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arXiv · 2609.34942

Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models

Zhiqiang Xia·Yang Li·Xinyuan Zhang·Yuchen Liu·Haoyu Lu·Jiaming Xu·Runyu Shi·Ying Huang
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Abstract

The inference efficiency of Multimodal Large Language Models (MLLMs) is severely constrained by massive visual token sequences induced by high-resolution inputs, with computational cost scaling quadratically. Existing approaches primarily focus on downstream token compression, while overlooking a fundamental upstream inefficiency: input resolution is treated as a static, task-agnostic hyperparameter. We propose Task-Conditioned Resolution Routing (TCRR), which formulates visual compression as a task-conditioned decision and employs a lightweight cross-modal router that conditions backbone visual representations on textual semantics via feature-wise modulation and cross-attention to predict the minimal sufficient compression level per query. To support this, we curate a dataset of 500k samples across 12 task categories, labeled via a teacher-oracle pipeline to approximate Pareto-optimal compression scales. Extensive experiments across diverse architectures show that TCRR achieves a superior efficiency frontier, specifically reducing visual FLOPs by 40.9% and latency by 53.7% on Qwen3-VL-8B while preserving competitive performance. Further analysis of scaling behavior confirms that dynamically routing visual compression enables optimal resource allocation without modifying the MLLM backbone.

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Zhiqiang Xia, Yang Li, Xinyuan Zhang, Yuchen Liu, Haoyu Lu, Jiaming Xu, Runyu Shi, Ying Huang. 2026-09-28. Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models. https://arxiv.org/abs/2609.34942

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