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arXiv · 2604.14029

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management

Yikun Liu·Yuan Liu·Haicheng Wang·Zhongyin Zhao·Le Tian·Xiao Zhou·Jiangchao Yao·Yanfeng Wang·Weidi Xie
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Abstract

Large Multimodal Models (LMMs) excel at visual perception but struggle with real-time, knowledge-intensive queries due to their reliance on static parametric knowledge. While multimodal search agents offer a promising solution, developing them from vanilla LMMs presents two major challenges: the lack of open recipes to cultivate search agency from scratch, and the severe context explosion (attention dilution) that occurs during long-horizon multi-turn investigations. In this paper, we address both challenges by developing a native multimodal search agent. First, we introduce an open training recipe featuring Agentic Seeding, a formative training stage that bootstraps tool-use and planning abilities directly from a non-agentic foundation. Second, to overcome the context bottleneck, we propose V-Fold, an adaptive memory management mechanism. V-Fold retains recent interactions as high-fidelity text while folding stale historical context into the visual space via rendering, exploiting the model's cross-modal alignment to preserve raw evidence without text token redundancy. Combining these innovations, we present POINTS-Seeker-8B, which achieves state-of-the-art performance among models of comparable scale across six multimodal search benchmarks.

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Yikun Liu, Yuan Liu, Haicheng Wang, Zhongyin Zhao, Le Tian, Xiao Zhou, Jiangchao Yao, Yanfeng Wang, Weidi Xie. 2026-04-15. POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management. https://arxiv.org/abs/2604.14029

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