CARV: A Diagnostic Benchmark for Compositional Analogical Reasoning in Multimodal LLMs

Abstract:Analogical reasoning tests a fundamental aspect of human cognition: mapping the relation from one pair of objects to another. Existing evaluations of this ability in multimodal large language models (MLLMs) overlook the ability to compose rules from multiple sources, a critical component of higher-order intelligence. To close this gap, we introduce CARV (Compositional Analogical Reasoning in Vision), a novel task together with a 5,500-sample dataset as the first diagnostic benchmark. We extend the analogy from a single pair to multiple pairs, which requires MLLMs to extract symbolic rules from each pair and compose new transformations. Evaluation on the state-of-the-art MLLMs reveals a striking performance gap: even Gemini-2.5 Pro achieving only 40.4% accuracy, far below human-level performance of 100%. Diagnostic analysis shows two consistent failure modes: (1) decomposing visual changes into symbolic rules, and (2) maintaining robustness under diverse or complex settings, highlighting the limitations of current MLLMs on this task.

Artificial Intelligence (cs.AI)

arXiv:2603.27958 [cs.AI]

(or arXiv:2603.27958v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.27958 Focus to learn more arXiv-issued DOI via DataCite

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