[Submitted on 3 Oct 2026]
Title:Metonymic Circuits for Abstract Concept Grounding in Vision Transformers
View a PDF of the paper titled Metonymic Circuits for Abstract Concept Grounding in Vision Transformers, by Jing Ding and 4 other authors
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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.
Comments: EMNLP 2026 Main. Project Website: this https URL
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.06928 [cs.AI]
(or arXiv:2610.06928v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.06928
arXiv-issued DOI via DataCite
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From: Jing Ding [view email] [v1] Sat, 3 Oct 2026 00:53:32 UTC (3,894 KB)
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