A new study published on arXiv investigates the abstract reasoning capabilities of Vision Language Models (VLMs). Researchers found that while VLMs perform well on visual tasks, they struggle with abstract reasoning, a deficiency they traced using a psychological paradigm called Relational Match-to-Sample (RMTS). By analyzing frontier models like GPT, Claude, and Gemini, alongside open-source models such as Qwen 3.5 and Gemma 4, the study identified key factors influencing relational reasoning, including model scale and object complexity. Further mechanistic analysis revealed two competing internal circuits in VLMs: one focused on object features and another on abstract relations, with the latter being crucial for abstract reasoning tasks. AI
IMPACT Identifies specific limitations in VLM abstract reasoning and proposes a mechanistic understanding, potentially guiding future model development.
RANK_REASON Academic paper published on arXiv detailing research findings on VLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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