Causal Probing for Internal Visual Representations in Multimodal Large Language Models
This work proposes a causal framework based on activation steering to actively probe and manipulate internal visual representations, revealing a divergence in concept encoding: entity knowledge is distinctively localized, whereas abstract concepts are globally distributed across the network.