Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems

• URL: http://arxiv.org/abs/2508.12026v1
• Date: Sat, 16 Aug 2025 12:26:44 GMT
• Title: Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems
• Authors: Szymon Pawlonka, Mikołaj Małkiński, Jacek Mańdziuk,
• Abstract summary: Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR)<n>We introduce Bongard-RWR+, a dataset composed of $5,400$ instances that represent original BP abstract concepts using real-world-like images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts fromjust a few examples and describe them in natural language. Early BP benchmarks featured synthetic black-and-white drawings, which might not fully capture the complexity of real-world scenes. Subsequent BP datasets employed real-world images, albeit the represented concepts are identifiable from high-level image features, reducing the task complexity. Differently, the recently released Bongard-RWR dataset aimed at representing abstract concepts formulated in the original BPs using fine-grained real-world images. Its manual construction, however, limited the dataset size to just $60$ instances, constraining evaluation robustness. In this work, we introduce Bongard-RWR+, a BP dataset composed of $5\,400$ instances that represent original BP abstract concepts using real-world-like images generated via a vision language model (VLM) pipeline. Building on Bongard-RWR, we employ Pixtral-12B to describe manually curated images and generate new descriptions aligned with the underlying concepts, use Flux.1-dev to synthesize images from these descriptions, and manually verify that the generated images faithfully reflect the intended concepts. We evaluate state-of-the-art VLMs across diverse BP formulations, including binary and multiclass classification, as well as textual answer generation. Our findings reveal that while VLMs can recognize coarse-grained visual concepts, they consistently struggle with discerning fine-grained concepts, highlighting limitations in their reasoning capabilities.

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