@inproceedings{liu-etal-2026-closing,
    title = "Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning",
    author = "Liu, Chang  and
      Wagley, Benjamin  and
      Wang, Zibo  and
      Belviranli, Mehmet E.  and
      Wu, Bo",
    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.630/",
    pages = "13834--13849",
    ISBN = "979-8-89176-390-6",
    abstract = "While multi-modal large language models such as GPT-5 demonstrate exceptional general understanding, they suffer from a fundamental Spatial Execution Gap, failing to translate visual semantics into precise, schema-valid coordinate operations in interactive environments. In this work, we show that model scale alone cannot close this gap; instead, verifiable structured reasoning provides the key to spatial precision. We present a comprehensive pipeline that leverages Group Relative Policy Optimization to enforce a strict Identify-Reason-Verify protocol, effectively shifting the computational burden from parameters to test-time reasoning. By utilizing a multi-agent system to distill optimal reasoning schemas and training on execution-verifiable rewards, our specialized 3B agent achieves 100{\%} format coherence and 81.12{\%} operation accuracy on digital whiteboard tasks. Crucially, our approach outperforms a state-of-the-art frontier model, GPT-5, by 16.75{\%} in operation accuracy. The results suggest that for complex user interface manipulation, small, RL-aligned models with dedicated reasoning protocols are superior to generalist frontier models, offering a promising direction for building reliable web agents."
}