Scalable detection and causal evaluation of grounding aggregation heads in full-scale VLMs
Develop computationally viable methods that automatically detect aggregation heads across diverse vision–language models and enable scalable causal interventions to validate their role in symbol grounding, thereby addressing the current inability to systematically identify and test these mechanisms at scale.
References
For these reasons, while our case study highlights promising evidence of grounding heads in modern VLMs, systematic detection and causal evaluation of such heads at scale remains an open challenge. Future work will need to develop computationally viable methods for (i) automatically detecting aggregation heads across diverse VLMs, and (ii) applying causal interventions to validate their role in grounding.
— The Mechanistic Emergence of Symbol Grounding in Language Models
(Wu et al., 15 Oct 2025) in Section 6 (Discussions), Generalization to full-scale VLMs