot accuracy in most settings over existing compensation methods.
Peiqi Yu, Nam Ling, Wei Wang et al.· 1 citation· ⚡1
Merging finetuned models combines specialized capabilities without joint training or access to the original data. Most methods operate by linear arithmetic in Euclidean weight space, which cannot carry the geometry of the update. Orthogonal Model Merging (OrthoMerge) uses a single orthogonal transform for each weight m...
Xinyi Sui, Zi-Ran Liu, Nam Ling et al.· 1 citation
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