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arXiv · 2603.12760

HIFICL: High-Fidelity In-Context Learning for Multimodal Tasks

Xiaoyu Li·Yuhang Liu·Xuanshuo Kang·Zheng Luo·Fangqi Lou·Xiaohua Wu·Zihan Xiong
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Abstract

In-Context Learning (ICL) is a significant paradigm for Large Multimodal Models (LMMs), using a few in-context demonstrations (ICDs) for new task adaptation. However, its performance is sensitive to demonstration configurations and computationally expensive. Mathematically, the influence of these demonstrations can be decomposed into a dynamic mixture of the standard attention output and the context values. Current approximation methods simplify this process by learning a "shift vector". Inspired by the exact decomposition, we introduce High-Fidelity In-Context Learning (HIFICL) to more faithfully model the ICL mechanism. HIFICL consists of three key components: 1) a set of "virtual key-value pairs" to act as a learnable context, 2) a low-rank factorization for stable and regularized training, and 3) a simple end-to-end training objective. From another perspective, this mechanism constitutes a form of context-aware Parameter-Efficient Fine-Tuning (PEFT). Extensive experiments show that HiFICL consistently outperforms existing approximation methods on several multimodal benchmarks. The code is available at https://github.com/bbbandari/HiFICL.

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Xiaoyu Li, Yuhang Liu, Xuanshuo Kang, Zheng Luo, Fangqi Lou, Xiaohua Wu, Zihan Xiong. 2026-03-13. HIFICL: High-Fidelity In-Context Learning for Multimodal Tasks. https://arxiv.org/abs/2603.12760

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