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    <abstract>We study how Vision Transformers ground abstract concepts (e.g., angry) when training data provide limited direct referential evidence. We hypothesize a metonymic grounding mechanism in which abstract predictions are driven by concrete, interpretable anchor concepts (e.g., fire) that bridge visual signals to abstract semantics. By applying Transcoders on CLIP and DINO vision encoders, we recover intermediate features that can be associated with semantic labels for more concrete concepts, and trace their contributions in circuits underlying abstract concept recognition. Experiments on a carefully curated icon dataset reveal structured metonymic circuits, in which perceptual primitives dominate early layers and object-like anchors precede abstract targets. Images containing rendered text instead recruit a distinct perceptual-to-textual route. Causal interventions further validate that metonymic intermediates are functionally involved in grounding abstract concepts.</abstract>
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%0 Conference Proceedings
%T Metonymic Circuits for Abstract Concept Grounding in Vision Transformers
%A Ding, Jing
%A Ma, Ziqiao
%A Mao, Jiayuan
%A Chai, Joyce
%A Shi, Freda
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ding-etal-2026-metonymic
%X We study how Vision Transformers ground abstract concepts (e.g., angry) when training data provide limited direct referential evidence. We hypothesize a metonymic grounding mechanism in which abstract predictions are driven by concrete, interpretable anchor concepts (e.g., fire) that bridge visual signals to abstract semantics. By applying Transcoders on CLIP and DINO vision encoders, we recover intermediate features that can be associated with semantic labels for more concrete concepts, and trace their contributions in circuits underlying abstract concept recognition. Experiments on a carefully curated icon dataset reveal structured metonymic circuits, in which perceptual primitives dominate early layers and object-like anchors precede abstract targets. Images containing rendered text instead recruit a distinct perceptual-to-textual route. Causal interventions further validate that metonymic intermediates are functionally involved in grounding abstract concepts.
%U https://aclanthology.org/2026.emnlp-main.1663/
%P 37802-37842

Markdown (Informal)

[Metonymic Circuits for Abstract Concept Grounding in Vision Transformers](https://aclanthology.org/2026.emnlp-main.1663/) (Ding et al., EMNLP 2026)

• Metonymic Circuits for Abstract Concept Grounding in Vision Transformers (Ding et al., EMNLP 2026)

ACL

• Jing Ding, Ziqiao Ma, Jiayuan Mao, Joyce Chai, and Freda Shi. 2026. Metonymic Circuits for Abstract Concept Grounding in Vision Transformers. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 37802–37842, Budapest, Hungary. Association for Computational Linguistics.