Recent advances demonstrate that multimodal large language models (MLLMs) exhibit strong multimodal in-context learning (ICL) capabilities, enabling them to adapt to novel vision-language tasks from a few contextual examples. However, existing ICL approaches face challenges in reconciling scalability with robustness across diverse tasks and noisy contextual examples: manually selecting examples produces clean contexts but is labor-intensive and task-specific, while similarity-based retrieval improves scalability but could introduce irrelevant or structurally inconsistent samples that degrade ICL performance. To address these limitations, we propose ContextNav, the first agentic framework that integrates the scalability of automated retrieval with the quality and adaptiveness of human-like curation, enabling noise-robust and dynamically optimized contextualization for multimodal ICL. ContextNav unifies context management and noise-robust contextualization within a closed-loop workflow driven by graph-based orchestration. Specifically, it builds a resource-aware multimodal embedding pipeline, maintains a retrievable vector database, and applies agentic retrieval and structural alignment to construct noise-resilient contexts. An Operational Grammar Graph (OGG) further supports adaptive workflow planning and optimization, enabling the agent to refine its operational strategies based on downstream ICL feedback. Experimental results demonstrate that ContextNav achieves state-of-the-art performance across various datasets, underscoring the promise of agentic workflows for advancing scalable and robust contextualization in multimodal ICL.
Motivation for introducing agentic contextualization in multimodal ICL. Similarity-based retrieval can introduce semantic or structural noise into contextual candidates, which degrades ICL effectiveness. Employing an agent for human-like curation could effectively alleviate this challenge.
Framework of the ContextNav. The proposed agentic framework integrates three synergistic modules: (a) Agentic Context Management, which performs resource-aware multimodal embedding, maintains a continuously updated vector database, and subsequently leverages it for similarity-based retrieval to generate the initial candidate pool given an input query; (b) Noise-Robust Contextualization, which refines retrieved candidates through agentic retrieval and structural alignment to mitigate both semantic and structural noise; and (c) Graph-driven Workflow Orchestration, where the agent leverages an Operational Grammar Graph and memory module to adaptively plan and optimize operation sequence, thereby controlling the workflow. Collectively, these modules enable systematic management, representation, retrieval, and organization of multimodal contexts, supporting noise-robust and dynamically optimized contextualization for multimodal ICL.
@article{fu2025contextnav,
title={ContextNav: Towards Agentic Multimodal In-Context Learning},
author={Fu, Honghao and Ouyang, Yuan and Chang, Kai-Wei and Wang, Yiwei and Huang, Zi and Cai, Yujun},
journal={arXiv preprint arXiv:2510.04560},
year={2025}
}