Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning
Vision--language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bottleneck lies in reasoning or representation. We study this on Bongard-LOGO, a synthetic benchmark of abstract concept learning with ground-truth generative programs, by comparing end-to-end VLMs on raw images with large language models (LLMs) given symbolic inputs derived from those images. Using symbolic inputs as a diagnostic probe rather than a practical multimodal architecture, our \emph{Componential--Grammatical (C--G)} paradigm reformulates Bongard-LOGO as a symbolic reasoning task based on LOGO-style action programs or structured de...
| Authors | Mohit Vaishnav, Tanel Tammet |
|---|---|
| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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