ToolScope: An Agentic Framework for Vision-Guided and Long-Horizon Tool
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Authors: name: Mengjie Deng, name: Guanting Dong, name: Zhicheng Dou

Recently, large language models (LLMs) have demonstrated remarkable
problem-solving capabilities by autonomously integrating with external tools
for collaborative reasoning. However, due to the inherently complex and diverse
nature of multimodal information, enabling multimodal large language models
(MLLMs) to flexibly and efficiently utilize external tools during reasoning
remains an underexplored challenge. In this work, we introduce ToolScope, an
agentic framework designed to unify global planning with local multimodal
perception, adopting a specialized Perceive tool to mitigates visual context
degradation in long-horizon VQA task. ToolScope comprises three primary
components: the Global Navigator, the Agentic Executor, and the Response
Synthesizer. The Global Navigator functions as a "telescope", offering
high-level strategic guidance. The Agentic Executor operates iteratively to
augment MLLM with local perception through the integration of external
tools-Search, Code, and Perceive. Finally, the Response Synthesizer
consolidates and organizes the reasoning process into a coherent, user-friendly
output. We evaluate ToolScope on four VQA benchmarks across diverse domains,
including VQA 2.0, ScienceQA, MAT-Search and MathVista. It demonstrates strong
generalization capabilities, achieving an average performance improvement of up
to +6.69% across all datasets.