Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning
arXiv:2606.04434v1 Announce Type: cross Abstract: Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks. Despite its flexibility, multimodal ICL incurs high inference latency and suffers from instability due to sensitivity to demonstration formatting, ordering, and content. To address these limitations, we propose Hyper-ICL, a lightweight, training-based framework for demonstration-free multimodal ICL that recons
The rapid development and widespread adoption of Multimodal Large Language Models (MLLMs) necessitate improved efficiency and stability in their practical application, particularly for in-context learning.
This development addresses key bottlenecks in MLLM deployment, enabling more efficient and reliable inference which is critical for scaling AI applications across various industries.
The proposal of 'demonstration-free multimodal ICL' significantly reduces the computational burden and operational complexities associated with MLLMs, potentially accelerating their integration into real-world systems.
- · AI developers
- · Cloud providers
- · Industries adopting MLLMs
- · Inefficient MLLM systems
- · High-latency AI applications
Reduced inference costs and increased deployment speed for multimodal AI applications.
Broader accessibility and application of sophisticated AI models across diverse tasks due to enhanced stability and efficiency.
Acceleration of autonomous AI agents benefiting from more robust and less resource-intensive multimodal understanding capabilities.
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Read at arXiv cs.LG