VICoT-Agent: A Vision-Interleaved Chain-of-Thought Framework for Interpretable Multimodal Reasoning and Scalable Remote Sensing Analysis

TL;DR

VICoT-Agent introduces an interleaved vision-language reasoning framework for remote sensing analysis, enhancing interpretability, flexibility, and efficiency through multi-round reasoning and tool integration.

Contribution

The paper presents VICoT, a novel multimodal reasoning framework with a reasoning stack and tool suite, plus a distillation method for lightweight models, advancing remote sensing analysis capabilities.

Findings

Outperforms state-of-the-art in reasoning transparency

Achieves higher execution efficiency

Generates higher quality reasoning outputs

Abstract

The current remote sensing image analysis task is increasingly evolving from traditional object recognition to complex intelligence reasoning, which places higher requirements on the model's reasoning ability and the flexibility of tool invocation. To this end, we propose a new multimodal agent framework, Vision-Interleaved Chain-of-Thought Framework (VICoT), which implements explicit multi-round reasoning by dynamically incorporating visual tools into the chain of thought. Through a stack-based reasoning structure and a modular MCP-compatible tool suite, VICoT enables LLMs to efficiently perform multi-round, interleaved vision-language reasoning tasks with strong generalization and flexibility.We also propose the Reasoning Stack distillation method to migrate complex Agent behaviors to small, lightweight models, which ensures the reasoning capability while significantly reducing complexity. Experiments on multiple remote sensing benchmarks demonstrate that VICoT significantly outperforms existing SOTA frameworks in reasoning transparency, execution efficiency, and generation quality.