Test-Time Computing for Referring Multimodal Large Language Models

• URL: http://arxiv.org/abs/2602.19505v1
• Date: Mon, 23 Feb 2026 04:42:10 GMT
• Title: Test-Time Computing for Referring Multimodal Large Language Models
• Authors: Mingrui Wu, Hao Chen, Jiayi Ji, Xiaoshuai Sun, Zhiyuan Liu, Liujuan Cao, Ming-Ming Cheng, Rongrong Ji,
• Abstract summary: We propose ControlMLLM++, a novel test-time adaptation framework.<n>It injects learnable visual prompts into frozen multimodal large language models.
• Score: 143.49848714354698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose ControlMLLM++, a novel test-time adaptation framework that injects learnable visual prompts into frozen multimodal large language models (MLLMs) to enable fine-grained region-based visual reasoning without any model retraining or fine-tuning. Leveraging the insight that cross-modal attention maps intrinsically encode semantic correspondences between textual tokens and visual regions, ControlMLLM++ optimizes a latent visual token modifier during inference via a task-specific energy function to steer model attention towards user-specified areas. To enhance optimization stability and mitigate language prompt biases, ControlMLLM++ incorporates an improved optimization strategy (Optim++) and a prompt debiasing mechanism (PromptDebias). Supporting diverse visual prompt types including bounding boxes, masks, scribbles, and points, our method demonstrates strong out-of-domain generalization and interpretability. The code is available at https://github.com/mrwu-mac/ControlMLLM.

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