@inproceedings{zinkovich-etal-2026-sparta,
    title = "{SPARTA}: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space",
    author = "Zinkovich, Viktoriia  and
      Antonov, Anton  and
      Spiridonov, Andrei  and
      Shepelev, Denis  and
      Moskalenko, Andrey  and
      Pugacheva, Daria  and
      Tutubalina, Elena  and
      Kuznetsov, Andrey  and
      Shakhuro, Vlad",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.126/",
    pages = "2754--2775",
    ISBN = "979-8-89176-380-7",
    abstract = "Multimodal large language models (MLLMs) have shown impressive capabilities in vision-language tasks such as reasoning segmentation, where models generate segmentation masks based on textual queries. While prior work has primarily focused on perturbing image inputs, semantically equivalent textual paraphrases{---}crucial in real-world applications where users express the same intent in varied ways{---}remain underexplored. To address this gap, we introduce a novel adversarial paraphrasing task: generating grammatically correct paraphrases that preserve the original query meaning while degrading segmentation performance. To evaluate the quality of adversarial paraphrases, we develop a comprehensive automatic evaluation protocol validated with human studies. Furthermore, we introduce SPARTA{---}a black-box, sentence-level optimization method that operates in the low-dimensional semantic latent space of a text autoencoder, guided by reinforcement learning. SPARTA achieves significantly higher success rates, outperforming prior methods by up to 2x on both the ReasonSeg and LLMSeg-40k datasets. We use SPARTA and competitive baselines to assess the robustness of advanced reasoning segmentation models. We reveal that they remain vulnerable to adversarial paraphrasing{---}even under strict semantic and grammatical constraints. All code and data will be released publicly upon acceptance."
}