Viper-F1: Fast and Fine-Grained Multimodal Understanding with Cross-Modal State-Space Modulation

• URL: http://arxiv.org/abs/2511.11177v3
• Date: Tue, 18 Nov 2025 07:12:36 GMT
• Title: Viper-F1: Fast and Fine-Grained Multimodal Understanding with Cross-Modal State-Space Modulation
• Authors: Quoc-Huy Trinh, Mustapha Abdullahi, Do Duy Hung Trinh, Bo Zhao, Debesh Jha,
• Abstract summary: We introduce Viper-F1, a Hybrid State-Space Vision-Language Model that replaces attention with efficient Liquid State-Space Dynamics.<n>We show that Viper-F1 achieves accurate, fine-grained understanding with significantly improved efficiency.
• Score: 7.171333807979583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment in resource-constrained scenarios such as robotic manipulation, personal assistants, and smart cameras. Most existing methods rely on Transformer-based cross-attention, whose quadratic complexity hinders efficiency. Moreover, small vision-language models often struggle to precisely capture fine-grained, task-relevant visual regions, leading to degraded performance on fine-grained reasoning tasks that limit their effectiveness in the real world. To address these issues, we introduce Viper-F1, a Hybrid State-Space Vision-Language Model that replaces attention with efficient Liquid State-Space Dynamics. To further enhance visual grounding, we propose a Token-Grid Correlation Module, which computes lightweight correlations between text tokens and image patches and modulates the state-space dynamics via FiLM conditioning. This enables the model to selectively emphasize visual regions relevant to the textual prompt while maintaining linear-time inference. Experimental results across multiple benchmarks demonstrate that Viper-F1 achieves accurate, fine-grained understanding with significantly improved efficiency.

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