@inproceedings{wang-etal-2026-simple,
    title = "Simple-{VGC}: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition",
    author = "Wang, Ye  and
      Chen, Qianglong  and
      Wang, Siyuan  and
      Li, Zejun  and
      Guo, Shijie  and
      Zhang, Zhirui  and
      Wei, Zhongyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.223/",
    pages = "4903--4929",
    ISBN = "979-8-89176-390-6",
    abstract = "Multimodal Large Language Models (MLLMs) have achieved strong performance on vision-language tasks, yet often fail to preserve and effectively leverage visual evidence throughout generation. We identify three fundamental types of visual grounding failures: Long-Context Grounding Error, where visual information gradually decays over long sequences; Fine-Grained Grounding Error, where low-resolution or degraded inputs hinder the recovery of detailed visual information; and Regional Grounding Error, where spatially diffuse attention weakens region-level vision-language alignment. To address these issues, we propose a tool-augmented reasoning framework with three targeted compensation strategies: reuse, which re-injects the original image to mitigate visual forgetting; focus{\_}area, which constrains attention to task-relevant regions; and zoom{\_}in, which enhances visual resolution for fine-grained perception. We further construct the TWI-Tools-146K dataset and develop Simple-VGC, a tool-augmented MLLM that interleaves visual and textual tokens. Extensive experiments show that each tool yields targeted improvements for its corresponding grounding error, while their combination produces synergistic gains in visual reasoning. Beyond performance, our analysis provides mechanistic insights into how tool-based interventions improve visual grounding, pointing toward more reliable multimodal reasoning."
}