Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance

• URL: http://arxiv.org/abs/2509.05669v1
• Date: Sat, 06 Sep 2025 10:14:49 GMT
• Title: Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance
• Authors: Weijie Shen, Xinrui Wang, Yuanqi Nie, Apiradee Boonmee,
• Abstract summary: Context-Aware Multi-Turn Visual Reasoning (CAMVR) is designed to empower LVLMs with robust and coherent multi-turn visual-textual inference capabilities.<n>Our multi-level reasoning integration strategy ensures that response generation is deeply coherent with both current inputs and accumulated historical context.
• Score: 2.166625683790549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current Large Language Models (LLMs) and Vision-Language Large Models (LVLMs) excel in single-turn tasks but face significant challenges in multi-turn interactions requiring deep contextual understanding and complex visual reasoning, often leading to fragmented reasoning, context loss, and hallucinations. To address these limitations, we propose Context-Aware Multi-Turn Visual Reasoning (CAMVR), a novel framework designed to empower LVLMs with robust and coherent multi-turn visual-textual inference capabilities. CAMVR introduces two key innovations: a Visual-Textual Context Memory Unit (VCMU), a dynamic read-write memory network that stores and manages critical visual features, textual semantic representations, and their cross-modal correspondences from each interaction turn; and an Adaptive Visual Focus Guidance (AVFG) mechanism, which leverages the VCMU's context to dynamically adjust the visual encoder's attention to contextually relevant image regions. Our multi-level reasoning integration strategy ensures that response generation is deeply coherent with both current inputs and accumulated historical context. Extensive experiments on challenging datasets, including VisDial, an adapted A-OKVQA, and our novel Multi-Turn Instruction Following (MTIF) dataset, demonstrate that CAMVR consistently achieves state-of-the-art performance.

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