Symbolic and Abstractive Reasoning with Complex Visual Queries

• URL: http://arxiv.org/abs/2606.09195v1
• Date: Mon, 08 Jun 2026 08:30:51 GMT
• Title: Symbolic and Abstractive Reasoning with Complex Visual Queries
• Abstract summary: We explore a novel abstract data type termed complex visual query (CVQ)<n>CVQ probes symbolic and abstractive reasoning, which is a critical yet underexplored dimension of human-like neuro-symbolic reasoning for MLLMs.<n>We introduce a two-stage training framework that progressively equips MLLMs with robust visual reasoning capabilities.
• Score: 57.59111649292774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data type termed complex visual query (CVQ), designed to probe symbolic and abstractive reasoning, which is a critical yet underexplored dimension of human-like neuro-symbolic reasoning for MLLMs. We present a comprehensive investigation from three perspectives: \textbf{Data $\times$ Paradigm $\times$ Exploration}. Specifically, we propose a scalable pipeline for synthesizing CVQs grounded in large-scale multi-modal knowledge graphs, generating a diverse dataset encompassing 14 distinct query types via systematic combinations of first-order logic operators. We further introduce a two-stage training framework that progressively equips MLLMs with robust visual reasoning capabilities. We conduct extensive experiments to rigorously evaluate MLLMs across multiple dimensions, including reasoning performance on CVQs, as well as cross-task and cross-scenario generalization. We believe our work opens new perspectives and avenues for advancing the reasoning frontiers of MLLMs.

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